CN113623126A - Direct-drive permanent magnet hydroelectric power generation system control method, system, terminal and readable storage medium based on fuzzy control - Google Patents

Direct-drive permanent magnet hydroelectric power generation system control method, system, terminal and readable storage medium based on fuzzy control Download PDF

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CN113623126A
CN113623126A CN202110699171.8A CN202110699171A CN113623126A CN 113623126 A CN113623126 A CN 113623126A CN 202110699171 A CN202110699171 A CN 202110699171A CN 113623126 A CN113623126 A CN 113623126A
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power
variation
rotating speed
water turbine
fuzzy
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CN113623126B (en
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罗德荣
周强
李孟秋
吴比
谭志红
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Hunan University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B15/00—Controlling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/001—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0014—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2101/00—Special adaptation of control arrangements for generators
    • H02P2101/10—Special adaptation of control arrangements for generators for water-driven turbines
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2103/00—Controlling arrangements characterised by the type of generator
    • H02P2103/20—Controlling arrangements characterised by the type of generator of the synchronous type
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/20—Hydro energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a control method, a system, a terminal and a readable storage medium of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, wherein the control method aims at the situation that a water turbine exists in the conventional maximum power tracking control process: the method has the advantages that the tracking speed is low, the steady-state precision is low, a large amount of energy is easily lost in the tracking process, the efficiency is reduced, the comprehensive characteristic curve of the water turbine is used for extracting data of power P, rotating speed n and flow Q, the data of the power P, the rotating speed n and the flow Q are trained through a BP neural network, and the neural network offline model is obtained. The method has the advantages that the initial rotating speed is evaluated by utilizing the neural network offline model, the maximum power is tracked by using the fuzzy control MPPT, the combination of the neural network and the fuzzy controller is realized, in addition, the influence of the change of the flow Q is also considered, the new maximum power point is quickly tracked, the power loss and the energy waste are reduced, and the efficiency of the water turbine is improved.

Description

Direct-drive permanent magnet hydroelectric power generation system control method, system, terminal and readable storage medium based on fuzzy control
Technical Field
The invention belongs to the technical field of hydroelectric power generation, and particularly relates to a control method, a control system, a control terminal and a readable storage medium of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control.
Background
Compared with other traditional fossil energy, hydropower does not generate chemical change in the process of converting the hydropower into the electric energy, does not discharge harmful substances, does not generate pollution to air and water, and is high-quality renewable energy. The constant-speed constant-frequency system is limited by the frequency of a power grid, the output of a water turbine is greatly limited, and therefore a variable-speed constant-frequency hydroelectric generation technology is developed. The direct-drive permanent magnet hydroelectric power generation system realizes variable-speed constant-frequency power generation by rectifying and inverting through a back-to-back three-phase bridge circuit, the permanent magnet synchronous generator still operates in a synchronous mode, stepless speed regulation can be realized in a full-power and speed range, and the direct-drive permanent magnet hydroelectric power generation system is well applied to small hydropower.
The variable-speed constant-frequency hydroelectric generation can improve the efficiency of the water turbine, so that the rotating speed of the generator set can be changed according to the change of the inflow, and the optimal water energy capture efficiency of the water turbine in an operation interval can be effectively ensured, thereby increasing the generated energy of a system.
In a direct-drive permanent magnet hydroelectric power generation system, the control of a permanent magnet synchronous generator generally adopts rotating speed control, and the torque is automatically adjusted by a rotating speed ring. The output power of the water turbine is determined by the actual working water head and flow of the hydropower station and the water turbine water energy conversion efficiency, the working efficiency of the water turbine can be changed by changing the rotating speed of the water turbine according to the rotating speed characteristic curve of the water turbine, and the output power is controlled.
The commonly used control methods at present are: 1. the method comprises the following steps of storing a database according to a water head, flow, guide vane opening and the like of the hydropower station, looking up a table to obtain the optimal rotating speed under the current flow, and solving the problems of accuracy and complexity; 2. the method for establishing the water turbine efficiency model by using the data fitting method to solve the optimal rotating speed has the defects that the model solution is too complex, and errors exist in the efficiency model along with the time. 3. The method has the advantages that certain disturbance is applied to the permanent magnet synchronous generator by using an observation disturbance method, the power feedback condition is observed, the rotating speed is further adjusted until the optimal rotating speed is searched, the defects that the steady state time is long easily caused by overlarge step length setting, the optimization time is long easily caused by small step length, and once the flow changes caused by weather or season influence, a new working point is difficult to quickly search, and the dynamic performance is poor.
Therefore, the technical defect that the optimal rotating speed is difficult to accurately track in the existing control method, so that the output power of the water turbine is maximum exists, and the control method is easy to cause insufficient precision when approaching the maximum power point or cause continuous oscillation near the highest point.
Disclosure of Invention
The invention aims to provide a control method, a system, a terminal and a readable storage medium of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control aiming at the problem that the optimal rotating speed is difficult to accurately track in the existing control method.
On one hand, the invention provides a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, which comprises the following steps:
step 1: constructing a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
step 2: performing maximum power tracking by using the fuzzy controller;
firstly, applying rotation speed disturbance, recording the power variation and the rotation speed variation of the water turbine, and inputting the power variation and the rotation speed variation into the fuzzy controller to obtain the rotation speed variation at the current moment;
secondly, after the rotating speed variation is superposed, the water turbine is controlled to reach the corresponding rotating speed, and the power variation of the water turbine is recorded;
then, inputting the power variation and the rotation speed variation at the previous moment into the fuzzy controller to obtain the rotation speed variation at the next moment, and circulating the processes until the power variation is smaller than a preset power threshold value Pc;
and when the rotating speed variation is smaller than the preset power threshold Pc, the power corresponding to the hydraulic turbine is considered to be at the maximum power.
In a second aspect, the invention provides a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, which is applied to the working condition of constant flow of a water turbine, and the control method comprises the following steps:
s1-1: inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine, wherein the neural network is obtained by taking the comprehensive characteristic curve power P and the flow Q of the water turbine as training input data and taking the rotating speed n as training output data and carrying out network training;
s1-2: controlling the rotating speed of the water turbine to reach the initial rotating speed, and then carrying out maximum power tracking control based on a fuzzy controller, wherein the process of carrying out maximum power tracking control based on the fuzzy controller is the implementation process of step 2 in claim 1.
According to the invention, a neural network is combined with fuzzy control, the initial rotating speed is evaluated by using the neural network, the tracking time of the fuzzy control is reduced, the tracking precision is improved by using the fuzzy control, and the condition that the precision is insufficient when the maximum power point is close to or the continuous oscillation occurs near the highest point is avoided.
In a third aspect, the invention provides a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, which is applied to the working condition of variable flow of a water turbine, and the control method comprises the following steps:
s2-1: collecting power variation Pe, and if Pm > Pe > Ps, executing step S2-2; if Pc < Pe < Ps, go to step S2-3; if Pe > Pm, executing step S2-4, where Pm is the set power variation threshold value and Ps is the set power threshold upper limit;
s2-2: performing maximum power tracking control based on a fuzzy controller;
s2-3: maintaining the current control state;
s2-4: inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine, controlling the rotating speed of the water turbine to reach the initial rotating speed, and then carrying out maximum power tracking control based on a fuzzy controller.
In the existing control method, the flow Q is generally regarded as fixed, however, in the practical application process, the flow Q is influenced by weather or seasons, so that a new working point is difficult to quickly find, and the dynamic performance is poor. Aiming at the problem, the invention combines the neural network with the fuzzy control, and simultaneously considers the change influence of the flow Q. The highest point can be quickly tracked while the flow is changed, the convergence speed is high, the precision is high, and the energy loss in the tracking process is reduced.
Optionally, the fuzzy subset of power variations is represented as: { NB, NM, NS, ZO, PO, PS, PM, PB }, with a domain range of [ -8,8], NB, NM, NS, ZO, PO, PS, PM, PB denoting: negative large, negative middle, negative small, negative zero, positive small, positive middle, positive large, { NM, NS, ZO, PO, PS, PM }, { NM, NS, PS, PM } all use triangle membership functions, and { NB, PB } use trapezoid membership functions;
the rotation speed variation at the previous moment and the fuzzy subset of the rotation speed variation at the current moment are both expressed as: { NB, NM, NS, PS, PM, PB }, wherein the domain range corresponding to the rotating speed variation at the previous moment is [ -6,6], and the domain range corresponding to the rotating speed variation at the current moment is [ -4,4 ];
the fuzzy rules in the fuzzy controller are as follows:
Figure BDA0003129078650000031
and Pe is a fuzzy subset of the power variation, Sn-1 is a fuzzy subset of the rotation speed variation at the previous moment, and Sn is a fuzzy subset of the rotation speed variation at the current moment.
Optionally, before the neural network training, the data of the power P, the rotation speed n and the flow Q are subjected to normalization conversion, and the performance of the neural network is represented by using the MAD value.
In a fourth aspect, the present invention provides a control system for a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, comprising:
the fuzzy controller building module is used for building a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output of the fuzzy controller is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
the fuzzy tracking module is used for tracking the maximum power by using the fuzzy controller until the power variation is smaller than the preset power threshold value Pc, and looking at that the corresponding power of the water turbine is at the maximum power;
wherein the fuzzy tracking module comprises: the device comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to the fuzzy controller, and the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller and controlling the water turbine according to the superposed rotating speed.
In a fifth aspect, the invention provides a control system of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, which comprises:
the neural network construction module is used for training a neural network;
the initial rotating speed prediction module is used for inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine;
the fuzzy controller building module is used for building a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output of the fuzzy controller is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
the fuzzy tracking module is used for tracking the maximum power by using the fuzzy controller until the power variation is smaller than the preset power threshold value Pc, and looking at that the corresponding power of the water turbine is at the maximum power;
wherein the fuzzy tracking module comprises: the system comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to a fuzzy controller, the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller, controlling the water turbine according to the superposed rotating speed and controlling the rotating speed of the water turbine to reach the initial rotating speed.
In a sixth aspect, the present invention provides a control system for a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, comprising:
the power identification module is used for acquiring the power variation Pe and judging the power variation Pe, a power variation threshold Pm, a power threshold upper limit Ps and a power threshold Pc;
the fuzzy controller building module is used for building a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output of the fuzzy controller is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
the fuzzy tracking module is used for tracking the maximum power by using the fuzzy controller when Pm is greater than Pe and Ps until the power variation is smaller than the preset power threshold Pc, and observing that the power corresponding to the water turbine is at the maximum power;
the neural network construction module is used for training a neural network;
the initial rotating speed prediction module is used for inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine when Pe is larger than Pm; and the fuzzy tracking module carries out maximum power tracking by utilizing the fuzzy controller.
Wherein the fuzzy tracking module comprises: the system comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to a fuzzy controller, the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller, controlling the water turbine according to the superposed rotating speed and controlling the rotating speed of the water turbine to reach the initial rotating speed.
In a seventh aspect, the present invention provides a terminal comprising a processor and a memory, the memory storing a computer program, the processor calling the computer program to implement:
a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control.
In an eighth aspect, the present invention provides a readable storage medium storing a computer program for invocation by a processor to implement:
a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control.
Advantageous effects
1. According to the control method of the direct-drive permanent magnet hydroelectric power generation system based on the fuzzy control, the fuzzy controller is constructed, the tracking precision is improved by utilizing the characteristics of the fuzzy controller, and the condition that the precision is insufficient when the direct-drive permanent magnet hydroelectric power generation system is close to a maximum power point or the direct-drive permanent magnet hydroelectric power generation system continuously vibrates near the highest point is avoided.
2. The invention further provides a control method, which combines the neural network with the fuzzy controller, on one hand, the neural network is used for evaluating the initial rotating speed and reducing the tracking time of the fuzzy control, and on the other hand, the fuzzy control is used for improving the tracking precision. In addition, the condition that the flow Q is changed is also considered, the highest point can be quickly tracked while the flow Q is changed, the convergence speed is high, the precision is high, and the energy loss in the tracking process is reduced.
Drawings
FIG. 1 is a control flow chart of a control method described in embodiment 2 of the invention;
FIG. 2 is a control flowchart of the control method described in embodiment 3 of the invention;
FIG. 3 is a flow, rotational speed and power curve of a water turbine according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a control system employing an embodiment of the present invention;
fig. 5 is a diagram of a BP neural network structure according to an embodiment of the present invention.
Detailed Description
The invention provides a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, which aims to realize the optimal rotating speed tracking of a water turbine and ensure that the water turbine keeps the maximum power.
Example 1:
the emphasis of this embodiment is to achieve maximum power tracking with a fuzzy controller. The fuzzy controller constructed by the present embodiment will be explained below.
Aiming at a certain flow rate, a rotating speed-power curve of the water turbine is an approximate parabola, and the unique maximum power point is tracked by continuously changing the rotating speed of the water turbine. Therefore, the fuzzy controller constructed in this embodiment is a two-dimensional fuzzy controller, the input data of the fuzzy controller is the power variation Pe within the sampling time T and the last-time rotation speed variation Sn-1, and the output data of the fuzzy controller is the current-time rotation speed variation Sn. Compared with a disturbance observation method, the rotating speed step length can be changed in real time by utilizing the fuzzy controller, and the tracking speed can be accelerated and the steady-state oscillation at the maximum power point can be reduced when the rotating speed step length is in a non-optimal operation area.
The present invention defines fuzzy subset Pe as { NB, NM, NS, ZO, PO, PS, PM, PB }, denoted negative large, negative medium, negative small, negative zero, positive small, positive medium, positive large, respectively. The fuzzy subsets Sn-1, Sn are defined as { NB, NM, NS, PS, PM, PB }, which are respectively expressed as negative big, negative middle, negative small, positive middle, and positive big. The range of discourse of the power variation Pe is [ -8,8],
the domain scope of the last moment rotating speed variation Sn-1 is [ -6,6], and the current moment rotating speed variation Sn is [ -4,4 ]. The fuzzy controller input variable membership functions are divided into two types, wherein { NM, NS, ZO, PO, PS, PM } is subjected to triangular membership functions, and { NB, PB } is subjected to trapezoidal membership functions.
The present invention sets the following fuzzy rules based on the control principle, as shown in table 1 below:
TABLE 1
Figure BDA0003129078650000061
It should be understood that the inverse transformation process of the fuzzy controller is implemented by using the prior art, and the present invention is not particularly limited thereto.
Based on the fuzzy controller, the method for controlling a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control provided by embodiment 1 of the invention comprises the following steps:
step 1: constructing a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
step 2: performing maximum power tracking by using the fuzzy controller;
firstly, applying rotation speed disturbance, recording the power variation and the rotation speed variation of the water turbine, and inputting the power variation and the rotation speed variation into the fuzzy controller to obtain the rotation speed variation at the current moment;
secondly, after the rotating speed variation is superposed, the water turbine is controlled to reach the corresponding rotating speed, and the power variation of the water turbine is recorded;
and then, inputting the power variation and the rotating speed variation at the previous moment into the fuzzy controller to obtain the rotating speed variation at the next moment. It should be understood that the closer to the maximum power point, the less the amount of power change caused by the change in the rotational speed thereof. And circulating the above processes until the power variation Pe is smaller than the preset power threshold Pc, and considering that the maximum power of the water turbine is tracked, wherein the Pc value of the invention is a power variation value in a smaller range at the maximum power point of the flow-rotating speed curve of the water turbine, and the Pc value can be set according to the actual precision requirement and experience, and when Pe is smaller than Pc, the power variation caused by rotating speed variation is considered to be extremely small, namely the current power variation is closer to the maximum power point. In this embodiment, the power threshold Pc is set to 50-100W, and in other possible embodiments, the power threshold Pc may be adaptively adjusted according to actual conditions.
Example 2:
as shown in fig. 1, in the present embodiment, the neural network is combined with the fuzzy control, so as to improve the tracking efficiency and accuracy. The control method for the direct-drive permanent magnet hydroelectric power generation system based on the fuzzy control, provided by the embodiment 2, comprises the following steps:
s1-1: inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine, wherein the neural network is obtained by taking the power P and the flow Q of the comprehensive characteristic curve of the water turbine as training input data and taking the rotating speed n as training output data and carrying out network training. Because the comprehensive characteristic curve data of the water turbine is used as training data, and a trained model can be approximately used as a rotating speed-power curve of the current water turbine, the output rotating speed n of a neural network is not necessarily the optimal rotating speed of the actual water turbine in operation, but in the range near the optimal rotating speed, the rotating speed is used as the initial rotating speed of the water turbine (the time for tracking the maximum power to the initial rotating speed in the traditional mode can be saved). The training process of the BP neural network architecture shown in fig. 5 is as follows:
1) extracting data of comprehensive characteristic curve power P, rotating speed n and flow Q of the water turbine; the power P and the flow Q are used as the input of a BP neural network, and the rotating speed n is used as the output of the neural network.
2) Carrying out normalization conversion processing on the data of the power P, the rotating speed n and the flow Q, wherein the formula is as follows:
Figure BDA0003129078650000071
in the formula, a is the original data,
Figure BDA0003129078650000072
for normalized data, amaxIs the maximum value in the sample data, aminIs the minimum value.
3) The performance of the BP neural network is expressed by MAD, which is the average value of absolute errors between the numerical result predicted by the model and the actual result, the smaller the value of MAD is, the better the BP neural network is, and the formula is expressed as follows:
Figure BDA0003129078650000073
μiis the output value of the rotation speed under the current model, XiFor the real data speed output value, n represents the data volume.
4) And training the BP neural network by taking the normalized data as sample data, reversely adjusting the weight and the threshold layer by adopting a reverse feedback mode, continuously updating the current weight and the threshold, then substituting an average absolute error function to solve the MAD, stopping training the model when the MAD is less than 0.0001 to obtain the optimal weight and threshold, and considering the model as the optimal BP neural network model.
S1-2: and controlling the rotating speed of the water turbine to reach the initial rotating speed, and then carrying out maximum power tracking control based on a fuzzy controller. For the process of constructing the fuzzy controller and the process of controlling by using the fuzzy controller, please refer to the related statements of embodiment 1, which are not described herein again.
It should be noted that the flow rate Q in this embodiment 2 is regarded as a fixed value. As shown in fig. 4, in the embodiment, when the direct-drive permanent magnet hydraulic grid-connected power generation system is operated, the machine side adopts a unit power factor vector control mode, CLARKE and PARK conversion is performed on three-phase current collected by the machine side to obtain equivalent direct current id and iq, id is a torque current component, iq is a weak magnetic current component, a rotating speed and current double closed-loop control strategy is adopted, and an SVPWM is adopted as a modulation algorithm; the network side adopts an active and reactive decomposition vector control mode, CLARKE and PARK conversion is carried out on three-phase current collected by the network side to obtain equivalent direct current id and iq, a d axis coincides with a power grid electromotive force vector, id is an active current component, iq is a reactive current component, a voltage and current double closed-loop control strategy is adopted, and SVPWM is adopted as a modulation algorithm.
Example 3:
as shown in fig. 2, in this embodiment, it is considered that, in an actual working condition, a flow rate is likely to change along with an influence of weather or season, and therefore, the control method for a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, provided by this embodiment 3, is applied to a variable flow rate working condition of a water turbine, and the control method includes:
s2-1: collecting power and calculating a variation Pe, if Pm > Pe > Ps, indicating that the flow Q is greatly changed to enable the water turbine to deviate from the optimal rotating speed to be far under the influence of weather or seasons, and executing a step S2-2; if Pc < Pe < Ps, the flow Q changes slightly to cause the deviation of the water turbine from the optimal rotating speed to be small, and step S2-3 is executed; if Pe is larger than Pm, the flow rate Q is greatly changed, the current rotating speed is far away from the optimal rotating speed, and step S2-4 is executed. Wherein, Pm, Ps are variable threshold values set for the power difference generated when the flow is changed and the rotating speed is not changed, and the meaning of Pm is regarded as: the large change of the flow rate can cause the water turbine to deviate from the optimal rotating speed to a large extent, namely, the power change threshold value set based on the working condition, namely Pe > Pm, is regarded as that the flow rate Q is changed to be large and the current rotating speed is cheap and the optimal rotating speed is caused. The meaning of Ps is considered as: the small change of the flow rate can cause the deviation of the water turbine from the optimal rotating speed to be small, namely, the power threshold upper limit set based on the working condition, namely Pe < Ps, is regarded as that the change of the flow rate Q is small, and the influence is limited.
In this embodiment, the upper limit Ps of the power threshold is set to 200W, and the power variation threshold Pm under flow rate variation is set to 400W. It should be understood that other possible embodiments may be adapted.
S2-2: and carrying out maximum power tracking control based on the fuzzy controller. For the process of constructing the fuzzy controller and the process of controlling by using the fuzzy controller, please refer to the related statements of embodiment 1, which are not described herein again.
S2-3: the current control state is maintained, at the moment, the flow Q is slightly changed or slightly disturbed, the power change is small, the maximum power can be considered to be tracked, and unnecessary energy loss is reduced;
s2-4: inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine, controlling the rotating speed of the water turbine to reach the initial rotating speed, and carrying out maximum power tracking control based on a fuzzy controller. For the training process of the neural network, the construction process of the fuzzy controller, and the process of controlling by using the fuzzy controller, please refer to the related statements of embodiment 1 and embodiment 2, which are not repeated herein.
Example 4:
the present embodiment is directed to the control system of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control provided in embodiment 1, and the control system includes: the fuzzy controller comprises a fuzzy controller building module and a fuzzy tracking module. Particularly, maximum power tracking is realized by using a fuzzy controller.
The fuzzy controller building module is used for building a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output of the fuzzy controller is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
the fuzzy tracking module is used for carrying out maximum power tracking by utilizing the fuzzy controller; wherein the fuzzy tracking module comprises: the device comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to the fuzzy controller, and the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller and controlling the water turbine according to the superposed rotating speed.
For the specific implementation process of each unit module, refer to the corresponding process of the foregoing method. It should be understood that, the specific implementation process of the above unit module refers to the method content, and the present invention is not described herein in detail, and the division of the above functional module unit is only a division of a logic function, and there may be another division manner in the actual implementation, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. Meanwhile, the integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
Example 5:
the present embodiment is directed to embodiment 2 and provides a control system of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, which includes: the device comprises a neural network construction module, an initial rotating speed prediction module, a fuzzy controller construction module and a fuzzy tracking module.
The neural network construction module is used for training a neural network;
the initial rotating speed prediction module is used for inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine;
the fuzzy controller building module is used for building a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output of the fuzzy controller is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
the fuzzy tracking module is used for carrying out maximum power tracking by utilizing the fuzzy controller; wherein the fuzzy tracking module comprises: the device comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to the fuzzy controller, and the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller and controlling the water turbine according to the superposed rotating speed.
For the specific implementation process of each unit module, refer to the corresponding process of the foregoing method. It should be understood that, the specific implementation process of the above unit module refers to the method content, and the present invention is not described herein in detail, and the division of the above functional module unit is only a division of a logic function, and there may be another division manner in the actual implementation, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. Meanwhile, the integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
Example 6:
the present embodiment is directed to embodiment 3 to provide a control system of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control, which includes: the device comprises a power identification module, a fuzzy controller construction module, a fuzzy tracking module, a neural network construction module and an initial rotating speed prediction module.
The power identification module is used for acquiring a power variation Pe and judging the power variation Pe, a power variation threshold Pm, a power threshold upper limit Ps and a power threshold Pc;
the fuzzy controller building module is used for building a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output of the fuzzy controller is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
the fuzzy tracking module is used for tracking the maximum power by utilizing the fuzzy controller when Pm > Pe > Ps; wherein the fuzzy tracking module comprises: the system comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to a fuzzy controller, and the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller and controlling the water turbine according to the superposed rotating speed;
the neural network construction module is used for training a neural network;
the initial rotating speed prediction module is used for inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine when Pe is larger than Pm; and the fuzzy tracking module carries out maximum power tracking by utilizing the fuzzy controller.
For the specific implementation process of each unit module, refer to the corresponding process of the foregoing method. It should be understood that, the specific implementation process of the above unit module refers to the method content, and the present invention is not described herein in detail, and the division of the above functional module unit is only a division of a logic function, and there may be another division manner in the actual implementation, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. Meanwhile, the integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
Example 7:
the present embodiment provides a terminal, which includes a processor and a memory, where the memory stores a computer program, and the processor calls the computer program to implement: a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control.
The implementation process of the control method of the direct-drive permanent magnet hydroelectric power generation system based on the fuzzy control can refer to the relevant statements of the embodiments 1-3.
The specific implementation process of each step refers to the explanation of the foregoing method.
It should be understood that in the embodiments of the present invention, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include both read-only memory and random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information.
Example 8:
the present embodiments provide a readable storage medium storing a computer program for invocation by a processor to implement: a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control.
The implementation process of the control method of the direct-drive permanent magnet hydroelectric power generation system based on the fuzzy control can refer to the relevant statements of the embodiments 1-3.
The specific implementation process of each step refers to the explanation of the foregoing method.
The readable storage medium is a computer readable storage medium, which may be an internal storage unit of the controller according to any of the foregoing embodiments, for example, a hard disk or a memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the controller. Further, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used for storing the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.
Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the invention is not to be limited to the examples described herein, but rather to other embodiments that may be devised by those skilled in the art based on the teachings herein, and that various modifications, alterations, and substitutions are possible without departing from the spirit and scope of the present invention.

Claims (10)

1. A control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control is characterized by comprising the following steps: the method comprises the following steps:
step 1: constructing a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
step 2: performing maximum power tracking by using the fuzzy controller;
firstly, applying rotation speed disturbance, recording the power variation and the rotation speed variation of the water turbine, and inputting the power variation and the rotation speed variation into the fuzzy controller to obtain the rotation speed variation at the current moment;
secondly, after the rotating speed variation is superposed, the water turbine is controlled to reach the corresponding rotating speed, and the power variation of the water turbine is recorded;
then, inputting the power variation and the rotation speed variation at the previous moment into the fuzzy controller to obtain the rotation speed variation at the next moment, and circulating the processes until the power variation is smaller than a preset power threshold value Pc;
and when the power variation is smaller than the preset power threshold Pc, considering that the power corresponding to the water turbine is at the maximum power.
2. A control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control is characterized by comprising the following steps: the control method is applied to the working condition that the flow of the water turbine is constant, and comprises the following steps:
s1-1: inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine, wherein the neural network is obtained by taking the comprehensive characteristic curve power P and the flow Q of the water turbine as training input data and taking the rotating speed n as training output data and carrying out network training;
s1-2: controlling the rotating speed of the water turbine to reach the initial rotating speed, and then carrying out maximum power tracking control based on a fuzzy controller, wherein the process of carrying out maximum power tracking control based on the fuzzy controller is the implementation process of the step 2 in the claim 1;
3. a control method of a direct-drive permanent magnet hydroelectric power generation system based on fuzzy control is characterized by comprising the following steps: the control method is applied to the working condition of variable flow of the water turbine, and comprises the following steps:
s2-1: collecting power variation Pe, and if Pm > Pe > Ps, executing step S2-2; if Pc < Pe < Ps, go to step S2-3; if Pe > Pm, executing step S2-4, where Pm is the set power variation threshold value and Ps is the set power threshold upper limit;
s2-2: performing maximum power tracking control based on a fuzzy controller;
s2-3: maintaining the current control state;
s2-4: inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine, controlling the rotating speed of the water turbine to reach the initial rotating speed, and then carrying out maximum power tracking control based on a fuzzy controller.
4. A method according to any one of claims 1-3, characterized in that: the fuzzy subset of power variations is represented as: { NB, NM, NS, ZO, PO, PS, PM, PB }, with a domain range of [ -8,8], NB, NM, NS, ZO, PO, PS, PM, PB denoting: negative large, negative middle, negative small, negative zero, positive small, positive middle, positive large, { NM, NS, ZO, PO, PS, PM }, { NM, NS, PS, PM } all use triangle membership functions, and { NB, PB } use trapezoid membership functions;
the rotation speed variation at the previous moment and the fuzzy subset of the rotation speed variation at the current moment are both expressed as: { NB, NM, NS, PS, PM, PB }, wherein the domain range corresponding to the rotating speed variation at the previous moment is [ -6,6], and the domain range corresponding to the rotating speed variation at the current moment is [ -4,4 ];
the fuzzy rules in the fuzzy controller are as follows:
Figure FDA0003129078640000021
and Pe is a fuzzy subset of the power variation, Sn-1 is a fuzzy subset of the rotation speed variation at the previous moment, and Sn is a fuzzy subset of the rotation speed variation at the current moment.
5. A method according to any one of claims 1-3, characterized in that: before the neural network is trained, the data of the power P, the rotating speed n and the flow Q are subjected to normalized conversion, and the performance of the neural network is represented by the MAD value.
6. A control system according to claim 1, wherein: the method comprises the following steps:
the fuzzy controller building module is used for building a fuzzy controller, wherein the input of the fuzzy controller is power variation and rotation speed variation at the previous moment, and the output of the fuzzy controller is the rotation speed variation at the current moment; the sampling duration of the power variation is set to be T, and the T is a positive integer;
the fuzzy tracking module is used for tracking the maximum power by using the fuzzy controller until the power variation is smaller than the preset power threshold value Pc, and looking at that the corresponding power of the water turbine is at the maximum power;
wherein the fuzzy tracking module comprises: the device comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to the fuzzy controller, and the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller and controlling the water turbine according to the superposed rotating speed.
7. A control system according to claim 2, wherein: the method comprises the following steps:
the neural network construction module is used for training a neural network;
the initial rotating speed prediction module is used for inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine;
the fuzzy tracking module is used for tracking the maximum power by using the fuzzy controller until the power variation is smaller than the preset power threshold value Pc, and looking at that the corresponding power of the water turbine is at the maximum power;
wherein the fuzzy tracking module comprises: the system comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to a fuzzy controller, the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller, controlling the water turbine according to the superposed rotating speed and controlling the rotating speed of the water turbine to reach the initial rotating speed.
8. A control system according to claim 3, wherein: the method comprises the following steps:
the power identification module is used for acquiring the power variation Pe and judging the power variation Pe, a power variation threshold Pm, a power threshold upper limit Ps and a power threshold Pc;
the fuzzy tracking module is used for tracking the maximum power by using the fuzzy controller when Pm is greater than Pe and Ps until the power variation is smaller than the preset power threshold Pc, and observing that the power corresponding to the water turbine is at the maximum power;
the neural network construction module is used for training a neural network;
the initial rotating speed prediction module is used for inputting the flow Q and the power P of the water turbine into a neural network to predict the initial rotating speed of the water turbine when Pe is larger than Pm; the fuzzy tracking module carries out maximum power tracking by utilizing the fuzzy controller;
wherein the fuzzy tracking module comprises: the system comprises a collecting unit and a control unit, wherein the collecting unit is used for recording the power variation and the rotating speed variation of the water turbine and inputting the power variation and the rotating speed variation to a fuzzy controller, the control unit is used for superposing the rotating speed variation obtained from the fuzzy controller, controlling the water turbine according to the superposed rotating speed and controlling the rotating speed of the water turbine to reach the initial rotating speed.
9. A terminal, characterized by: comprising a processor and a memory, the memory storing a computer program that the processor calls to implement:
the process steps of any one of claims 1 to 5.
10. A readable storage medium, characterized by: a computer program is stored, which is invoked by a processor to implement:
the process steps of any one of claims 1 to 5.
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