CN113482853B - Yaw control method, system, electronic equipment and storage medium - Google Patents
Yaw control method, system, electronic equipment and storage medium Download PDFInfo
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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
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/0204—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor for orientation in relation to wind direction
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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
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
- F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/82—Forecasts
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/84—Modelling or simulation
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/60—Control system actuates through
- F05B2270/602—Control system actuates through electrical actuators
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- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
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- 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
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention relates to a yaw control method, a yaw control system, an electronic device and a storage medium. The method comprises the following steps: acquiring various historical wind condition data, and predicting the wind speed and the wind direction of a short period of time in the future by using the acquired various historical wind condition data; calculating the yaw time of each measure in the future short term according to the predicted wind direction; calculating the fan energy obtaining conditions of each subsection in a short term in the future according to the predicted wind speed and wind direction; and calculating the wasted wind energy and the wind energy obtained by the fan as a whole according to the yaw time and the wind energy obtaining condition of the fan, and selecting a yaw target capable of obtaining the maximum wind energy. According to the scheme of the invention, the selection of the optimal yaw target is realized, the wind energy utilization rate is improved, the condition of stopping the cable winding is reduced, and the generating capacity of the fan is finally improved.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to an intelligent yaw control method and system of a wind generating set, electronic equipment and a computer readable storage medium.
Background
With the increasing awareness of environmental protection and the demand for clean energy, wind energy has been widely regarded as a renewable energy source. In recent years, the development and utilization of wind energy are continuously strengthened in various countries, and wind power technology is vigorously developed. Meanwhile, the development of wind energy is gradually saturated in coastal areas and some areas with high wind speed, people begin to utilize wind resources in low wind speed areas, but the wind direction and the wind speed of the low wind speed areas are constantly changed due to the complex terrain of the low wind speed areas, so that in order to improve the utilization rate of the wind energy, the front face of a cabin of a wind driven generator is required to be ensured to face the wind all the time.
Therefore, a yaw system is additionally arranged in the wind driven generator, and the direction of a cabin of the wind driven generator can be continuously adjusted according to the change of the wind direction so as to ensure that the wind driven generator always faces the wind frontally, thereby utilizing the wind energy to the maximum extent. The yaw system of the wind power generator plays a role in tracking the wind direction, and the yaw system generally drives a gear of a yaw speed reducer through a yaw motor to adjust the direction so that the front of the nacelle faces the wind. However, because the wind direction and the wind speed of the low wind speed area change frequently, the problem that the yaw action cannot keep up with the change of the wind speed and the wind direction can be caused by directly transplanting the existing yaw and pitch control strategy of the fan, and the wind speed and the wind direction of the fan change before, when or in a short time after the corresponding yaw action is finished aiming at the current wind direction, so that the yaw action is frequent, the invalid yaw times and the yaw starting and stopping times are increased, and the unwarranted loss of a mechanical system is increased.
Disclosure of Invention
An object of the present invention is to solve at least one of the problems in the background art described above and to provide a yaw control method, a yaw control system, an electronic apparatus, and a storage medium. And a yaw target is selected to yaw according to the predicted wind condition data, so that the invalid yaw times and yaw starting and stopping times are reduced, the wind energy utilization rate and the intolerant loss of a mechanical system are improved, and the generated energy of the fan is finally improved.
In order to achieve the purpose, the technical scheme provided by the invention comprises the following steps:
collecting various historical wind condition data of the wind driven generator;
training a prediction model by using the historical wind condition data;
inputting real-time wind condition data into the prediction model, and outputting wind condition data of a plurality of sections of time periods in the future;
judging the wind condition data of a plurality of sections in the future, calculating and selecting a yaw target capable of obtaining the wind energy with the maximum value, and issuing a yaw instruction;
and identifying the yaw command, and enabling the yaw motor to rotate correspondingly to realize the yaw control of the fan.
According to one aspect of the invention, each historical wind condition data is collected by the wind measuring system and transmitted to the main controller through communication, and the main controller divides each historical wind condition data into sections according to the set small time.
According to one aspect of the invention, the training of the predictive model comprises:
the prediction model carries out distributed storage and parallel cooperative processing on various historical wind condition data acquired by the wind measuring system according to a set learning criterion;
giving a random value in the interval (0,1) to each connection weight value of the prediction model;
inputting image modes corresponding to various historical wind condition data into a prediction model, weighting and summing the input modes by the prediction model, comparing the input modes with a threshold, and performing nonlinear operation to obtain the output of the prediction model;
in this case, the prediction model randomly outputs (0,1) the number of intervals, and if the output is the number corresponding to the specific bar of the historical wind condition data, the connection weight will be increased, so that when the prediction model meets the specific bar of the historical wind condition data again, the corresponding judgment can still be made.
According to one aspect of the invention, the selection of the yaw target comprises:
the yaw main controller takes the predicted wind direction as a yaw target, calculates the difference value between the current just-facing angle of the fan and the yaw target angle, actively judges the yaw direction, and calculates the yaw time required by the current yaw target according to the yaw speed of the fan;
in the current subsection, the yaw main controller respectively judges each subsection which follows the current subsection in turn as a yaw target;
when the required yaw time for the nth section in the future as a yaw target is less than the total time of the n sections, and the yaw time for each section from the current section to the future is greater than the total time of the n sections, it is determined that the nth section as the yaw target can reduce the waste of wind energy;
if the yaw target cannot be found by recurrently deducing to a plurality of future time period sections, the wind energy of the current section in the plurality of future time period sections must be wasted, and at the moment, the fan performs yaw action in the direction opposite to the cable winding direction;
when a plurality of yaw targets appear in a plurality of time period subsections in the future, the yaw main controller respectively obtains action plans of the fan in the plurality of time period subsections in the future through wind condition data of the plurality of time period subsections in the future, calculates wind energy obtained by each subsection, accumulates total wind energy obtained by the yaw target according to the wind energy obtained by each subsection, and selects the yaw target capable of obtaining the maximum wind energy.
According to one aspect of the invention, when the yaw main controller detects that the wind speed continuously exceeds the dangerous wind speed for 2 times, the yaw main controller automatically issues a stop instruction and a 90-degree crosswind yaw instruction, and the fan is stopped and turned to a 90-degree crosswind state in a yaw mode; when the condition of the limit cable twisting occurs, a shutdown automatic cable releasing yaw instruction is given.
According to one aspect of the invention, the wind direction is determined as follows:
the wind direction is divided into N sectors according to the angle, each sector is 360/N degrees, the central angle of the sector represents the wind direction, the half sector angle represents the wind alignment error, the yaw alignment error is 180/N degrees, the value of N is 16, or an integer which can be divided by 360 between 12 and more than or equal to N and more than or equal to 24.
According to one aspect of the invention, the selection of the maximum wind energy comprises:
wind energy calculation formula:
where ρ is the air density, C p For the wind energy utilization rate, R is the radius of a wind wheel, v is the wind speed, theta is the included angle between the wind direction and the wind direction, lambda is the tip speed ratio of the blade, and beta is the blade propellerThe pitch angle, n, is the impeller rotational speed.
And calculating the wind energy obtained by each section according to a wind energy calculation formula, accumulating the total wind energy obtained by the yaw target, and selecting the yaw target capable of obtaining the maximum wind energy.
To achieve the above object, the present invention provides a yaw control system including:
the data acquisition module is used for acquiring various historical wind condition data of the wind driven generator and transmitting the various historical wind condition data to the main controller through communication, and the main controller divides the various historical wind condition data into sections according to set small time;
the model construction module is used for inputting the historical wind condition data divided according to the set time into the prediction model, and the prediction model is trained in the model according to the set learning criterion;
the data prediction module inputs real-time wind condition data into the trained prediction model and predicts wind condition data of a plurality of time period measures in the future;
the data analysis module is used for judging the wind condition data of a plurality of time periods in the future, calculating and selecting a yaw target capable of obtaining the wind energy with the maximum value, and issuing a yaw instruction;
and the yaw action module is used for identifying the yaw command and enabling the yaw motor to rotate correspondingly so as to realize the yaw control of the fan.
To achieve the above object, the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements a yaw control method as described above.
To achieve the above object, the present invention provides a storage medium, characterized in that the storage medium stores thereon a computer program, which when executed by a processor implements the yaw control method.
Based on this, compared with the prior art, the invention has the following beneficial effects:
the yaw main controller judges the predicted wind condition data, selects a proper yaw target for yaw through comparison of the yaw time of a certain section in the future with the section time, eliminates invalid yaw, reduces the starting and stopping times of yaw, and further reduces the unwarranted loss of a mechanical system.
The invention provides a yaw control method, a yaw control system, electronic equipment and a storage medium, wherein when a yaw main controller processes wind condition data, the wind condition data output by prediction of a prediction model is referred to instead of historical wind condition data, so that errors and hysteresis generated in a yaw process are reduced, and wind energy is fully utilized.
The yaw action and the pitch change action are carried out simultaneously, and the invalid yaw is eliminated, so that the starting and stopping times of the yaw are reduced, the loads of the blade root and the hub of the fan are reduced, and the service life of the fan is prolonged.
Drawings
FIG. 1 schematically shows a flow chart of a yaw control method;
FIG. 2 schematically shows a flow chart of a yaw target selection method;
FIG. 3 schematically shows a block diagram of a yaw control system.
Detailed Description
The contents of the present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only for the purpose of enabling a person of ordinary skill in the art to better understand and thus implement the contents of the present invention, and do not imply any limitation on the scope of the present invention.
As used herein, the term "include" and its variants are to be read as open-ended terms meaning "including, but not limited to. The term "based on" is to be read as "based, at least in part, on". The terms "one embodiment" and "an embodiment" are to be read as "at least one embodiment".
In order to make those skilled in the art better understand the technical solution of the present invention, the method and system for controlling the yaw of a wind turbine provided by the present invention are further described in detail with reference to the accompanying drawings.
Fig. 1 schematically shows a flow chart of a yaw control method. As shown in fig. 1, a yaw controlling method according to the present invention includes the steps of:
101. collecting various historical wind condition data of the wind driven generator;
102. training a prediction model by using the historical wind condition data;
103. inputting real-time wind condition data into the prediction model, and outputting wind condition data of a plurality of sections of time periods in the future;
104. judging the wind condition data of the sections of the plurality of time periods in the future, calculating and selecting a yaw target capable of obtaining the wind energy with the maximum value, and issuing a yaw instruction;
105. and identifying the yaw command, and enabling the yaw motor to rotate correspondingly to realize the yaw control of the fan.
According to one embodiment of the invention, the historical wind condition data are collected by a wind measuring system and transmitted to the main controller through communication, and the main controller divides the historical wind condition data into sections according to a set small time.
According to one embodiment of the invention, the training of the predictive model comprises: the prediction model performs distributed storage and parallel cooperative processing on various historical wind condition data acquired by the wind measuring system according to a set learning criterion; giving random values in the interval (0,1) to each connection weight value of the prediction model; inputting image modes corresponding to various historical wind condition data into a prediction model, weighting and summing the input modes by the prediction model, comparing the input modes with a threshold, and performing nonlinear operation to obtain the output of the prediction model; in this case, the prediction model randomly outputs (0,1) the number of intervals, and if the output is the number corresponding to the specific bar of the historical wind condition data, the connection weight will be increased, so that when the prediction model meets the specific bar of the historical wind condition data again, the corresponding determination can still be made.
According to one embodiment of the invention, the selection of the yaw target comprises: the yaw main controller takes the predicted wind direction as a yaw target, calculates the difference value between the current just-facing angle of the fan and the yaw target angle, actively judges the yaw direction, and calculates the yaw time required by the current yaw target according to the yaw speed of the fan; in the current subsection, the yaw main controller respectively judges each subsection which follows the current subsection in turn as a yaw target; when the required yaw time for taking the nth section in the future as a yaw target is less than the total time of the n sections, and the yaw time for each section from the current section to the future is greater than the total time of the n sections, determining that taking the nth section as the yaw target can reduce the waste of wind energy; if the yaw target cannot be found by recurrently deducing to a plurality of future time period sections, the wind energy of the current section in the plurality of future time period sections must be wasted, and at the moment, the fan performs yaw action in the direction opposite to the cable winding direction; when a plurality of yaw targets appear in a plurality of time period sections in the future, the yaw main controller respectively obtains action plans of the fan in the plurality of time period sections in the future according to the wind condition data of the plurality of time period sections in the future, calculates wind energy obtained by each section, accumulates total wind energy obtained by the yaw target according to the wind energy obtained by each section, and selects a yaw target capable of obtaining maximum wind energy, as shown in fig. 2.
According to one embodiment of the invention, when the yaw main controller detects that the wind speed exceeds the dangerous wind speed for 2 times continuously, the yaw main controller automatically issues a stop instruction and a 90-degree crosswind yaw instruction, and the fan is stopped and turned to a 90-degree crosswind state in a yaw mode; when the condition of the limit cable twisting occurs, a shutdown automatic cable releasing yaw instruction is given.
According to one embodiment of the invention, the wind direction is determined as follows: the wind direction is divided into N sectors according to angles, each sector is 360/N degrees, the central angle of each sector represents the wind direction, the angle of a half sector represents a wind alignment error, the yaw alignment error is 180/N degrees, the value of N is 16, or an integer which can be divided by 360 when N is more than or equal to 12 and more than or equal to 24.
According to an embodiment of the invention, the selection of the maximum wind energy comprises:
wind energy calculation formula:
where ρ is the air density, C p In order to achieve the wind energy utilization rate, R is the radius of a wind wheel, v is the wind speed, theta is the included angle between the wind direction and the wind direction, lambda is the tip speed ratio of the blade, beta is the pitch angle of the blade, and n is the rotating speed of an impeller.
And calculating the wind energy obtained by each section according to a wind energy calculation formula, accumulating the total wind energy obtained by the yaw target, and selecting the yaw target capable of obtaining the maximum wind energy.
According to one embodiment of the invention, the historical wind condition data includes a minor-pitch average wind speed, a minor-pitch instantaneous maximum wind speed, a minor-pitch prevailing wind direction, a minor-pitch instantaneous maximum wind speed corresponding wind direction, a minor-pitch average air temperature, a minor-pitch instantaneous maximum air temperature, a minor-pitch average air pressure, and a minor-pitch instantaneous maximum air pressure.
According to one embodiment of the invention, the dangerous wind speed is a cut-out wind speed set by the construction of the fan, and the dangerous wind speeds of different fans are different; the 90-degree crosswind state refers to a state that the impeller yaws to be 90 degrees to the current dangerous wind direction; the limit twist cable is determined according to the length of the fan cable, and the impeller is generally used for calculating 720 degrees or 1080 degrees from a completely non-cable-wrapping position.
According to the yaw control method provided by the invention, the wind speed and the wind direction of a short period of time in the future are predicted, the yaw time of each subsection in the short period of the future is respectively calculated according to the predicted wind direction, the energy obtaining condition of the fan of each subsection in the short period of the future is calculated according to the predicted wind speed and the predicted wind direction, then the wasted wind energy and the wind energy obtained by the fan in the whole are calculated according to the yaw time and the energy obtaining condition of the fan, the yaw target capable of obtaining the maximum wind energy is selected, the selection of the optimal yaw target is realized, the wind energy utilization rate of a low wind speed area is improved, the condition of stopping the machine around a cable is reduced, and the generated energy of the fan is finally improved.
Compared with the prior art, the beneficial effects of this embodiment lie in:
the invention provides a yaw control method.A yaw main controller judges predicted wind condition data, selects a proper yaw target to yaw by comparing the yaw time of a certain section in the future with the section time, eliminates invalid yaw, reduces the starting and stopping times of yaw, and further reduces the unconvenient loss of a mechanical system; when the related yaw main controller processes the wind condition data, the wind condition data output by the prediction model is referred to instead of historical wind condition data, so that errors and hysteresis generated in the yaw process are reduced, and the wind energy is fully utilized; the yaw action and the pitch change action are carried out simultaneously, and the invalid yaw is eliminated, so that the starting and stopping times of the yaw are reduced, the loads of the blade root and the hub of the fan are reduced, and the service life of the fan is prolonged.
FIG. 3 schematically shows a block diagram of a yaw control system.
To achieve the above object, the present invention further provides a yaw control system, comprising:
the data acquisition module is used for acquiring various historical wind condition data of the wind driven generator and transmitting the various historical wind condition data to the main controller through communication, and the main controller divides the various historical wind condition data into sections according to set small time;
the model construction module is used for inputting the historical wind condition data divided according to the set time into a prediction model, and the prediction model is trained in the model according to the set learning criterion;
the data prediction module inputs real-time wind condition data into the trained prediction model and predicts wind condition data of a plurality of time period measures in the future;
the data analysis module is used for judging the wind condition data of a plurality of time periods in the future, calculating and selecting a yaw target capable of obtaining the wind energy with the maximum value, and issuing a yaw instruction;
and the yaw action module is used for identifying the yaw command and enabling the yaw motor to rotate correspondingly so as to realize the yaw control of the fan.
According to one embodiment of the invention, the data acquisition module functions to include: the wind measuring system collects various historical wind condition data and consists of an automatic data collecting part and a receiving part. The automatic data acquisition part consists of a wind direction and wind speed sensor (a wind direction and wind speed instrument), a data processor, a modem, a radio transceiver, a printer and a matched cable; the receiving part consists of computer, modem, matching cable, radio transceiver and printer. The data acquisition unit can show information such as wind speed, wind direction and can set up the parameter, and simultaneously the anemometry system can be connected with the computer and carry out wind condition data analysis and operation. The wind direction and wind speed sensor samples wind condition data, after various historical wind condition data are collected, the historical wind condition data are transmitted to the PLC main controller through RS485 communication, the PLC main controller divides the various historical wind condition data into sections according to set small section time, calculates the average wind speed, the maximum wind speed and the minimum wind speed of each section of wind speed, calculates the prevailing wind direction, the secondary prevailing wind direction and the wind direction corresponding to the maximum wind speed of the wind direction and the wind direction corresponding to the minimum wind speed, and calculates the average air temperature and the average air pressure.
According to an embodiment of the present invention, the prediction model in the model building module is specifically a BP neural network model, and specifically includes: the system comprises an input and output model, an action function model, an error calculation model and a self-learning model. The action function is a function reflecting the stimulation pulse intensity of the lower layer input to the upper layer node, is also called a stimulation function, and is generally taken as a continuous value Sigmoid function in (0,1): f (x) = 1/(1+e) ―x ) (ii) a The error calculation model is a function reflecting the magnitude of the error between the desired output and the calculated output of the neural network: e p ＝1/2×∑(t pi ―o pi ) 2 Where t is the expected output value of the node and O is the calculated output value of the node.
The basic principle of BP neural network model processing information includes: inputting various historical wind condition data physical quantities (including wind speed, wind direction, air temperature and air pressure) acquired and processed by the wind measuring system into a BP neural network model, and performing distributed storage and parallel cooperative processing on the various historical wind condition data physical quantities by the BP neural network model according to a set learning criterion; giving random values in the interval (0,1) to each connection weight value of the BP neural network model; inputting image modes corresponding to various historical wind condition data physical quantities into a BP neural network model, weighting and summing the input modes by the BP neural network model, comparing the input modes with a threshold, and performing nonlinear operation to obtain the output of a prediction model; in this case, the prediction model randomly outputs (0,1) the number of intervals, and if the output is the number corresponding to the specific bar of the historical wind condition data, the connection weight is increased, so that the prediction model can still make a corresponding judgment when encountering the specific bar of the historical wind condition data. After repeated learning and training, the network parameters (weight and threshold) corresponding to the minimum error are determined, and the training is stopped. At the moment, the trained neural network can process and output the information which is subjected to nonlinear conversion and has the minimum error to the input information of similar samples.
According to one embodiment of the invention, the data prediction module functions to: processed various historical wind condition data D n (including the average wind speed v n Prevailing wind direction d n Etc.) are input into a prediction model for training, and the output is the average wind speed v of the future k interval later measures n+k And prevailing wind direction d n+k . After the prediction of k measures, there is an already predicted average wind speed v of k measures in the future at the n + k measure n+k And prevailing wind direction d n+k ，d n+k+1 …，d n+2k . Then the average wind speed and prevailing wind direction for the k future quarters of each quarter are predicted.
According to one embodiment of the invention, the data analysis module functions include: as shown in FIG. 2, at the current section p, the yaw system first calculates the required yaw time for the next section to be the yaw target, if the yaw time exceeds a section time t 0 And if the next subsection is in the yaw state all the time, the next subsection is selected as the yaw target. Continuously recurrently moving to the nth section of the future according to the predicted prevailing wind direction, calculating the required yaw time T (p, p + n) taking the nth section of the future as a yaw target, and accumulatively calculating the yaw time from the current p to each section of the future
If T (p)P + n) is less than n bar times nt 0 ，
Greater than n bar times nt 0 And determining that the n-th section is taken as a yaw target to reduce the waste of wind energy. When the calculation is carried out until the farthest predicted section k is reached, if a proper yaw target is not found, the wind energy predicted at present for a future period of time has to be wasted, and the yaw action in the direction opposite to the cable winding direction is considered. When a plurality of suitable yaw targets are obtained after recursive calculation, the wind energy which can be obtained by the wind turbine as a whole needs to be calculated according to the predicted average wind speed, and the yaw target which obtains the maximum wind energy is selected, as shown in fig. 2.
According to an embodiment of the invention, the action of the yaw action module comprises: and the yaw action module is used for giving a yaw instruction and controlling the yaw motor of the fan to rotate after the predicted data are judged by the yaw main controller and a proper yaw target is selected.
According to one embodiment of the invention, the historical wind condition data are collected by a wind measuring system and transmitted to the main controller through communication, and the main controller divides the historical wind condition data into sections according to a set small time.
According to one embodiment of the invention, the training of the predictive model comprises: the prediction model performs distributed storage and parallel cooperative processing on various historical wind condition data acquired by the wind measuring system according to a set learning criterion; giving random values in the interval (0,1) to each connection weight value of the prediction model; inputting image modes corresponding to various historical wind condition data into a prediction model, weighting and summing the input modes by the prediction model, comparing the input modes with a threshold, and performing nonlinear operation to obtain the output of the prediction model; in this case, the prediction model randomly outputs (0,1) the number of intervals, and if the output is the number corresponding to the specific measure of the historical wind condition data, the connection weight will be increased, so that the prediction model can still make a corresponding determination when encountering the specific measure of the wind condition data again.
According to an embodiment of the invention, the selecting of the yaw target comprises: the yaw main controller takes the predicted wind direction as a yaw target, calculates the difference value between the current dead angle of the fan and the yaw target angle, actively judges the yaw direction, and calculates the yaw time required by the current yaw target according to the yaw speed of the fan; in the current subsection, the yaw main controller respectively judges each subsection which follows the current subsection in turn as a yaw target; when the required yaw time for taking the nth section in the future as a yaw target is less than the total time of the n sections, and the yaw time for each section from the current section to the future is greater than the total time of the n sections, determining that taking the nth section as the yaw target can reduce the waste of wind energy; if the yaw target cannot be found in a plurality of sections of time periods in the future in a recursion manner, the wind energy of the sections of the time periods in the future of the current section is considered to be wasted, and at the moment, the fan performs yaw action in the direction opposite to the cable winding direction; when a plurality of yaw targets appear in a plurality of time period subsections in the future, the yaw main controller respectively obtains action plans of the fan in the plurality of time period subsections in the future through wind condition data of the plurality of time period subsections in the future, calculates wind energy obtained by each subsection, accumulates total wind energy obtained by the yaw target according to the wind energy obtained by each subsection, and selects the yaw target capable of obtaining the maximum wind energy, as shown in fig. 2.
According to one embodiment of the invention, when the yaw main controller detects that the wind speed exceeds the dangerous wind speed for 2 times continuously, the yaw main controller automatically issues a stop instruction and a 90-degree crosswind yaw instruction, and the fan is stopped and turned to a 90-degree crosswind state in a yaw mode; when the condition of the limit cable twisting occurs, a shutdown automatic cable untwisting yaw instruction is given.
According to one embodiment of the invention, the wind direction is determined as follows: the wind direction is divided into N sectors according to angles, each sector is 360/N degrees, the central angle of the sector represents the wind direction, the half sector angle represents the wind alignment error, the yaw alignment error is 180/N degrees, the value of N is 16, or an integer which can be divided by 360 between 12 and more than or equal to N and more than or equal to 24.
According to an embodiment of the invention, the selection of the maximum wind energy comprises:
wind energy calculation formula:
where ρ is the air density, C p In order to achieve the wind energy utilization rate, R is the radius of a wind wheel, v is the wind speed, theta is the included angle between the wind direction and the wind direction, lambda is the blade tip speed ratio of the blade, beta is the blade pitch angle, and n is the rotating speed of an impeller.
And calculating the wind energy obtained by each section according to a wind energy calculation formula, accumulating the total wind energy obtained by the yaw target, and selecting the yaw target capable of obtaining the maximum wind energy.
According to one embodiment of the invention, the historical wind condition data includes a minor-pitch average wind speed, a minor-pitch instantaneous maximum wind speed, a minor-pitch prevailing wind direction, a minor-pitch instantaneous maximum wind speed corresponding wind direction, a minor-pitch average air temperature, a minor-pitch instantaneous maximum air temperature, a minor-pitch average air pressure, and a minor-pitch instantaneous maximum air pressure.
According to one embodiment of the invention, the dangerous wind speed is a cut-out wind speed set by the construction of the fan, and the dangerous wind speeds of different fans are different; the 90-degree crosswind state refers to a state that the impeller yaws to be 90 degrees to the current dangerous wind direction; the limit twist cable is determined according to the length of the fan cable, and the impeller is generally used for calculating 720 degrees or 1080 degrees from a completely non-cable-wrapping position.
According to the yaw control system provided by the invention, the wind speed and the wind direction of a short period of time in the future are predicted, the yaw time of each subsection in the short period of the future is respectively calculated according to the predicted wind direction, the fan energy obtaining situation of each subsection in the short period of the future is calculated according to the predicted wind speed and the predicted wind direction, then the wasted wind energy and the wind energy obtained by the fan in the whole future are calculated according to the yaw time and the fan energy obtaining situation, the yaw target capable of obtaining the maximum wind energy is selected, the selection of the optimal yaw target is realized, the wind energy utilization rate is improved, the cable winding stop situation is reduced, and the generated energy of the fan is finally improved.
Compared with the prior art, the beneficial effects of this embodiment lie in:
the invention provides a yaw control system.A yaw main controller judges predicted wind condition data, selects a proper yaw target to yaw by comparing the yaw time of a certain section in the future with the section time, eliminates invalid yaw, reduces the starting and stopping times of yaw, and further reduces the unconvenient loss of a mechanical system; when the related yaw main controller processes the wind condition data, the wind condition data output by the prediction model is referred to instead of historical wind condition data, so that errors and hysteresis generated in the yaw process are reduced, and the wind energy is fully utilized; the yaw action and the pitch change action are carried out simultaneously, and the invalid yaw is eliminated, so that the starting and stopping times of the yaw are reduced, the loads of the blade root and the hub of the fan are reduced, and the service life of the fan is prolonged.
To achieve the above object, the present invention further provides an electronic device, which includes a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the electronic device implements the above yaw control method.
To achieve the above object, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a yaw control method as described above.
Those of ordinary skill in the art will appreciate that the modules and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and devices may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, each functional module in the embodiments of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several 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 for transmitting/receiving the power saving signal according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
It should be understood that, the serial numbers of the steps in the summary and the embodiments of the present invention do not absolutely imply the sequence of execution, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Claims (9)
1. A yaw control method, characterized by comprising the steps of:
collecting various historical wind condition data of the wind driven generator;
training a prediction model by using the historical wind condition data;
inputting real-time wind condition data into the prediction model, and outputting wind condition data of a plurality of sections of time periods in the future;
judging the wind condition data of the sections of the plurality of time periods in the future, calculating and selecting a yaw target capable of obtaining the wind energy with the maximum value, and issuing a yaw instruction;
identifying the yaw command, and enabling a yaw motor to rotate correspondingly to realize the yaw control of the fan;
the prediction model performs distributed storage and parallel cooperative processing on the various historical wind condition data acquired by the wind measuring system according to a set learning criterion;
giving a random value in an interval of (0,1) to each connection weight value of the prediction model;
inputting image modes corresponding to the historical wind condition data into the prediction model, performing weighted summation and threshold comparison on the input modes by the prediction model, and performing nonlinear operation to obtain the output of the prediction model;
in this case, the prediction model randomly outputs (0,1) the number of the intervals, and if the output is the number corresponding to the specific bar of the historical wind condition data, the connection weight will be increased, so that when the prediction model meets the wind condition data of the specific bar again, the corresponding determination can still be made.
2. The yaw control method of claim 1, characterized in that: and the historical wind condition data are collected by a wind measuring system and transmitted to a main controller through communication, and the main controller divides the historical wind condition data into sections according to set small time.
3. The yaw control method of claim 1, wherein the selecting of the yaw target comprises:
the yaw main controller takes the predicted wind direction as the yaw target, calculates the difference value between the current just-facing angle of the fan and the yaw target angle, actively judges the yaw direction, and calculates the yaw time required by the current yaw target according to the yaw speed of the fan;
in the current subsection, the yaw main controller respectively judges each subsection which follows the current subsection in turn as the yaw target;
when the yaw time required for the yaw target at the nth section in the future is less than the total time of the n sections, and the yaw time of each section from the current section to the future is greater than the total time of the n sections, determining that the yaw target at the nth section can reduce the waste of wind energy;
if the yaw target cannot be found by recursion to the future multiple time period sections, the wind energy of the future multiple time period sections of the current section is considered to be wasted, and at the moment, the wind turbine performs yaw action in the direction opposite to the cable winding direction;
when a plurality of yaw targets appear in the plurality of time period subsections in the future, the yaw main controller respectively obtains action plans of the fan in the plurality of time period subsections in the future through the wind condition data of the plurality of time period subsections in the future, calculates wind energy obtained by each subsection, accumulates total wind energy obtained by the yaw target according to the wind energy obtained by each subsection, and selects the yaw target capable of obtaining the maximum wind energy.
4. The yaw control method of claim 3, characterized in that: when the yaw main controller detects that the wind speed exceeds the dangerous wind speed for 2 times continuously, the yaw main controller automatically issues a stop instruction and a 90-degree crosswind yaw instruction, and the fan is stopped and changes to a 90-degree crosswind state in a yaw mode; when the condition of the limit cable twisting occurs, a shutdown automatic cable untwisting yaw instruction is given.
5. The yaw control method of claim 4, wherein the wind direction is determined as follows:
the wind direction is divided into N sectors according to angles, each sector is 360/N degrees, the central angle of each sector represents the wind direction, the angle of a half sector represents a wind alignment error, the yaw alignment error is 180/N degrees, the value of N is 16, or an integer which can be divided by 360 when N is more than or equal to 12 and more than or equal to 24.
6. The yaw control method of claim 4, wherein the selection of the maximum wind energy comprises:
wind energy calculation formula:
wherein, the first and the second end of the pipe are connected with each other,
in order to be the density of the air,
in order to achieve the utilization rate of wind energy,
is the radius of the wind wheel,
which is the wind speed,
is an included angle between the wind direction and the wind direction,
the speed ratio of the blade tip is the speed ratio of the blade tip,
for the blade pitch angle, the pitch angle,
is the impeller rotational speed;
and calculating the wind energy obtained by each section according to the wind energy calculation formula, thereby accumulating the total wind energy obtained by the yaw target, and selecting the yaw target capable of obtaining the maximum wind energy.
7. A yaw control system, comprising:
the data acquisition module is used for acquiring various historical wind condition data of the wind driven generator and transmitting the various historical wind condition data to the main controller through communication, and the main controller divides the various historical wind condition data into sections according to set small time;
the model construction module is used for inputting the historical wind condition data divided according to the set time into a prediction model, and the prediction model is trained in the model according to the set learning criterion;
the data prediction module inputs real-time wind condition data into the trained prediction model and predicts wind condition data of a plurality of time period measures in the future;
the data analysis module is used for judging the wind condition data of a plurality of time periods in the future, calculating and selecting a yaw target capable of obtaining the wind energy with the maximum value, and issuing a yaw instruction;
and the yaw action module is used for identifying the yaw command, and the yaw motor makes corresponding rotation to realize the yaw control of the fan in the low wind speed area.
8. An electronic device, characterized in that: comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing a yaw control method according to any one of claims 1 to 6.
9. A storage medium, characterized by: the storage medium having stored thereon a computer program which, when executed by a processor, implements a yaw control method as claimed in any one of claims 1 to 6.
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