JP7090445B2 - Control systems, learning devices, control devices, and control methods - Google Patents

Description

The present invention relates to a control system, a learning device, a control device, and a control method.

Conventionally, in a wind power generation system, there is a technique for controlling the output with high efficiency by changing the mounting angle (pitch angle) of the blades (blades) of the wind turbine. However, many vertical axis type wind turbines do not have wing pitch control. In a wind power generation system that does not have pitch control, the rotation speed of the wind turbine is reduced when the wind turbine receives a certain wind speed (strong wind). This prevents the situation where the rotation speed of the wind turbine becomes excessively rotated and is forcibly stopped under strong wind, and the rotating operation of the wind turbine is continued even under strong wind. By doing so, the operating conditions have been expanded (for example, Patent Document 1).

Japanese Patent No. 4401117

However, since the time-series changes in wind speed (hereinafter referred to as wind conditions) change from moment to moment, the reality is that they are unstable and difficult to predict. For this reason, if the expected strong wind does not blow while the rotation speed of the wind turbine is decelerated in preparation for a strong wind, the generated power will be reduced according to the amount of deceleration of the rotation speed of the wind turbine. Therefore, it is more difficult to control than pitch control.
Furthermore, since the wind conditions change depending on the terrain of the place where the wind turbine is installed and the season, it is possible to determine how much the rotation of the wind turbine should be slowed down and controlled to prevent over-rotation during strong winds. Have difficulty.
For this reason, in strong winds, the wind turbine is forcibly stopped due to over-rotation under certain conditions, and under other conditions, the upper limit of stopping operation (power generation) despite suppressing over-rotation and maintaining power generation. Since the wind speed reached the wind speed (limit wind speed), the rotation of the wind turbine was sometimes forcibly stopped.

The present invention is to provide a control system, a learning device, a control device, and a control method capable of changing the critical wind speed according to the wind condition.

In order to solve the above-mentioned problems, one embodiment of the present invention is a control system that controls the rotation speed of the wind turbine of the wind power generation system, and wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, said. The wind speed and the rotation are based on the rotation information regarding the rotation of the wind turbine and the relational information indicating the wind speed and the maximum value of the rotation speed at which the wind turbine can rotate, which is the relation information between the wind speed and the rotation. A learning unit for learning the correspondence information of the wind turbine, a storage unit for storing the correspondence information between the wind speed and the rotation, the rotation information when the rotation control parameter for controlling the rotation speed of the wind turbine is set in the wind turbine, and A state detection unit that detects wind speed information, a determination unit that determines the rotation information based on the rotation information and the wind speed information detected by the state detection unit, and the corresponding information, and a determination unit that determines the rotation information. A control unit that controls the rotation of the wind turbine based on the rotation information is provided , and the rotation detected by the state detection unit in a strong wind section where the wind speed is equal to or higher than a predetermined strong wind determination threshold in a time-series change of the wind speed. When the difference between the number and the maximum value of the rotation number is less than the predetermined margin threshold value, and the change rate of the rotation number indicated in the rotation information detected by the state detection unit is a predetermined change. When it is less than the threshold value, it is a control system that increases the wind speed limit, which is the upper limit of the wind speed that can be generated by the wind power generation system .

Further, one embodiment of the present invention is the above-mentioned control system, and is a reward for calculating a reward according to a predetermined reward condition based on the rotation information detected by the state detection unit and the wind velocity information. Further including a calculation unit, the relational information includes a reward calculated by the reward calculation unit, and the learning unit is an enhanced learning model for learning the corresponding information based on the reward.

Further, one embodiment of the present invention is the control system described above, and calculates a reward based on the maximum value of the rotation speed, the rotation speed, and the rate of change of the rotation speed.

Further, one embodiment of the present invention is a control system that controls the rotation speed of the wind turbine of the wind power generation system, and wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, rotation information regarding the rotation of the wind turbine. , And, based on the relational information between the wind speed and the rotation and showing the relational information indicating the wind speed and the maximum value of the rotation speed at which the wind turbine can rotate, the correspondence information between the wind speed and the rotation is learned. A state of detecting the rotation information and the wind speed information when the learning unit, the storage unit for storing the correspondence information between the wind speed and the rotation, and the rotation control parameter for controlling the rotation speed of the wind turbine are set in the wind turbine. Based on the detection unit, the determination unit that determines the rotation information based on the rotation information and the wind speed information detected by the state detection unit, and the corresponding information, and the rotation information determined by the determination unit. Further, a reward calculation unit that includes a control unit that controls the rotation of the wind turbine , and calculates a reward according to a predetermined reward condition based on the rotation information detected by the state detection unit and the wind speed information. The related information includes the reward calculated by the reward calculation unit, the learning unit learns the corresponding information based on the reward by enhanced learning, and the reward calculation unit is the maximum value of the rotation speed. , The reward is calculated based on the rotation speed and the rate of change of the rotation speed, and the reward calculation unit has a predetermined difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed. If it is less than the margin threshold and the rate of change of the number of rotations indicated in the rotation information detected by the state detection unit is equal to or more than the predetermined change threshold, the first level reward is calculated. It is a control system that calculates a second level reward higher than the first level when the rate of change is less than the change threshold .

Further, one embodiment of the present invention is the above-mentioned control system, and in the reward calculation unit, the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed is a predetermined margin threshold value. In the above case, the reward of the third level higher than the first level and lower than the second level is calculated.
Further, one embodiment of the present invention is the above-mentioned control system, in which the rotation speed detected by the state detection unit in a strong wind section where the wind speed is equal to or higher than a predetermined strong wind determination threshold value in a time-series change of the wind speed. When the difference from the maximum value of the rotation speed is less than the predetermined margin threshold value, and the change rate of the rotation speed shown in the rotation information detected by the state detection unit is less than the predetermined change threshold value. If this is the case, the wind speed limit, which is the upper limit of the wind speed that can be generated by the wind power generation system, is increased.

Further, in one embodiment of the present invention, wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, rotation information regarding the rotation of the wind turbine, power information regarding the regenerative power generated by the wind power generation system, and the above-mentioned The wind speed is based on the relationship information between the wind speed and the rotation, which is the limit of the wind speed that can be generated by the wind power generation system, and the maximum value of the rotation speed at which the wind turbine can rotate. It is a learning device that has a learning unit that learns the correspondence information between the wind turbine and the rotation, and can acquire the reward required by the control device. A reward according to a predetermined reward condition is calculated based on the rotation information and the state detection unit that detects the wind speed information in the case of setting, the rotation information detected by the state detection unit, and the wind speed information. A reward calculation unit is provided, the reward calculation unit calculates a reward based on the maximum value of the rotation speed, the rotation speed, and the rate of change of the rotation speed, and the reward calculation unit is performed by the state detection unit. When the difference between the detected rotation speed and the maximum value of the rotation speed is less than a predetermined margin threshold value, and the rate of change of the rotation speed indicated in the rotation information detected by the state detection unit. Is equal to or greater than a predetermined change threshold, a first-level reward is calculated, and if the change rate is less than the change threshold, a second-level reward higher than the first level is calculated. , The learning unit includes the reward calculated by the reward calculation unit, and the learning unit is a learning device that learns the corresponding information based on the reward by enhanced learning .

Further, in one embodiment of the present invention, when the rotation control parameter for controlling the rotation speed of the wind turbine of the wind power generation system is set in the wind power generation system, the rotation information regarding the rotation of the wind turbine and the power generation by the wind power generation system are generated. A state detection unit that detects power information related to the regenerative power and wind speed information related to the wind speed in the wind turbine, the power information detected by the state detection unit, the wind speed information, and the correspondence between the wind speed and the rotation of the wind turbine. A determination unit that determines the rotation information based on the information and a control unit that controls the rotation speed based on the rotation information determined by the determination unit are provided , and the wind speed is predetermined in a time-series change of the wind speed. The difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed in the strong wind section equal to or higher than the strong wind determination threshold value is less than the predetermined margin threshold value, and the state detection This is a control device that increases the wind speed limit, which is the upper limit of the wind speed that can be generated by the wind power generation system, when the rate of change of the rotation speed indicated in the rotation information detected by the unit is less than a predetermined change threshold .

Further, one embodiment of the present invention is a control method for controlling the rotation speed of the wind turbine of the wind power generation system, wherein the learning unit provides wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, the wind turbine. Correspondence information between the wind speed and the rotation based on the rotation information regarding the rotation and the relational information indicating the wind speed and the maximum value of the rotation speed at which the wind turbine can rotate, which is the relational information between the wind speed and the rotation. The storage unit stores the correspondence information between the wind speed and the rotation, and the state detection unit sets the rotation control parameter for controlling the rotation speed to the wind turbine, and the rotation information and the wind speed. The information is detected, the determination unit determines the rotation information based on the rotation information, the wind speed information, and the corresponding information detected by the state detection unit, and the control unit is determined by the determination unit. The rotation speed and the rotation speed detected by the state detection unit in a strong wind section where the wind speed is equal to or higher than a predetermined strong wind determination threshold value in a time-series change of the wind speed by controlling the rotation of the wind turbine based on the rotation information. When the difference from the maximum value of is less than the predetermined margin threshold value, and the rate of change of the rotation speed shown in the rotation information detected by the state detection unit is less than the predetermined change threshold value. This is a control method for increasing the wind speed limit, which is the upper limit of the wind speed that can be generated by the wind power generation system .
Further, one embodiment of the present invention is a control method for controlling the rotation speed of the wind turbine of the wind power generation system, wherein the learning unit provides wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, the wind turbine. Correspondence information between the wind speed and the rotation based on the rotation information regarding the rotation and the relational information indicating the wind speed and the maximum value of the rotation speed at which the wind turbine can rotate, which is the relational information between the wind speed and the rotation. The storage unit stores the correspondence information between the wind speed and the rotation, and the state detection unit sets the rotation control parameter for controlling the rotation speed to the wind turbine, and the rotation information and the wind speed. The information is detected, the reward calculation unit calculates the reward according to the predetermined reward condition based on the rotation information detected by the state detection unit and the wind speed information, and the reward calculation unit performs the rotation. The reward is calculated based on the maximum value of the number, the number of rotations, and the rate of change of the number of rotations, and the reward calculation unit determines the number of rotations detected by the state detection unit and the maximum value of the number of rotations. When the difference is less than the predetermined margin threshold and the rate of change of the number of rotations indicated in the rotation information detected by the state detection unit is equal to or more than the predetermined change threshold, the first level reward. When the rate of change is less than the threshold of change, the reward of the second level higher than the first level is calculated, and the related information includes the reward calculated by the reward calculation unit, and the learning. The unit learns the correspondence information by reinforcement learning based on the reward, and the determination unit determines the rotation information based on the rotation information, the wind speed information, and the correspondence information detected by the state detection unit. However, this is a control method in which the control unit controls the rotation of the wind turbine based on the rotation information determined by the determination unit.

As described above, according to the present invention, it is possible to change the critical wind speed according to the wind conditions.

It is a block diagram which shows an example of the schematic structure of the wind power generation system 1 which concerns on 1st Embodiment. It is a block diagram which shows an example of the structure of the control device 60 and the learning device 70 of the wind power generation system 1 which concerns on 1st Embodiment. It is a figure which shows the example of the reward condition at the time of a strong wind which concerns on 1st Embodiment. It is a figure which shows an example of the relationship between the wind speed and the rotation speed of a wind turbine in the wind power generation system 1 which concerns on 1st Embodiment. It is a figure which shows an example of the relationship between the wind speed and the generated electric power in the wind power generation system 1 which concerns on 1st Embodiment. It is a flowchart which shows the operation example of the control device 60 which concerns on 1st Embodiment. It is a figure explaining the target section which concerns on 2nd Embodiment. It is a flowchart which shows the operation example of the control device 60 which concerns on 2nd Embodiment. It is a flowchart which shows the operation example of the control device 60 which concerns on 3rd Embodiment. It is a flowchart which shows the operation example of the control device 60 which concerns on 4th Embodiment. It is a block diagram which shows an example of the structure of the control device 60A of the wind power generation system 1A which concerns on 5th Embodiment. It is a block diagram which shows an example of the structure of the control device 60B of the wind power generation system 1B which concerns on 6th Embodiment.

Hereinafter, the control system, the learning device, and the control device of the embodiment will be described with reference to the drawings.

<First Embodiment>
FIG. 1 is a block diagram showing an example of a schematic configuration of the wind power generation system 1 according to the first embodiment. The wind power generation system 1 includes a wind power generator main body 10 and a control system 50. Various information is exchanged between the wind power generator main body 10 and the control system 50.
As shown in FIG. 1, for example, a control parameter for controlling the wind power generator main body 10 is output from the control system 50 to the wind power generator main body 10.
Further, for example, a state parameter indicating the state of the wind power generator main body 10 is output from the wind power generator main body 10 to the control system 50.

The control parameters are, for example, a rotation speed control parameter for controlling the rotation speed of the wind turbine 20 and a power control parameter for controlling the electric energy of the regenerative power generated by the generator 30.
Further, the state parameters are information indicating, for example, the wind speed of the wind turbine 20, the rotation speed of the wind turbine 20 (hereinafter, also simply referred to as the rotation speed), and the electric energy of the regenerated electric power generated by the generator 30.

The wind power generator main body 10 includes a wind turbine 20, a generator 30, a rectifying / boosting unit 31, a voltage detection unit 32, a current detection unit 33, a wind speed sensor 41, and a rotation speed sensor 42.
The wind turbine 20 is configured as, for example, a vertical axis type wind turbine, and is configured by a straight wing vertical axis wind turbine or the like in which a plurality of straight blades are integrally rotatably connected around a rotating shaft extending in the vertical direction.
For example, the wind turbine 20 is connected to the rotor of the generator 30 described later via a rotation shaft, and rotates integrally with the rotor of the generator 30. Here, the rotor of the generator 30 rotates at a rotation speed corresponding to the electric energy of the regenerated electric power generated by the generator 30. Further, the electric energy of the regenerative power is controlled by MPPT (Maximum Power Point Tracking) by the control system 50 described later. Therefore, the rotation speed of the wind turbine 20 is indirectly controlled by the MPPT control by the control system 50.

The generator 30 is a device that converts the rotational force of the wind turbine 20 to generate electric power. For example, the generator 30 is configured as a three-phase alternating current generator, and a rotor that rotates in conjunction with the rotation of the wind turbine 20 rotates the wind turbine 20. AC power is generated by being connected to a shaft and rotating. The generator 30 supplies the generated AC power to the rectifying / boosting unit 31. The generator 30 operates as a generator that supplies the generated power to the rectifying / boosting unit 31 side, and also operates as an electric motor to which AC power is supplied from the rectifying / boosting unit 31. The generator 30 operates as an electric motor, for example, when performing assist control for assisting rotation when the wind turbine 20 is started.

The rectifying / boosting unit 31 converts the AC power generated by the generator 30 into DC power, and converts (boosts) the voltage of the converted DC power. The rectifying / boosting unit 31 is, for example, a boosting chopper circuit. The DC power output from the rectifying / boosting unit 31 corresponds to the regenerative power generated by the generator 30.

The rectifying / boosting unit 31 operates as a boosting chopper circuit when the generator 30 is operated to generate power, and operates as an inverter when the generator 30 is operated as an electric motor during assist control or the like. It is a circuit. The electric power supplied during the assist control may be the electric power from the battery (not shown) of the wind power generation system 1.

The voltage detection unit 32 is composed of a known voltmeter, detects the output voltage output from the rectifying / boosting unit 31, and outputs the detected output voltage to the control system 50.
The current detection unit 33 is composed of a known ammeter, detects the output current output from the rectifying / boosting unit 31, and outputs the detected output current to the control system 50.

The wind speed sensor 41 is composed of a known wind speed sensor, and is provided at a predetermined position near the wind turbine 20 (for example, a portion other than the rotary blade in the wind turbine 20) to detect the wind speed of the wind received by the wind turbine. The wind speed sensor 41 outputs information indicating the detected wind speed to the control system 50.

The rotation speed sensor 42 detects the rotation speed of the wind turbine 20. The rotation speed sensor 42 may be any sensor that can detect the rotation speed of the rotation shaft portion (not shown) of the wind turbine 20, and various known rotation speed sensors can be used. The rotation speed sensor 42 outputs information indicating the detected rotation speed to the control system 50.

The control system 50 includes a control device 60 and a learning device 70.
The control device 60 controls the rotation speed of the wind turbine by determining the rotation speed control parameter based on the wind speed detected by the wind speed sensor 41 and the rotation speed detected by the rotation speed sensor 42. The control device 60 uses the learning device 70 to determine the rotation speed control parameters. The method in which the control device 60 determines the rotation speed control parameter using the learning device 70 will be described in detail later.

The learning device 70 is, for example, a device that performs reinforcement learning. In this case, the learning device 70 corresponds to an agent that is a learning subject in reinforcement learning, and is for more appropriately controlling the controlled object by interacting with the controlled object (in the present embodiment, the wind power generator main body 10). Advance learning.
Hereinafter, the case where the learning device 70 performs reinforcement learning will be described as an example, but the present invention is not limited to this. The learning device 70 may be one that learns so that the parameters for controlling the controlled object become more appropriate based on the state of the controlled object (wind power generator main body 10). The learning device 70 may perform supervised learning, unsupervised learning, or other learning. Here, the state related to the controlled object (wind power generator main body 10) is the state around the wind power generator main body 10 and the wind power generator main body 10, and for example, the wind speed in the wind turbine 20 and the wind turbine 20 indicated by the state parameter. It is a variable such as a rotation speed and a power generation amount of the generator 30. Further, the state here includes a state that changes from moment to moment such as the above-mentioned wind speed, and a predetermined state, for example, a limit value of the rotation speed of the wind turbine 20, an upper and lower limit of the rotation torque of the wind turbine 20. And the maximum amount of electric power that the generator 30 can generate.

In the present embodiment, the learning device 70 outputs a rotation speed control parameter for controlling the wind power generator main body 10, observes the state of the wind power generator main body 10 according to the output rotation speed control parameter, and changes the state. The next rotation speed control parameter is determined according to.
Further, the learning device 70 receives a reward according to the state of the wind power generator main body 10. As a result, the learning device 70 can proceed with learning by determining whether the rotation speed control parameter output by itself is good or bad by using the reward as a clue, and can output a more suitable rotation speed control parameter.

FIG. 2 is a block diagram showing an example of the configuration of the control device 60 of the wind power generation system 1 according to the embodiment of the present invention.
As shown in FIG. 2, the control device 60 includes a parameter acquisition unit 61, a state detection unit 62, a reward calculation unit 63, and a reward output unit 64. Further, the learning device 70 includes a reinforcement learning unit 71. Here, the reinforcement learning unit 71 is an example of the “learning unit”.

The parameter acquisition unit 61 acquires the rotation speed control parameter output from the reinforcement learning unit 71. The parameter acquisition unit 61 outputs the acquired rotation speed control parameter to the wind power generator main body 10.

The state detection unit 62 detects a state parameter indicating the state of the wind power generator main body 10. The state parameters are information about the wind turbine 20 and the generator 30 included in the wind power generator main body 10, and are, for example, the wind speed detected by the wind speed sensor 41, the rotation speed detected by the rotation speed sensor 42, and the generator 30. It is information indicating the regenerated electric power generated. The state detection unit 62 outputs the detected state parameter to the reward calculation unit 63.

Here, the wind speed detected by the wind speed sensor 41 is an example of "wind speed information". The rotation speed detected by the rotation speed sensor 42 is an example of "rotation information". Further, the regenerative electric power generated by the generator 30 is an example of "electric power information".

The reward calculation unit 63 calculates the reward based on the information indicating the wind speed and the rotation speed acquired from the state detection unit 62. The reward calculation unit 63 calculates the reward according to a predetermined reward condition.
Here, the reward condition is set so that a higher reward can be obtained, for example, when it is determined that the rotation speed is more appropriately controlled with respect to the wind speed.

For example, when the reward calculation unit 63 is controlled based on the wind speed and the rotation speed without causing the rotation of the wind turbine to become over-rotated in a strong wind, and the generated power is suppressed from being excessively reduced, the reward calculation unit 63 is more likely to be used. Calculate high rewards. Further, for example, the reward calculation unit 63 inappropriately suppresses the rotation of the wind turbine based on the wind speed and the rotation speed, regardless of the wind conditions suitable for power generation, causing a decrease in the generated power. If so, calculate a lower reward. The reward calculation unit 63 outputs the calculated reward to the reward output unit 64.

The reward output unit 64 outputs the reward acquired from the reward calculation unit 63 to the reinforcement learning unit 71.

The reinforcement learning unit 71 acquires information indicating the wind speed and the rotation speed indicating the state of the wind power generator main body 10. Further, the reinforcement learning unit 71 acquires a reward from the reward output unit 64. The reinforcement learning unit 71 uses the reward as a clue to raise the reward based on the acquired information indicating the wind speed and the rotation speed, and the information indicating the allowable range of the rotation speed and the target value of the rotation speed predetermined for the wind speed. The learning is advanced so that the above can be obtained, and a more appropriate rotation speed control parameter is output.

Here, the permissible range of the rotation speed is a range permissible as the rotation speed of the wind turbine 20 determined based on the mechanical service limit of the wind turbine 20 and the generator 30. Further, the target value of the rotation speed is the rotation speed of the wind turbine having the maximum regenerative power determined for each wind speed.

In wind power generation, it may be difficult to control the rotation speed of the wind turbine, especially in strong winds, and if the rotation speed of the wind turbine becomes excessive, the wind turbine will be forcibly stopped and the generated power will decrease. Invite. Further, if the rotation speed of the wind turbine is suppressed too much in preparation for a strong wind, it may cause a decrease in the generated power.
Therefore, in the present embodiment, the reward condition is set so that the reward is different depending on whether or not the wind turbine is appropriately controlled with respect to the rotation speed control parameter in the strong wind. This makes it possible for the reinforcement learning unit 71 to learn so that more appropriate rotation speed control parameters can be output even in a strong wind.

FIG. 3 is a diagram showing an example of a reward condition in a strong wind according to the first embodiment.
As shown in FIG. 3, when the rotation speed is equal to or higher than a predetermined first threshold value in a strong wind, the reward is the first level, which is the lowest level. In other words, if the rotation speed is exceeded in a strong wind, the lowest reward will be given. The lowest level first level reward is, for example, a negative reward.

Further, when the rotation speed is equal to or higher than a predetermined second threshold value and less than the first threshold value, the reward is the second level, which is the highest rank. That is, when the rotation speed is controlled within an appropriate range in a strong wind, the highest reward is given. The second threshold value is lower than the first threshold value.

Further, when the rotation speed is less than a predetermined second threshold value, the reward is a third level lower than the second level and higher than the first level. That is, when the rotation speed is insufficient in a strong wind, the reward is lower than the highest reward, but the reward is higher than when the rotation speed is excessive.

The strong wind here is a case where a wind speed equal to or higher than a predetermined strong wind determination threshold value is detected, and this strong wind determination threshold value may be arbitrarily determined according to the structure of the wind turbine and the mechanical strength. Further, the above-mentioned first threshold value and the second threshold value may be constant values regardless of the wind speed, or may be different values depending on the wind speed.

FIG. 4 is a diagram showing an example of the relationship between the wind speed and the rotation speed of the wind turbine in the wind power generation system 1 according to the first embodiment. The horizontal axis of FIG. 4 shows the wind speed [m / s], and the vertical axis shows the rotation speed [r / min] of the wind turbine 20. Further, in FIG. 4, when the wind speed is B [m / s] or more, the wind becomes strong.

As shown in FIG. 4, the wind speed and the rotation speed are controlled to be proportional to each other by a positive proportional coefficient between the wind speed A [m / s] and the wind speed B [m / s], which are not strong winds. To. On the other hand, when the wind speed is B [m / s] or higher, which is a strong wind, it is desirable that the wind speed and the rotation speed are controlled so as to have a predetermined relationship. The predetermined relationship here is, for example, a relationship in which the rotation speed decreases as the wind speed increases. When the relationship between the wind speed and the rotation speed is controlled to be a point on the characteristic FG in this example, the torque of the generator 30 can be maintained at the maximum torque. In this case, it is possible to obtain the maximum generated power. In this example, it is most desirable that the rotation speed is controlled along the line shown in the characteristic FG. Therefore, in the present embodiment, the highest reward is set when the rotation speed of the wind turbine 20 is controlled within an appropriate range according to the wind speed in a strong wind. The appropriate range in this case is, for example, a predetermined range including a point on the characteristic FG according to the wind speed.

On the other hand, if the rotation speed exceeds the point on the characteristic FG, the wind turbine is forcibly stopped because the mechanical service limit of the rotor of the generator 30 may be exceeded. If the windmill is forcibly stopped, it will not be able to generate electricity. That is, when the relationship between the wind speed and the rotation speed is in the region D, the possibility that the wind turbine is forcibly stopped increases, so such a situation is not desirable. Therefore, in the present embodiment, the lowest reward is set when the rotation speed of the wind turbine 20 is controlled within the range of the speed exceeding in the rotation speed corresponding to the wind speed in a strong wind. The range of the overspeed in this case is, for example, a predetermined range included in the region D corresponding to the wind speed.

Further, when the rotation speed is lower than the point on the characteristic FG, the rotation speed of the wind turbine 20 is excessively suppressed, and the generator 30 can generate power even though it can generate more power. It will be in a state where it is not. In this case, there is no concern that the wind turbine 20 will be forcibly stopped or the mechanical endurance limit of the generator 30 will be exceeded, but the generated power cannot be maximized. Therefore, in the present embodiment, when the rotation speed of the wind turbine 20 is controlled within the range of the speed shortage according to the wind speed in a strong wind, the reward is lower than the highest reward but higher than the lowest reward. Set. The range of insufficiency in this case is, for example, the range indicated by the region FBCG.

FIG. 5 is a diagram showing an example of the relationship between the wind speed and the output of the generated power in the wind power generation system 1 according to the first embodiment. The horizontal axis of FIG. 5 shows the wind speed [m / s], and the vertical axis shows the generated power [kW]. FIG. 5 shows the relationship between the wind power and the generated power when the rotation speed of the wind turbine is controlled based on the control characteristics in FIG. Further, in FIG. 5, as in FIG. 4, when the wind speed is B [m / s] or more, the wind becomes strong.
As shown in FIG. 5, the generated power increases cubicly according to the wind speed between the wind speed A [m / s] and the wind speed B [m / s], which are not strong winds. On the other hand, when the wind speed is higher than the wind speed B [m / s], which is a strong wind, the generated power is maintained at the maximum output Max by controlling the rotation speed according to the characteristic FG shown in FIG.

FIG. 6 is a flowchart showing an operation example of the control device 60 according to the first embodiment.
First, the parameter acquisition unit 61 of the control device 60 acquires the rotation speed control parameter from the reinforcement learning unit 71 (step S10).
Next, the state detection unit 62 detects the wind speed and the rotation speed (step S11). The state detection unit 62 detects the wind speed and the rotation speed by acquiring the wind speed detected by the wind speed sensor 41 and the rotation speed detected by the rotation speed sensor 42. The state detection unit 62 outputs the detected wind speed and rotation speed to the reward calculation unit 63.

Next, the reward calculation unit 63 calculates the reward.
First, the reward calculation unit 63 determines whether or not the wind speed is a strong wind (step S12). When the wind speed is a strong wind, the reward calculation unit 63 determines whether or not the rotation speed is equal to or higher than the first threshold value (step S13). When the rotation speed is equal to or higher than the first threshold value, the reward calculation unit 63 sets the reward as the first level reward (step S14). On the other hand, when the rotation speed is less than the first threshold value, the reward calculation unit 63 determines whether or not the rotation speed is equal to or higher than the second threshold value (step S16).

When the rotation speed is equal to or higher than the second threshold value, the reward calculation unit 63 sets the reward as the second level reward (step S17). On the other hand, when the rotation speed is less than the second threshold value, the reward calculation unit 63 sets the reward as the third level reward (step S18).

In step S12, if the wind speed is not a strong wind, the reward calculation unit 63 sets the reward as a normal level reward (step S19). The normal level reward is, for example, the highest reward when the rotation speed is controlled to be included in the appropriate range, and when it is out of the appropriate range, the direction is out of the range (excessive speed or insufficient speed). Regardless, the reward will be reduced according to the degree of deviation from the appropriate range.

The reward calculation unit 63 outputs the calculated reward to the reward output unit 64. The reward output unit 64 outputs the reward to the reinforcement learning unit 71 (step S15).

<Second embodiment>
Next, the second embodiment will be described.
This embodiment differs from other embodiments in that the controlled object of the control device 60 is regenerative power. Hereinafter, the points different from those of the above-described embodiment will be described, and the same reference numerals will be given to the configurations having the same or similar functions as those of the above-described embodiments, and the description thereof will be omitted.

First, as a premise, in wind power generation, the maximum value of regenerative power with respect to the wind speed received by the wind turbine 20 is uniquely determined as a characteristic value peculiar to each wind turbine. Then, the maximum value of this regenerative power is set as a target value of the regenerative power according to the wind speed, and the regenerative power is controlled so as to approach the target value of the regenerative power.

The control device 60 uses the learning device 70 to determine power control parameters. The control device 60 performs MPPT (Maximum Power Point Tracking) control so that the DC power output from the rectifying / boosting unit 31 approaches the power value specified by the power control parameter based on the power control parameter. Specifically, in the control device 60, the output power determined by the output voltage detected by the voltage detection unit 32 and the output current detected by the current detection unit is the target of the regenerative power specified by the power control parameter. The duty ratio of the PWM signal given to the rectifying / boosting unit 31 is changed so as to be a value.

The learning device 70 outputs a power control parameter based on the state parameter detected by the state detection unit 62 and the reward output by the reward output unit 64. Further, the learning device 70 observes a change in the state of the wind power generator main body 10 due to the output power control parameter being set in the rectifying / boosting unit 31 of the wind power generator main body 10, and is used as a state change or a reward. The following power control parameters are determined accordingly.

The reinforcement learning unit 71 acquires information indicating the wind speed, the rotation speed, and the regenerative power indicating the state of the wind power generator main body 10. Based on the acquired information indicating the wind speed, rotation speed, and regenerative power, and the information indicating the target value of the regenerative power, the reinforcement learning unit 71 proceeds with learning so that a higher reward can be obtained by using the reward as a clue, and is more appropriate. Power control parameters are output. Here, the target value of the regenerative power is the maximum value of the regenerative power that can be generated, which is determined for each wind speed.

When calculating the reward, the reward calculation unit 63 extracts a predetermined target section for which the reward is calculated. Here, the target section is a predetermined section determined according to the wind condition, and is, for example, a section in which a deceleration section in which the wind speed decelerates and an acceleration section in which the wind speed accelerates are combined.

In wind power generation, even though the wind speed is decelerating, if the wind turbine is continuously rotated at a rotation speed at which the maximum regenerative power corresponding to the wind speed can be obtained, the rotation of the wind turbine may stall due to the power generation load. there were. As a countermeasure for this, for example, in the deceleration section, the control to maximize the regenerative power is not performed, the control to maintain the rotation speed is performed so that the rotation does not stall, and the rotation speed is increased during acceleration to increase the regenerative power. It is conceivable to control so that By controlling in this way, it is possible to increase the total amount of power generation.

Therefore, in the present embodiment, the reward calculation unit 63 calculates a higher reward when the added value of the regenerative power in the predetermined target section is equal to or higher than the predetermined power threshold value. Further, the reward calculation unit 63 calculates a lower reward when the added value of the regenerative power in the target section is less than a predetermined power threshold value. As a result, the learning device 70 can be trained to output a power control parameter such that the added value of the regenerative power in a predetermined target section becomes high. For example, the learning device 70 is switched from a control that maximizes the regenerative power at which timing in the deceleration section to a control that maintains the rotation speed, and a control that maximizes the regenerative power from a control that maintains the rotation speed at which timing in the acceleration section. If you switch to, you can learn whether the total amount of power generation will increase.

FIG. 7 is a diagram showing a target section in the wind power generation system 1 according to the second embodiment. FIG. 7A is a diagram schematically showing the time change of the wind speed. FIG. 7B is an example of the time change of the wind speed. The horizontal axis of FIGS. 7A and 7B shows the time [min], and the vertical axis shows the wind speed [m / s] of the wind turbine 20.

As shown in FIG. 7A, in the time change of the wind speed, the peak P1 of acceleration is shown, then the peak P2 of deceleration is shown after turning to deceleration, and then the peak P3 of acceleration is shown after turning to acceleration. In this way, the wind speed changes while alternately repeating deceleration and acceleration. The reward calculation unit 63 extracts a target section in which the deceleration section and the subsequent acceleration section are combined, with the acceleration peak P1 to the deceleration peak P2 as the deceleration section and the deceleration peak P2 to the acceleration peak P3 as the acceleration section. do.

As shown in FIG. 7B, when the wind speed reaches the peak of acceleration at time T1 and then the decelerated wind speed reaches the peak of acceleration again at time T2, the target section is between time T1 and T2 (hereinafter, simply). It is described as "time T1 to T2"). Similarly, the times T2 to T3, the times T3 to T4, ... Are the target sections, respectively. The time of the target section may be any time determined according to the wind conditions, and the time of one target section and another target section may be different. Further, when the wind speed once decelerated is maintained for a while and then decelerated again, as in the time T4 to T5, the deceleration section may be set, and the target section may be combined with the subsequent acceleration section. Further, when the ratio of the deceleration section to the target section is extremely small such as time T5 to T6, or when the ratio of the increase section to the target section is extremely small such as time T6 to T7, the target section is also used. May be.

When the reward calculation unit 63 extracts the target section, for example, the wind speed V (n) detected by the state detection unit 62 and the wind speed V (n-1) detected by the state detection unit 62 before that. By calculating the wind speed difference (V (n) -V (n-1)), it is determined whether the wind speed is decelerating, decelerating peak, accelerating, or accelerating peak. .. When the wind speed difference is a negative value, the reward calculation unit 63 determines that the wind speed is decelerating. The reward calculation unit 63 determines that the wind speed is the peak of deceleration when the wind speed difference changes from a negative value to within a predetermined range close to 0 (zero) or 0 (zero). The reward calculation unit 63 determines that the wind speed is acceleration when the wind speed difference is a positive value. The reward calculation unit 63 determines that the wind speed is the peak of acceleration when the wind speed difference changes from a positive value to within a predetermined range close to 0 (zero) or 0 (zero).

The cycle for detecting the wind speed may be arbitrarily determined according to the control cycle (for example, 10 [ms]) for controlling the wind power generator main body 10, the wind condition, and the like. Further, since it is conceivable that there is a predetermined delay before the regenerative power according to the wind condition is output, the reward calculation unit 63 regenerates the time according to the target section in consideration of the predetermined delay time. The reward may be calculated based on the electric power. The delay time in this case may be a constant value regardless of the wind speed, or may be a value that fluctuates according to the wind speed.

Further, in the above, the reward calculation unit 63 calculates the reward based on the magnitude of the added value of the regenerative power in the target section, but the reward is not limited to this. The reward calculation unit 63 may calculate a higher reward when the rotation speed is maintained without stalling in the deceleration section, and may calculate a lower reward when the rotation speed stalls in the deceleration section.

Further, in the above, the reward calculation unit 63 has extracted the target section based on the wind speed, but the present invention is not limited to this. The reward calculation unit 63 may extract the target section by using the rotation speed instead of the wind speed, or may extract the target section by using the wind speed and the rotation speed.

Further, the reward calculation unit 63 may determine whether or not to calculate the reward based on the total power generation amount in the target section based on the wind speed. The reward calculation unit 63 determines that the reward is calculated based on the total power generation amount in the target section, for example, when the wind speed is less than a predetermined strong wind threshold value, that is, when the wind speed is not strong. On the other hand, the reward calculation unit 63 determines that the reward is not calculated based on the total power generation amount in the target section when the wind speed is equal to or higher than the predetermined strong wind threshold value, that is, when the wind speed is strong. This is because if an attempt is made to increase the amount of power generation in a strong wind, the wind turbine 20 may over-rotate.

FIG. 8 is a flowchart showing an operation example of the control device 60 according to the second embodiment.
First, the parameter acquisition unit 61 of the control device 60 acquires the power control parameter from the reinforcement learning unit 71 (step S20).
Next, the state detection unit 62 detects the wind speed, the rotation speed, and the regenerative power (step S21). The state detection unit 62 detects the wind speed and the rotation speed by acquiring the wind speed detected by the wind speed sensor 41 and the rotation speed detected by the rotation speed sensor 42. Further, the state detection unit 62 detects the regenerative power by acquiring the voltage of the regenerative power detected by the voltage detection unit 32 and the current of the regenerative power detected by the current detection unit 33. The state detection unit 62 outputs the detected wind speed, rotation speed, and regenerative power to the reward calculation unit 63.

Next, the reward calculation unit 63 calculates the reward.
First, the reward calculation unit 63 determines whether or not the wind speed is the peak of acceleration (step S22). When the wind speed is the peak of acceleration, the reward calculation unit 63 determines whether or not the added value (total power generation amount) of the regenerative power in the target section is equal to or higher than the predetermined power threshold value (step S23). When the total power generation amount is equal to or greater than the power threshold value, the reward calculation unit 63 sets the reward as the second level reward (step S24). On the other hand, when the total power generation amount is less than the power threshold value, the reward calculation unit 63 sets the reward as the first level reward lower than the second level (step S25).
The reward calculation unit 63 clears the total electric energy and returns to the process shown in step S20 (step S26).

On the other hand, if the wind speed is not the peak of acceleration in step S22, the reward calculation unit 63 adds the detected regenerative power to the total power generation amount, and returns to the process shown in step S20 (step S27).

<Third embodiment>
Next, a third embodiment will be described.
This embodiment is different from other embodiments in that when the rotation speed of the wind turbine 20 is controlled, different controls are performed according to the wind conditions. Hereinafter, the points different from those of the above-described embodiment will be described, and the same reference numerals will be given to the configurations having the same or similar functions as those of the above-described embodiments, and the description thereof will be omitted.

In the present embodiment, the wind conditions are classified into a deceleration section, an acceleration section, and a strong wind section, and the learning device 70 is made to learn to control the rotation speed of the wind turbine 20 based on each of the classified sections. Here, the deceleration section and the acceleration section are equivalent to the deceleration section and the acceleration section in the second embodiment. The strong wind section is a section in which the wind speed is equal to or higher than a predetermined strong wind determination threshold value.

In wind power generation, it is desirable to rotate the wind turbine at a rotation speed at which the maximum regenerative power corresponding to the wind speed can be obtained in the acceleration section. Regarding the relationship between the wind speed and the rotation speed, the rotation speed of the wind turbine 20 tends to increase as the wind speed accelerates, but the inertia of the wind turbine causes a non-constant delay with respect to the wind speed. Therefore, even when the wind turbine is rotated at a rotation speed corresponding to the wind speed, the maximum expected regenerative power may not be obtained. Therefore, it is desirable to control the acceleration section in consideration of the inertia of the wind turbine.

Further, in the deceleration section, if the wind turbine is continuously rotated at a rotation speed at which the maximum regenerative power corresponding to the wind speed can be obtained, the rotation of the wind turbine may stall due to the power generation load. Therefore, it is desirable that the deceleration section is controlled in consideration of the degree of change (deceleration) of the wind speed (the amount of change in the wind speed per unit time).

Further, in the strong wind section, the rotation speed of the wind turbine 20 is controlled so as not to exceed the speed by decelerating the rotation speed, but if the speed is reduced too much, the regenerative power may decrease. Therefore, in the strong wind section, it is desirable that the control is performed in consideration of the degree of change (acceleration) of the wind speed (the amount of change in the wind speed per unit time) and the regenerative power.

Therefore, in the present embodiment, in the acceleration section, the target range of the target range is based on the target value of the rotation speed of the wind turbine 20 determined according to the wind speed, and the target range is the rotation speed in a predetermined range including the reference target value. By controlling the rotation speed of the wind turbine 20 within and giving a higher reward when a larger regenerative power can be obtained, the learner is made to search for a more suitable target value according to the wind condition.

Specifically, the reinforcement learning unit 71 acquires information indicating the wind speed and the rotation speed indicating the state of the wind power generator main body 10. The reinforcement learning unit 71 uses the reward as a clue to raise the reward based on the acquired information indicating the wind speed and the rotation speed and the information indicating the rotation speed in a predetermined range that can be set for the wind turbine 20 including the target value of the regenerative power. We proceed with learning so that we can obtain more appropriate power control parameters. Here, the rotation speed in a predetermined range that can be set in the wind turbine 20 is a rotation speed in a predetermined range including the rotation speed of the wind turbine that maximizes the regenerative power determined for each wind speed. It is desirable that this range is within an allowable range as the rotation speed of the wind turbine 20 determined based on the mechanical service limit of the wind turbine 20 and the generator 30.

Further, the reward calculation unit 63 calculates a higher reward in the acceleration section when a larger regenerative power can be obtained. As a result, the reinforcement learning unit 71 outputs the rotation speed control parameter within the target range in the acceleration section, and the output rotation speed control parameter corresponds to the rotation state of the wind turbine 20 when the wind turbine 20 is set. And learn the control to obtain larger regenerative power.

Further, in the present embodiment, in the deceleration section, the reward calculation unit 63 calculates the reward according to the degree of deceleration of the wind speed. Specifically, the reward calculation unit 63 calculates a higher reward when the amount of change in the rotation speed of the wind turbine 20 with respect to the degree of deceleration of the wind speed is smaller. As a result, the reinforcement learning unit 71 learns the control for maintaining the rotational speed of the wind turbine 20 so as not to stall even when the wind speed is decelerated in the deceleration section.

Further, the reward calculation unit 63 calculates a higher reward when the added value of the regenerative power in the section (deceleration target section) in which the deceleration section and the subsequent acceleration section are combined is larger. As a result, the reinforcement learning unit 71 learns the control so that a larger regenerative power is output in the subsequent acceleration section even when the regenerative power becomes smaller in the deceleration section.

Further, in the present embodiment, in the strong wind section, when the added value of the regenerative power in the section including the strong wind acceleration section in which the wind speed accelerates in the strong wind and the subsequent deceleration section (strong wind target section) is larger, the additional value is higher. Calculate high rewards. As a result, the reinforcement learning unit 71 learns the control so that the regenerative power is not reduced too much in the strong wind section and the regenerative power is not reduced in the subsequent deceleration section.

In the strong wind section, the reward calculation unit 63 calculates the lowest reward when the rotation speed of the wind turbine 20 exceeds the speed.
As a result, the reward calculation unit 63 learns the control so as not to cause the speed to be exceeded in the strong wind section.

FIG. 9 is a flowchart showing an operation example of the control device 60 according to the third embodiment.
First, the parameter acquisition unit 61 of the control device 60 acquires the rotation speed control parameter within the target range from the reinforcement learning unit 71 (step S30).
Next, the state detection unit 62 detects the wind speed, the rotation speed, and the regenerative power (step S31).
Next, the reward calculation unit 63 determines whether or not to extract the section based on the acquired wind speed (step S32). The reward calculation unit 63 determines that the section is extracted when the acquired wind speed changes from a strong wind to a normal wind speed which is not a strong wind, when the acceleration peaks, or when the deceleration peaks. If the reward calculation unit 63 does not extract the section, the reward calculation unit 63 returns to step S30.

When extracting a section, the reward calculation unit 63 extracts the section until the wind speed becomes less than the predetermined strong wind determination threshold when the wind speed becomes equal to or higher than the predetermined strong wind determination threshold. Further, when the wind speed reaches the peak of acceleration, the reward calculation unit 63 extracts the period until the wind speed reaches the peak of deceleration as a deceleration section. When the wind speed reaches a predetermined deceleration peak, the reward calculation unit 63 extracts the period until the wind speed reaches the acceleration peak as an acceleration section.

The reward calculation unit 63 determines whether or not the extracted section is a strong wind section (step S33). When the extracted section is a strong wind section, the reward calculation unit 63 calculates a reward according to the added value of the regenerative power in the strong wind acceleration section and the subsequent deceleration section (step S34), while the reward calculation unit 63 calculates the section. If is not a strong wind section, it is determined whether or not the extracted section is a deceleration section (step S35).
When the extracted section is a deceleration section, the reward calculation unit 63 calculates a reward according to the added value of the regenerative power in the deceleration section and the subsequent acceleration section (step S36).
On the other hand, when the extracted section is not a deceleration section, that is, an acceleration section, the reward calculation unit 63 calculates a reward according to the regenerative power (step S37).

<Fourth Embodiment>
Next, a fourth embodiment will be described.
This embodiment is different from other embodiments in that the limit wind speed is changed according to the wind condition when the rotation speed of the wind turbine 20 is controlled. Hereinafter, the points different from those of the above-described embodiment will be described, and the same reference numerals will be given to the configurations having the same or similar functions as those of the above-described embodiments, and the description thereof will be omitted.

The critical wind speed is the upper limit of the wind speed that can be generated by the wind power generation system 1. The limit wind speed is determined in consideration of a predetermined margin for the breaking wind speed determined based on, for example, the mechanical service limit of the wind turbine 20 or the generator 30. Here, the breaking wind speed is such that the wind turbine 20 and the generator 30 are damaged or destroyed.

In the wind power generation system 1, when the wind speed received by the wind turbine 20 reaches the limit wind speed, the operation is stopped. In this case, the wind power generation system 1 forcibly stops the operation regardless of whether or not the power generation is controlled. Therefore, even when the power generation is controlled during a strong wind, the operation must be stopped when the wind speed reaches the limit wind speed, which may be a factor of reducing the regenerative power. Therefore, if the power generation is properly controlled even in a strong wind, it is desirable to change the critical wind speed in a direction closer to the breaking wind speed and control the power generation so that the power generation can be maintained.

Therefore, in the present embodiment, based on the stability of the control in the strong wind section, even if the wind speed is approaching the limit wind speed, if the stable control is performed, the wind speed is changed to approach the breaking wind speed to make it more stable. By giving a higher reward when control is performed, learning is performed so that more stable control is performed according to the wind conditions even in strong winds.

Specifically, the reward calculation unit 63 calculates the difference between the rotation speed of the wind turbine 20 and the maximum rotation speed and the rate of change of the rotation speed of the wind turbine 20 in the strong wind section, and is based on the calculated difference and the rate of change. And calculate the reward.

Further, the reward calculation unit 63 receives a higher reward than when the difference between the rotation speed of the wind turbine 20 and the maximum rotation speed is less than the predetermined margin threshold value and the change rate of the rotation speed of the wind turbine 20 is less than the predetermined change threshold value. (Second level) is calculated, and a lower reward (first level) is calculated than when the rate of change of the rotation speed is equal to or higher than a predetermined change threshold. This is because when the rate of change in the rotation speed of the wind turbine 20 is low, it can be determined that the rotation of the wind turbine 20 is controlled more stably than in the case where the rate of change in the rotation speed of the wind turbine 20 is high.

The reward calculation unit 63 receives a reward when the difference between the rotation speed of the wind turbine 20 and the maximum rotation speed is equal to or more than a predetermined margin threshold, and the rate of change of the rotation speed described above is equal to or more than a predetermined change threshold. A reward (third level) higher than the reward (second level) higher than the reward (second level) when the rate of change of the rotation speed is less than a predetermined change threshold is calculated. This is because when the difference between the rotation speed of the wind turbine 20 and the maximum rotation speed is equal to or larger than the margin threshold value, it can be determined that the control in a strong wind is excessively in a safe direction.

Here, the maximum rotation speed of the wind turbine 20 is the maximum value of the rotation speed that can be rotated by the wind turbine 20. The maximum rotation speed is, for example, an upper limit in strength design.

Further, in the control device 60 of the present embodiment, in a strong wind section, the difference between the rotation speed of the wind turbine 20 and the maximum rotation speed is less than a predetermined margin threshold value, and the change in the rotation speed of the wind turbine 20. When the rate is less than a predetermined change threshold, the critical wind speed is changed in a direction approaching the breaking wind speed. Specifically, the limit wind speed stored in the wind speed information storage unit (not shown) that stores the limit wind speed is rewritten. As a result, control is performed according to the changed critical wind speed. That is, the control device 60 refers to the limit wind speed stored in the wind speed information storage unit based on the wind speed detected by the state detection unit 62, and when the wind speed is equal to or higher than the limit wind speed, the wind power generator main body 10 Stop the operation of.

It is desirable to change the limit wind speed step by step based on a predetermined limit wind speed change value. The limit wind speed change value may be arbitrarily set based on the structure of the wind turbine 20, location conditions, seasons, and the like.

FIG. 10 is a flowchart showing an operation example of the control device 60 according to the fourth embodiment.
First, the parameter acquisition unit 61 of the control device 60 acquires the rotation speed control parameter from the reinforcement learning unit 71 (step S40).
Next, the state detection unit 62 detects the wind speed, the rotation speed, and the regenerative power (step S41).
Next, the reward calculation unit 63 determines whether or not the acquired wind speed is a strong wind (step S42).

When the acquired wind speed is a strong wind, the reward calculation unit 63 calculates the difference between the rotation speed acquired from the state detection unit 62 and the maximum rotation speed, and determines whether or not the calculated difference is equal to or greater than the margin threshold value (step). S43). When the calculated difference is equal to or greater than the margin threshold value, the reward calculation unit 63 calculates the third level reward (step S44).

When the calculated difference is less than the margin threshold value, the reward calculation unit 63 calculates the change rate of the rotation speed acquired from the state detection unit 62, and determines whether or not the calculated change rate is less than the predetermined change threshold value (. Step S45). When the calculated change rate is less than the change threshold value, the reward calculation unit 63 calculates the second level reward (highest level) (step S46). In this case, the control device 60 changes the critical wind speed toward the breaking wind speed (step S48).
When the calculated change rate is equal to or higher than the change threshold value, the reward calculation unit 63 calculates the first level reward (lowest level) (step S47).

(Fifth Embodiment)
Next, a fifth embodiment will be described.
The present embodiment differs from the above-described embodiment in that the control device 60 controls the rotation speed of the wind turbine 20 by using the trained model.
FIG. 11 is a block diagram showing an example of a schematic configuration of the wind power generation system 1A according to the modified example of the fifth embodiment. As shown in FIG. 11, the control device 60A includes a learned model storage unit 65, a determination unit 66, and a control unit 67.

The trained model storage unit 65 stores the trained model. The trained model is a database (learned model) in which information (relationship information) showing the relationship between the state of the wind power generator main body 10 to be controlled and the control for the wind power generator main body 10 is stored. The trained model is a model that estimates a parameter (hereinafter, referred to as a control index parameter) indicating an index for controlling the wind power generator main body 10 corresponding to the state of the wind power generator main body 10.

Here, the control index parameter is information that is an index for controlling the wind power generator main body 10, and may be the control parameter itself or the information used for deriving the control parameter.

For example, when the control index parameter is information that is an index for controlling the rotation of the wind turbine 20, the control index parameter may be the rotation speed control parameter itself, or indicates the rotation speed or the rotation speed of the wind turbine 20 numerically. It may be one, or it may be relatively indicating the control of the rotation speed of the wind turbine 20 such as increasing or decreasing the rotation speed or the rotation speed.

For example, when the control index parameter is information that is an index for controlling the regenerative power, the control index parameter may be the power control parameter itself, or may indicate a target value of the regenerative power. It may relatively indicate the control of the wind regenerative power such as increasing or decreasing the regenerative power.

The trained model may be, for example, a trained model created by learning by the reinforcement learning unit 71 in the above-described embodiment, or another wind turbine having a structure similar to that of the wind turbine 20. It may be a trained model that has learned the relationship between the state and control of the wind power generation system in a wind turbine installed in an area similar to the area where the wind turbine 20 is installed.

The determination unit 66 determines control information regarding control for the wind power generator main body 10 based on the acquired control index parameters. The control information here is information indicating control determined according to a control index parameter, for example, rotation information regarding the rotation of a wind turbine, and power information regarding, for example, regenerative power. That is, the determination unit 66 determines the rotation information based on the rotation index parameter. Further, the determination unit 66 determines the power information based on the power index parameter. The determination unit 66 outputs the determined rotation information to the control unit 67.

The rotation information here includes, for example, information indicating a change in the rotation speed such as whether to increase or decrease the rotation speed of the wind turbine, as well as a change in the rotation speed such as whether to change the wind turbine stepwise or at once. It also contains information that shows how to make it.

Further, in the electric power information, for example, in addition to information indicating a change in the regenerative power such as whether to increase or decrease the regenerative power, the degree to which the regenerative power is changed such as whether to change it stepwise or at once is included. Information to indicate is also included.

The control unit 67 determines the control parameters for controlling the wind power generator main body 10 based on the control information determined by the determination unit 66. The control unit 67 determines, for example, a rotation speed control parameter that controls the rotation of the wind turbine 20 so that the rotation of the wind turbine 20 falls within the allowable range and approaches the target, based on the rotation information determined by the determination unit 66. do. Further, the control unit 67 determines a power control parameter for controlling the regenerative power so that the regenerative power approaches the target, for example, based on the power information determined by the determination unit 66. The control unit 67 outputs the determined control parameter to the wind power generator main body 10 via the parameter acquisition unit 61.

Here, the permissible range in the rotation of the wind turbine 20 is the permissible range as the rotation speed of the wind turbine 20, for example, a region having a lower rotation speed than the characteristic EF and the characteristic FG in FIG. 4, that is, FIG. It is an area surrounded by AEFGC. Further, the target is a target value of the rotation speed of the wind turbine 20, and is, for example, a value along the characteristic EF and the characteristic FG in FIG.

(Sixth Embodiment)
Next, the sixth embodiment will be described.
In the present embodiment, the rotation speed of the wind turbine 20 is determined by using either the control index parameter (hereinafter, simply referred to as a parameter) output by the control device 60 using the trained model or the parameter output by the learning device 70. It differs from the above-described embodiment in that it is controlled.
FIG. 12 is a block diagram showing an example of a schematic configuration of the wind power generation system 1B according to the modified example of the sixth embodiment. As shown in FIG. 12, the control device 60B includes a selection unit 68.

The selection unit 68 outputs either a parameter output from the trained model stored in the trained model storage unit 65 or a parameter output by the learning device 70 to the determination unit 66. The selection unit 68 may determine which one to select according to a predetermined phase. The selection unit 68 selects, for example, the parameters output by the learning device 70 in the learning phase in which the learning device 70 learns the control of the rotation speed of the wind turbine 20. On the other hand, in the selection unit 68, the learning model learned to control the rotation speed of the windmill 20 is stored in the learned model storage unit 65, and in the control phase in which the learning model is used to control the rotation speed of the windmill 20. Select the parameter output from the trained model stored in the trained model storage unit 65.

Further, in at least one embodiment described above, the content learned by the reinforcement learning unit 71 is stored in the trained model storage unit 65 and other storage units (not shown), and further learning is performed based on the stored content. You may try to proceed. As a result, the model learned about some basic control common to the wind turbine 20 can be used for wind conditions in the area where the wind turbine 20 is installed, seasonal wind conditions, differences in wind conditions depending on the time of day and night, weather, etc. It is possible to perform control according to the state.
In the above-mentioned at least one embodiment, the case where the rotation speed control parameter is used as the parameter for controlling the rotation speed of the wind turbine 20 has been described as an example, but the present invention is not limited to this. The control system 50 may control the rotation speed, the rotation time, and the like as parameters for controlling the rotation speed of the wind turbine 20. In this case, the parameters that control the rotation speed of the wind turbine 20 may be, for example, a rotation speed parameter, a rotation time parameter, or the like. A rotation control parameter may be used as a general term for such parameters for controlling the rotation speed of the wind turbine 20. That is, the rotation speed control parameter is an example of the "rotation speed control parameter".

As described above, the control system 50 of the first embodiment has wind speed information indicating the wind speed at the installation location of the wind turbine 20 (for example, the wind speed detected by the wind speed sensor 41) and rotation information regarding the rotation of the wind turbine 20 (for example,). , The rotation speed detected by the rotation speed sensor 42), and the relation information indicating the allowable range and the target, which is the relation information between the wind speed and the rotation (for example, the relation between the wind speed and the rotation speed shown in FIG. 4). Based on this, the enhanced learning unit 71 that learns the correspondence information between wind speed and rotation, the learned model storage unit 65 that stores the correspondence information between wind speed and rotation, and the rotation control parameter that controls the rotation speed of the windmill are set to the windmill 20. The state detection unit 62 that detects the wind speed when set, the determination unit 66 that determines the rotation information based on the wind speed detected by the state detection unit 62, and the corresponding information, and the rotation information determined by the determination unit 66. Based on the above, the control unit 67 for controlling the rotation of the wind turbine 20 is provided so that the rotation information falls within the allowable range and approaches the target. As a result, the control system 50 of the embodiment can control so as to optimize the rotation speed of the wind turbine 20 even in an unstable wind condition.

Further, the control system 50 of the first embodiment further includes a reward calculation unit 63 that calculates a reward according to a predetermined reward condition based on the wind speed detected by the state detection unit 62 and the rotation speed of the wind turbine 20. The reinforcement learning unit 71 is a reinforcement learning model that learns correspondence information (for example, rotation speed control parameter) between wind speed and rotation based on a reward. Thereby, the control system 50 of the embodiment can learn more appropriate control as a reward clue.

Further, in the control system 50 of the first embodiment, the reward calculation unit 63 determines that the wind speed detected by the state detection unit 62 is a strong wind and the rotation speed of the wind turbine 20 is equal to or higher than the first threshold value. Calculate the first level reward. Further, the reward calculation unit 63 is higher than the first level when the wind speed detected by the state detection unit 62 is a strong wind and the rotation speed of the wind turbine 20 is equal to or higher than the second threshold value smaller than the first threshold value. Calculate the second level reward. Further, when the wind speed detected by the state detection unit 62 is a strong wind and the rotation speed of the wind turbine 20 is less than the second threshold value, the reward calculation unit 63 is higher than the first level and is at the second level. Calculate the lower third level reward. Thereby, the control system 50 of the embodiment can control so that the rotation speed does not exceed when the wind speed is a strong wind. In addition, if the rotation speed does not exceed, it is possible to learn so that the rotation speed is within the appropriate range rather than becoming insufficient, so even in strong winds where the rotation speed tends to exceed, it is forcibly stopped. It is possible to suppress the storage and to learn to maintain the maximum generated power.

Further, the learning device 70 of the first embodiment learns the correspondence information between the wind speed and the rotation based on the wind speed information indicating the wind speed at the installation location of the wind turbine 20 of the wind power generation system 1 and the rotation information regarding the rotation of the wind turbine 20. Since the enhanced learning unit 71 is provided, it is possible to learn how to control the rotation of the wind turbine according to the state of the wind speed and the rotation of the wind turbine. It becomes possible to control the rotation more appropriately.

Further, the control device 60 of the first embodiment has a state detection unit 62 for detecting the wind speed when the rotation control parameter for controlling the rotation speed of the wind turbine 20 of the wind power generation system 1 is set in the wind turbine 20, and a state detection unit. A determination unit that determines rotation information regarding the rotation of the wind turbine based on the wind speed detected by 62 and the correspondence information between the wind speed and the rotation of the wind turbine (for example, the trained model stored in the trained model storage unit 65). 66 is provided with a control unit 67 that controls the rotation of the wind turbine 20 based on the rotation information determined by the determination unit 66. As a result, the control device 60 of the embodiment controls according to the control parameters output from the trained model when the state such as the wind speed is a state similar to the trained state in the trained model. It can be controlled more appropriately.

As described above, the control system 50 of the second embodiment has wind speed information indicating the wind speed at the installation location of the wind turbine 20 (for example, the wind speed detected by the wind speed sensor 41) and rotation information regarding the rotation of the wind turbine 20 (for example,). , Rotation speed detected by the rotation speed sensor 42), power information regarding the regenerative power generated by the power generation system (for example, the voltage of the regenerative power detected by the voltage detection unit 32, and the regeneration detected by the current detection unit 33). The enhanced learning unit 71, which learns the correspondence information between wind speed, rotation, and regenerative power, based on the relational information between wind speed, rotation, and regenerative power, which indicates the target of regenerative power. Detects changes in regenerative power and wind speed when the trained model storage unit 65, which stores information on the correspondence between wind speed, rotation, and regenerative power, and the rectification / booster unit 31 are controlled based on the power control parameters that control regenerative power. Based on the state detection unit 62, the determination unit 66 that determines the power information based on the regenerative power and wind speed detected by the state detection unit 62, and the corresponding information, and the power information determined by the determination unit 66. A control unit 67 that controls the regenerated electric power is provided so as to approach the target of the electric power information. As a result, the control system 50 of the embodiment can be controlled so that the total generated power is maximized even when the wind condition changes.

Further, the control system 50 of the second embodiment further includes and strengthens a reward calculation unit 63 that calculates a reward according to a predetermined reward condition based on the wind speed detected by the state detection unit 62 and the regenerative power. The learning unit 71 is a reinforcement learning model that learns correspondence information (for example, power control parameter) between wind speed and regenerated power based on a reward. Thereby, the control system 50 of the embodiment can learn more appropriate control by using the reward as a clue.

Further, in the control system 50 of the second embodiment, the reward calculation unit 63 calculates the first level reward when the regenerative power detected by the state detection unit 62 is less than a predetermined power threshold value. Further, the reward calculation unit 63 calculates a second level reward higher than the first level when the regenerative power detected by the state detection unit 62 is equal to or higher than a predetermined power threshold value. Thereby, the control system 50 of the embodiment can be controlled so that the regenerative power becomes large.
Further, in the control system 50 of the second embodiment, the reward calculation unit 63 extracts a target section including a deceleration section and a subsequent acceleration section based on the wind speed detected by the state detection unit 62, and the target section. When the added value of the regenerative power detected by the state detection unit 62 is equal to or higher than the predetermined power threshold value, the second level reward is calculated. As a result, in the control system 50 of the second embodiment, even if the rotation of the wind turbine may stall if the regenerative power is continuously output in the deceleration section, the output of the regenerative power is suppressed in the deceleration section to accelerate. It is possible to learn the control such as outputting the regenerative power higher in the section and control so that the total regenerative power in the target section becomes large.
Further, in the control system 50 of the second embodiment, the reward calculation unit 63 calculates the reward when the wind speed detected by the state detection unit 62 is less than a predetermined strong wind determination threshold value. As a result, in the control system 50 of the second embodiment, it is possible to suppress erroneous control that causes over-rotation in an attempt to increase the regenerative power even in a strong wind.

Further, the learning device 70 of the second embodiment relates to wind speed information indicating the wind speed at the installation location of the wind turbine 20 of the wind power generation system 1, rotation information regarding the rotation of the wind turbine 20, and regenerative power generated by the wind power generation system 1. Since the enhanced learning unit 71 that learns the correspondence information between the wind speed, the rotation, and the regenerated power based on the power information is provided, how to control the regenerated power according to the state of the wind speed, the rotation of the wind turbine, and the regenerated power is determined. Since it can be learned, it becomes possible to more appropriately control the regenerated power even when the wind conditions change.

Further, the control device 60 of the second embodiment detects a state of detecting the regenerated power and the wind speed when the power control parameter for controlling the regenerated power generated by the wind power generation system 1 is set in the rectifying / boosting unit 31. Based on the regenerative power and wind speed detected by the unit 62 and the state detection unit 62, and the correspondence information between the wind speed and the rotation of the wind turbine and the regenerative power (for example, the trained model stored in the trained model storage unit 65). A determination unit 66 that determines power information regarding the regenerative power, and a control unit 67 that controls the regenerative power based on the power information determined by the determination unit 66 are provided. As a result, the control device 60 of the embodiment controls according to the control parameters output from the trained model when the state such as the wind speed is a state similar to the trained state in the trained model. It can be controlled more appropriately.

As described above, the control system 50 of the third embodiment has wind speed information indicating the wind speed at the installation location of the wind turbine 20 (for example, the wind speed detected by the wind speed sensor 41) and rotation information regarding the rotation of the wind turbine 20 (for example). , Rotational speed detected by the rotation speed sensor 42), power information regarding the regenerated power generated by the power generation system (for example, the voltage of the regenerated power detected by the voltage detection unit 32, and the regeneration detected by the current detection unit 33). Wind speed, rotation, and regenerative power based on the relationship information between wind speed, rotation, and regenerative power, which indicates the range of the rotation speed that can be set for the wind turbine, including the target of the rotation speed. The enhanced learning unit 71 that learns the correspondence information with, the trained model storage unit 65 that stores the correspondence information of the wind speed, the rotation, and the regenerative power, and the rotation speed and the wind speed when controlled based on the rotation control parameters are detected. Based on the state detection unit 62, the determination unit 66 that determines the rotation information based on the rotation speed and wind speed detected by the state detection unit 62, and the corresponding information, and the rotation information determined by the determination unit 66. A control unit 67 that controls the rotation of the wind turbine 20 is provided. As a result, the control system 50 of the embodiment can control so that the amount of generated power is maximized even when the wind condition changes.

As described above, the control system 50 of the fourth embodiment has wind speed information indicating the wind speed at the installation location of the wind turbine 20 (for example, the wind speed detected by the wind speed sensor 41) and rotation information regarding the rotation of the wind turbine 20 (for example). , Rotation speed detected by the rotation speed sensor 42), power information regarding the regenerative power generated by the power generation system (for example, the voltage of the regenerative power detected by the voltage detection unit 32, and the regeneration detected by the current detection unit 33). The wind speed, rotation, and regenerative power are based on the relationship information between the wind speed, rotation, and regenerative power, which indicates the wind speed and the maximum value of the rotation speed at which the wind turbine can rotate. An enhanced learning unit 71 that learns correspondence information, a learned model storage unit 65 that stores correspondence information between wind speed, rotation, and regenerative power, and a state that detects the rotation speed and wind speed when controlled based on rotation control parameters. The wind turbine 20 is based on the rotation speed and wind speed detected by the detection unit 62, the determination unit 66 that determines the rotation information based on the corresponding information, and the rotation information determined by the determination unit 66. A control unit 67 for controlling the rotation of the wind turbine is provided. As a result, the control system 50 of the embodiment can be controlled so that the total generated power is maximized even when the wind condition changes.

All or part of the processing performed by each of the control system 50, the control device 60, and the learning device 70 in the above-described embodiment may be realized by a computer. In that case, a program for realizing this function may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read by a computer system and executed. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. Further, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, or a CD-ROM, and a storage device such as a hard disk built in a computer system. Further, a "computer-readable recording medium" is a communication line for transmitting a program via a network such as the Internet or a communication line such as a telephone line, and dynamically holds the program for a short period of time. It may also include a program that holds a program for a certain period of time, such as a volatile memory inside a computer system that is a server or a client in that case. Further, the above program may be for realizing a part of the above-mentioned functions, and may be further realized for realizing the above-mentioned functions in combination with a program already recorded in the computer system. It may be realized by using a programmable logic device such as FPGA.

Although the embodiments of the present invention have been described in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and includes designs and the like within a range that does not deviate from the gist of the present invention.

1 Wind power generation system 10 Wind power generator body 20 Wind turbine 30 Generator 31 Rectification / boosting unit 32 Voltage detection unit 33 Current detection unit 41 Wind speed sensor 42 Rotation speed sensor 50 Control system 60 Control device 61 Parameter acquisition unit 62 State detection unit 63 Reward Calculation unit 64 Reward output unit 65 Learned model storage unit 66 Determination unit 67 Control unit 70 Learning device 71 Enhanced learning unit

Claims (10)

A control system that controls the number of revolutions of a wind turbine in a wind power generation system.
Wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, rotation information regarding the rotation of the wind turbine, and information on the relationship between the wind speed and the rotation of the wind speed and the rotation speed at which the wind turbine can rotate. A learning unit that learns the correspondence information between the wind speed and the rotation based on the relational information indicating the maximum value.
A storage unit that stores information on the correspondence between the wind speed and the rotation,
A state detection unit that detects the rotation information and the wind speed information when the rotation control parameter for controlling the rotation speed of the wind turbine is set for the wind turbine.
A determination unit that determines the rotation information based on the rotation information, the wind speed information, and the corresponding information detected by the state detection unit.
A control unit that controls the rotation of the wind turbine based on the rotation information determined by the determination unit is provided .
When the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed in the strong wind section where the wind speed is equal to or higher than the predetermined strong wind determination threshold value in the time-series change of the wind speed is less than the predetermined margin threshold value. Moreover, when the rate of change of the rotation speed indicated in the rotation information detected by the state detection unit is less than a predetermined change threshold value, it is the upper limit of the wind speed at which the wind power generation system can generate power. Increase wind speed limit
Control system. Further, a reward calculation unit for calculating a reward according to a predetermined reward condition based on the rotation information detected by the state detection unit and the wind speed information is provided.
The control system according to claim 1, wherein the related information includes a reward calculated by the reward calculation unit, and the learning unit learns the corresponding information based on the reward by reinforcement learning . The control system according to claim 2, wherein the reward calculation unit calculates a reward based on the maximum value of the rotation speed, the rotation speed, and the rate of change of the rotation speed. A control system that controls the number of revolutions of a wind turbine in a wind power generation system.
Wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, rotation information regarding the rotation of the wind turbine, and information on the relationship between the wind speed and the rotation of the wind speed and the rotation speed at which the wind turbine can rotate. A learning unit that learns the correspondence information between the wind speed and the rotation based on the relational information indicating the maximum value.
A storage unit that stores information on the correspondence between the wind speed and the rotation,
A state detection unit that detects the rotation information and the wind speed information when the rotation control parameter for controlling the rotation speed of the wind turbine is set for the wind turbine.
A determination unit that determines the rotation information based on the rotation information, the wind speed information, and the corresponding information detected by the state detection unit.
A control unit that controls the rotation of the wind turbine based on the rotation information determined by the determination unit is provided .
Further, a reward calculation unit for calculating a reward according to a predetermined reward condition based on the rotation information detected by the state detection unit and the wind speed information is provided.
The relational information includes the reward calculated by the reward calculation unit.
The learning unit learns the corresponding information by reinforcement learning based on the reward, and
The reward calculation unit calculates a reward based on the maximum value of the rotation speed, the rotation speed, and the rate of change of the rotation speed.
The reward calculation unit is a case where the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed is less than a predetermined margin threshold value, and is detected by the state detection unit. When the change rate of the rotation speed shown in the rotation information is equal to or more than a predetermined change threshold value, the reward of the first level is calculated, and when the change rate is less than the change threshold value, the second level is higher than the first level. Calculate level rewards
Control system. When the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed is equal to or greater than the margin threshold value, the reward calculation unit is higher than the first level and higher than the second level. The control system of claim 4, which calculates a lower third level reward. When the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed in the strong wind section where the wind speed is equal to or higher than the predetermined strong wind determination threshold value in the time-series change of the wind speed is less than the predetermined margin threshold value. Moreover, when the rate of change of the rotation speed indicated in the rotation information detected by the state detection unit is less than a predetermined change threshold value, it is the upper limit of the wind speed at which the wind power generation system can generate power. The control system according to claim 4 or 5 , which increases the wind speed limit. Wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, rotation information regarding the rotation of the wind turbine, power information regarding the regenerative power generated by the wind power generation system, and information on the relationship between the wind speed and the rotation. The correspondence information between the wind speed and the rotation is learned based on the relational information indicating the limit wind speed which is the limit of the wind speed that can be generated by the wind power generation system and the maximum value of the rotation speed at which the wind turbine can rotate. It is a learning device equipped with a learning unit and capable of obtaining the reward required by the control device .
The control device is
A state detection unit that detects the rotation information and the wind speed information when the rotation control parameter for controlling the rotation speed of the wind turbine is set for the wind turbine.
With the reward calculation unit that calculates the reward according to the predetermined reward condition based on the rotation information detected by the state detection unit and the wind speed information.
Equipped with
The reward calculation unit calculates a reward based on the maximum value of the rotation speed, the rotation speed, and the rate of change of the rotation speed.
The reward calculation unit is a case where the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed is less than a predetermined margin threshold value, and is detected by the state detection unit. When the change rate of the rotation speed shown in the rotation information is equal to or more than a predetermined change threshold value, the reward of the first level is calculated, and when the change rate is less than the change threshold value, the second level is higher than the first level. Calculate the level reward,
The relational information includes the reward calculated by the reward calculation unit.
The learning unit learns the corresponding information by reinforcement learning based on the reward.
Learning device. When the rotation control parameter for controlling the rotation speed of the wind turbine of the wind power generation system is set in the wind power generation system, the rotation information regarding the rotation of the wind turbine, the power information regarding the regenerative power generated by the wind power generation system, and the wind turbine. A state detector that detects wind speed information related to the wind speed in
A determination unit that determines the rotation information based on the power information, the wind speed information, and the correspondence information between the wind speed and the rotation of the wind turbine detected by the state detection unit.
A control unit that controls the rotation speed based on the rotation information determined by the determination unit is provided .
When the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed in the strong wind section where the wind speed is equal to or higher than the predetermined strong wind determination threshold value in the time-series change of the wind speed is less than the predetermined margin threshold value. Moreover, when the rate of change of the rotation speed indicated in the rotation information detected by the state detection unit is less than a predetermined change threshold value, it is the upper limit of the wind speed at which the wind power generation system can generate power. Increase wind speed limit
Control device. It is a control method that controls the rotation speed of the wind turbine of the wind power generation system.
The learning unit has wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, rotation information regarding the rotation of the wind turbine, and information on the relationship between the wind speed and the rotation, and the wind speed and the wind turbine can rotate. Based on the relational information indicating the maximum value of the rotation speed, the correspondence information between the wind speed and the rotation is learned, and the correspondence information is learned.
The storage unit stores the correspondence information between the wind speed and the rotation, and stores the correspondence information.
The state detection unit detects the rotation information and the wind speed information when the rotation control parameter for controlling the rotation speed is set in the wind turbine.
The determination unit determines the rotation information based on the rotation information, the wind speed information, and the corresponding information detected by the state detection unit.
The control unit controls the rotation of the wind turbine based on the rotation information determined by the determination unit .
When the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed in the strong wind section where the wind speed is equal to or higher than the predetermined strong wind determination threshold value in the time-series change of the wind speed is less than the predetermined margin threshold value. Moreover, when the rate of change of the rotation speed indicated in the rotation information detected by the state detection unit is less than a predetermined change threshold value, it is the upper limit of the wind speed at which the wind power generation system can generate power. Increase wind speed limit
Control method. It is a control method that controls the rotation speed of the wind turbine of the wind power generation system.
The learning unit has wind speed information indicating the wind speed at the installation location of the wind turbine of the wind power generation system, rotation information regarding the rotation of the wind turbine, and information on the relationship between the wind speed and the rotation, and the wind speed and the wind turbine can rotate. Based on the relational information indicating the maximum value of the rotation speed, the correspondence information between the wind speed and the rotation is learned, and the correspondence information is learned.
The storage unit stores the correspondence information between the wind speed and the rotation, and stores the correspondence information.
The state detection unit detects the rotation information and the wind speed information when the rotation control parameter for controlling the rotation speed is set in the wind turbine.
The reward calculation unit calculates a reward according to a predetermined reward condition based on the rotation information and the wind speed information detected by the state detection unit.
The reward calculation unit calculates a reward based on the maximum value of the rotation speed, the rotation speed, and the rate of change of the rotation speed.
The reward calculation unit is a case where the difference between the rotation speed detected by the state detection unit and the maximum value of the rotation speed is less than a predetermined margin threshold value, and is detected by the state detection unit. When the change rate of the rotation speed shown in the rotation information is equal to or more than a predetermined change threshold value, the reward of the first level is calculated, and when the change rate is less than the change threshold value, the second level is higher than the first level. Calculate the level reward,
The relational information includes the reward calculated by the reward calculation unit.
The learning unit learns the corresponding information by reinforcement learning based on the reward, and
The determination unit determines the rotation information based on the rotation information, the wind speed information, and the corresponding information detected by the state detection unit.
A control method in which the control unit controls the rotation of the wind turbine based on the rotation information determined by the determination unit.

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