CN116677560A - Dynamic optimization method, system, terminal and medium for yaw control of wind turbine generator - Google Patents
Dynamic optimization method, system, terminal and medium for yaw control of wind turbine generator 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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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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
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Abstract
The invention relates to the technical field of wind turbine generator control, and discloses a dynamic optimization method, a system, a terminal and a medium for yaw control of a wind turbine generator. According to the invention, yaw controller parameter updating iteration can be carried out in a certain period according to the actual running environment of the wind turbine generator; the optimization solving process is deployed in the wind farm to realize periodic automatic starting calculation, so that the yaw control optimization can acquire historical operation data in a short time at regular intervals, the optimization result is updated and iterated, and the self-adaptability of the yaw control of the unit is greatly improved.
Description
Technical Field
The invention relates to the technical field of wind turbine generator control, in particular to a dynamic optimization method, a dynamic optimization system, a dynamic optimization terminal and a dynamic optimization medium for yaw control of a wind turbine generator.
Background
Although wind resources in nature are continuous, they have volatility and randomness. Therefore, a certain control is required to be applied to the dispatching of the fans and the wind fields, so that the power generation efficiency of the unit is improved, and the efficient utilization of wind resources is realized. The control of the wind turbine aims at improving the single-machine performance of the wind turbine, and comprises yaw control of the current maximum wind energy conversion efficiency. Typically, yaw control of a wind turbine is controlled by a set of specific control parameters, namely a yaw control threshold and a yaw control delay time. The control principle is that when the deviation angle of the inflow wind direction and the current machine set cabin position is larger than the yaw control threshold value and the duration exceeds the yaw control delay time, the wind turbine set starts yaw control to perform yaw opposite wind, and the yaw control strategy of most wind turbine sets in the current market is also adopted. The yaw control of the wind turbine generator is typical open loop control, once the yaw error of the wind turbine generator meets the starting condition of the yaw control, after the yaw controller gives an instruction to the yaw execution system, the yaw execution motor starts to execute actions until the yaw control stops after the yaw control stops, and the main control system cannot influence the yaw execution process, so that the yaw control does not have a dynamic adjustment process. The yaw control strategy of the wind turbine generator set has the defects of poor control precision, poor robustness and poor self-adaptability, and the defects are key research objects of yaw control optimization.
The wind turbine generator yaw control system has various optimization methods, and the final optimization aim is to improve the generating capacity by improving the accuracy of the wind turbine generator to wind. In a common yaw control optimization method, for example, a yaw correction method, the accuracy of wind speed and wind direction measurement is improved by replacing a high-precision wind condition measurement device, a cabin type laser radar and the like. Meanwhile, according to historical operation data of the wind turbine, the inherent yaw error of the wind turbine is calculated through excavation, the yaw error is corrected, and the wind accuracy is improved; secondly, the wind direction prediction is introduced into yaw control optimization, so that the wind direction prediction is an effective optimization method, the advanced yaw is realized, and the power generation loss caused by yaw error can be reduced; in addition, according to different wind speed intervals, the yaw control parameters, namely the yaw threshold value and the yaw delay time, are optimized and set, so that the yaw wind efficiency of the unit can be improved to a certain extent, and the wind rejection rate of the unit is reduced.
In the method, although effective, the yaw correction method by using the high-precision wind meter devices such as the laser radar is generally expensive, and the installation of the wind meter devices such as the laser radar on each fan of the wind power plant is difficult to realize, so that most cases only select a typical unit to perform optimization correction, and then the method is applied to other units. On the one hand, the correction value can have low adaptability on other units, so that the yaw correction effect is poor and even a negative effect is generated; on the other hand, correction based on historical data is usually performed by using running data of a unit for a period of time to perform yaw error mining, and correction values are not updated and iterated, which can lead to the reduction of control accuracy of the optimization method on a longer time scale. The yaw control optimization of wind speed and direction prediction is introduced, and the control effect is greatly influenced by the wind speed and direction prediction accuracy, but the current wind speed and direction prediction technology cannot make high-accuracy prediction in a specific range, so that the yaw optimization method stays on a theoretical level more, and is difficult to apply in actual engineering. The yaw control of the wind turbine generator is influenced by the complexity and variability of the inflow wind during actual running of the wind turbine generator, so that the yaw control has the defect of poor self-adaptability, and the yaw control parameters set during the exiting of the wind turbine generator have different control effects in different running environments due to the fact that the wind conditions of different regions are greatly different, so that the yaw control of the wind turbine generator is optimized in a targeted mode.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a dynamic optimization method, a system, a terminal and a medium for yaw control of a wind turbine generator, which are used for solving the technical problems that in the prior art, optimization correction cannot be carried out on each fan, adaptability is low, yaw correction effect is poor, errors exist in historical data correction, and correction accuracy is affected.
The invention is realized by the following technical scheme:
a dynamic optimization method for yaw control of a wind turbine generator comprises the following steps:
step 1, a linearization model of a wind turbine generator is established, a space expression of a linearization state of the wind turbine generator is obtained according to structural parameters of the wind turbine generator, wind speed, pitch angle and rated torque of a generator are used as inputs, and power and torque of the generator are used as outputs;
step 2, fitting a coefficient of wind direction deviation on power influence through a wind turbine linearization model, and correcting power output by the wind turbine linearization model;
step 3, calculating the yaw control process and power output of the wind turbine generator set under the wind condition input according to the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
step 4, establishing a multi-objective optimization solution problem which is mostly used for improving the equivalent power generation amount and limiting the great increase of the yaw execution times, constructing a minimum value optimization problem, and searching for a pareto optimal solution model by utilizing a pareto optimal theory;
And 5, storing a plurality of pareto optimal solution models in a server, wherein each pareto optimal solution model corresponds to one wind turbine generator of a wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and a new delay time calculated by an optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing the dynamic optimization work of yaw control of the wind turbine generator.
Preferably, in step 1, a unit linearization state space expression is obtained according to structural parameters of the wind turbine, and the formula is as follows:
wherein A is a system state coefficient matrix, B is a system control coefficient matrix, C is an output state coefficient matrix, D is an output control coefficient matrix,x, u and y are vectors, wherein u is an input vector comprising wind speed, rated torque and pitch angle, and y is an output vector comprising power and generator rotation speed; x and x are the current time state and the next time state of the system respectively.
Preferably, in step 2, the wind turbine generator linearization model fits the coefficient of wind direction deviation on power influence, and the specific process is as follows:
Setting the wind energy capturing efficiency of the wind turbine generator set as P and the yaw error angle of the wind turbine generator set as theta, and adopting the following formula of the wind energy capturing efficiency:
wherein: p is wind energy captured by the unit, unit: w is a metal; ρ is the air density in units: kg/m3; r is the radius of the impeller of the wind turbine, and the unit is: m; v is the inflow wind speed in units of: m/s; θ is yaw error angle, unit: rad; n is a coefficient to be determined;
wherein, the energy loss generated in the process of converting wind energy captured by the wind turbine generator into active power is recorded as P Damage to The real active power of the unit is: p (P) Active power ＝P-P Damage to ；
Wherein, when yaw error is θ:
P active power ＝P 0 ·Cos n θ
Wherein P is 0 The yaw error is the active power of the wind turbine generator set when the yaw error is 0 degrees;
and (3) fitting a curve according to the yaw error sequence and the active power recorded in the collected wind turbine scada by adopting a least square method or a Fourier series approximation method, and determining the value of the undetermined coefficient n.
Preferably, in step 3, a yaw control algorithm is written by using python or c++, and a yaw control process and power output of the wind turbine set under the wind condition input are calculated for the power output by the corrected wind turbine set linearization model, wherein the yaw control process is represented by an absolute azimuth angle of a cabin of the wind turbine set and a yaw zone bit; the yaw zone bit is a series of digital signals, when the yaw motor is started, the yaw zone bit is set to be 0, and when the yaw motor executes yaw control action, the yaw zone bit is set to be 1; and counting the yaw execution times in the time period by counting the times of triggering the yaw zone bit.
Preferably, in step 4, a minimum value optimization problem formula is constructed as follows:
wherein f 1 、f 2 Representing the mapping relation between the negative equivalent power generation amount and the yaw execution times relative to the threshold value and the delay time; x is a controller parameter, namely a threshold value and a delay time; x represents the range of threshold and delay time, the threshold takes 5-20 degrees, the delay time takes 20s-210s, natural numbers are taken, and the minimum value is optimizedThe maximum value of the yaw control execution times is set as a boundary condition in the questions.
Further, in step 4, a multi-objective optimization solution problem is established to promote the equivalent power generation amount and limit the yaw execution times to be greatly increased, and the pareto optimal solution is sought by utilizing the pareto optimal theory, and the specific process is as follows:
based on the solution corresponding to the initial value yaw threshold and the delay time, optimizing by utilizing a genetic algorithm, and calculating the yaw threshold and the delay time corresponding to the optimal solution on the pareto front edge to be compared with the initial value; if the solution on the front edge is better than the initial solution, selecting the middle point on the front edge as the optimized solution to perform optimization iteration of the threshold value and the delay time, and if the initial value is also positioned on the front edge, not updating, so as to complete the optimization solution of one period; and finally packaging all algorithms.
Preferably, in step 5, the specific process of updating the new threshold and delay time calculated by the optimization solution algorithm to the master control system of the corresponding unit is as follows:
setting the data optimization period as x hours, acquiring wind speed, wind direction, power and yaw execution process data in x hours every x hours, taking the current threshold value, delay time and wind condition information as input, starting an optimization algorithm corresponding to the unit in a server, solving a new optimal value and current value comparison, and if the new optimal value is superior to the current value, iteratively writing the new optimal value and the new optimal value into a main control system by using the new threshold value and the new delay time.
A wind turbine yaw control dynamic optimization system, comprising:
the model building module is used for building a wind turbine generator linearization model, obtaining a space expression of a turbine generator linearization state according to structural parameters of the wind turbine generator, and taking wind speed, pitch angle and rated torque of a generator as input and power and torque of the generator as output;
the model correction module is used for fitting a coefficient of wind direction deviation to power influence through the wind turbine linearization model and correcting power output by the wind turbine linearization model;
the first data processing module is used for calculating the yaw control process and power output of the wind turbine generator set under the wind condition input for the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
The second data processing module is used for establishing a multi-objective optimization solution problem which is used for improving the equivalent power generation amount and limiting the yaw execution times to be greatly increased, constructing a minimum value optimization problem, and searching for a pareto optimal solution by utilizing the pareto optimal theory to complete the dynamic optimization work of the yaw control of the wind turbine.
The third data processing module is used for storing a plurality of pareto optimal solution models in the server, wherein each pareto optimal solution model corresponds to one wind turbine generator of the wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and delay time calculated by the optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing dynamic optimization work of yaw control of the wind turbine generator.
A mobile terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of a wind turbine yaw control dynamic optimization method as described above when executing the computer program.
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of a wind turbine yaw control dynamic optimization method as described above.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a dynamic optimization method for yaw control of a wind turbine, which is characterized in that according to historical operation data of the wind turbine, an intelligent algorithm is used for optimizing, and a yaw threshold value and a delay time are updated and iterated in a fixed period, so that a yaw control process with higher precision is realized, the cost of optimizing and reforming is reduced, and the adaptability of yaw control is improved. According to the invention, yaw controller parameter updating iteration can be carried out in a certain period according to the actual running environment of the wind turbine generator; the optimization solving process is deployed in the wind farm to realize periodic automatic starting calculation, so that the yaw control optimization can acquire historical operation data in a short time at regular intervals, the optimization result is updated and iterated, and the self-adaptability of the yaw control of the unit is greatly improved.
The invention also provides a dynamic optimizing system for yaw control of the wind turbine, wherein a master control algorithm of the wind turbine is generally deployed in a master control plc of the wind turbine, and the language which can be used is very limited.
The invention also provides a mobile terminal, and a dynamic optimization method for yaw control of the corresponding wind turbine is realized aiming at different wind turbine computer programs when the computer programs are executed by a processor. Each computer program is independent, and yaw control parameters of each wind turbine generator are optimized in parallel without mutual influence. The same control parameters are generally used among different units in yaw control, but the invention can realize that different units use different yaw control parameters, and realize more ideal control effects.
Drawings
FIG. 1 is a flow chart of a dynamic optimization method for yaw control of a wind turbine generator in the present invention
FIG. 2 is a flow chart of a dynamic optimization method for yaw control of a wind turbine generator system in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a wind turbine generator system according to an embodiment of the present invention;
FIG. 4 is a graph showing yaw control parameters versus yaw rate in an embodiment of the present invention;
FIG. 5 is a diagram of the pareto front in an embodiment of the invention;
FIG. 6 is a diagram of a process for building a yaw control optimization solution model in an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
the invention aims to provide a dynamic optimization method, a dynamic optimization system, a dynamic optimization terminal and a dynamic optimization medium for yaw control of a wind turbine, which are used for solving the technical problems that in the prior art, optimization correction cannot be performed on each fan, adaptability is not high, yaw correction effect is not good, errors exist in historical data correction, and correction accuracy is affected.
Specifically, according to the method for dynamically optimizing yaw control of the wind turbine, as shown in fig. 1, the method comprises the following steps:
step 1, a linearization model of a wind turbine generator is established, a space expression of a linearization state of the wind turbine generator is obtained according to structural parameters of the wind turbine generator, wind speed, pitch angle and rated torque of a generator are used as inputs, and power and torque of the generator are used as outputs;
specifically, a unit linearization state space expression is obtained according to structural parameters of the wind turbine, and the formula is as follows:
wherein A is a system state coefficient matrix, B is a system control coefficient matrix, C is an output state coefficient matrix, D is an output control coefficient matrix,x, u and y are vectors, wherein u is an input vector comprising wind speed, rated torque and pitch angle, and y is an output vector comprising power and generator rotation speed; x and- >The current time state and the next time state of the system are respectively.
Step 2, fitting a coefficient of wind direction deviation on power influence through a wind turbine linearization model, and correcting power output by the wind turbine linearization model;
specifically, the wind turbine generator linearization model fits the coefficient of wind direction deviation on power influence, and the specific process is as follows:
setting the wind energy capturing efficiency of the wind turbine generator set as P and the yaw error angle of the wind turbine generator set as theta, and adopting the following formula of the wind energy capturing efficiency:
wherein: p is wind energy captured by the unit, unit: w is a metal; ρ is the air density in units: kg/m3; r is the radius of the impeller of the wind turbine, and the unit is: m; v is the inflow wind speed in units of: m/s; θ is yaw error angle, unit: rad; n is a coefficient of uncertainty.
Wherein, the energy loss generated in the process of converting wind energy captured by the wind turbine generator into active power is recorded as P Damage to The real active power of the unit is: p (P) Active power ＝P-P Damage to ；
Wherein, when yaw error is θ:
P active power ＝P 0 ·cos n θ
Wherein P is 0 The yaw error is the active power of the wind turbine generator set when the yaw error is 0 degrees;
and (3) fitting a curve according to the yaw error sequence and the active power recorded in the collected wind turbine scada by adopting a least square method or a Fourier series approximation method, and determining the value of the undetermined coefficient n.
Step 3, calculating the yaw control process and power output of the wind turbine generator set under the wind condition input according to the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
specifically, a yaw control algorithm is written by using python or C++, and the yaw control process and the power output of the wind turbine generator under the wind condition input are calculated for the power output by the corrected wind turbine generator linearization model, wherein the yaw control process is represented by the absolute azimuth angle of a cabin of the wind turbine generator and a yaw zone bit; the yaw zone bit is a series of digital signals, when the yaw motor is started, the yaw zone bit is set to be 0, and when the yaw motor executes yaw control action, the yaw zone bit is set to be 1; and counting the yaw execution times in the time period by counting the times of triggering the yaw zone bit.
And 4, establishing a multi-objective optimization solution problem which is used for improving the equivalent power generation amount and limiting the yaw execution times to be greatly increased, constructing a minimum value optimization problem, and searching for a pareto optimal solution by utilizing a pareto optimal theory to complete the dynamic optimization work of the yaw control of the wind turbine generator.
Specifically, the formula for constructing the minimum optimization problem is as follows:
wherein f 1 、f 2 Representing the mapping relation between the negative equivalent power generation amount and the yaw execution times relative to the threshold value and the delay time; x is a controller parameter, namely a threshold value and a delay time; x represents the range of the threshold value and the delay time, the threshold value is 5-20 degrees, the delay time is 20s-210s, and natural numbers are taken.
Wherein, the maximum value of the execution times of yaw control is set as a boundary condition in the minimum value optimization problem.
Specifically, a multi-objective optimization solution problem which is mostly used for improving the equivalent power generation amount and limiting the great increase of the yaw execution times is established, and the pareto optimal solution is sought by utilizing the pareto optimal theory, and the specific process is as follows:
based on the solution corresponding to the initial value yaw threshold and the delay time, optimizing by utilizing a genetic algorithm, and calculating the yaw threshold and the delay time corresponding to the optimal solution on the pareto front edge to compare with the initial value. If the solution on the front edge is better than the initial solution, selecting the middle point on the front edge as the optimized solution to perform optimization iteration of the threshold value and the delay time, and if the initial value is also positioned on the front edge, not updating, so as to complete the optimization solution of one period; and finally packaging all algorithms.
And 5, storing a plurality of pareto optimal solution models in a server, wherein each pareto optimal solution model corresponds to one wind turbine generator of a wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and a new delay time calculated by an optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing the dynamic optimization work of yaw control of the wind turbine generator.
The specific process of updating the new threshold value and the delay time calculated by the optimization solving algorithm to the main control system of the corresponding unit is as follows:
setting the data optimization period as x hours, acquiring wind speed, wind direction, power and yaw execution process data in x hours every x hours, taking the current threshold value, delay time and wind condition information as input, starting an optimization algorithm corresponding to the unit in a server, solving a new optimal value and current value comparison, and if the new optimal value is superior to the current value, iteratively writing the new optimal value and the new optimal value into a main control system by using the new threshold value and the new delay time.
Examples
According to the yaw control optimization method based on the dynamic adjustment of the yaw control threshold value and the yaw control delay time, the dynamic optimization method is realized, a multi-objective optimization problem solving model of the yaw control dynamic optimization is firstly required to be established, the model is packaged into a server, the execution period is set, and the optimization is executed every 3 hours according to the method shown in fig. 2 and 6. The detailed steps are as follows:
step one: and (3) establishing a linearization model of the wind turbine, as shown in fig. 3. The wind speed, the pitch angle and the rated torque of the generator are used as inputs, the power and the torque of the generator are used as outputs, and a linear state space expression of the wind turbine is established according to structural parameters and the like of the wind turbine:
Wherein A is a system state coefficient matrix, B is a system control coefficient matrix, C is an output state coefficient matrix, and D is an output control coefficient matrix.
Step two: and fitting a coefficient of influence of wind direction deviation on power on the basis of a linearization model of the wind turbine. Assuming that the wind energy capturing efficiency of the unit is P, at a certain moment, the yaw error angle of the unit is θ, then:
wherein: p is wind energy captured by the unit, unit: w is a metal;
ρ is the air density in units: kg/m 3 ；
R is the radius of the impeller of the wind turbine, and the unit is: m;
v is the inflow wind speed in units of: m/s;
θ is yaw error angle, unit: rad;
n is a coefficient of uncertainty.
The energy loss generated in the process of converting wind energy captured by the wind turbine generator into active power is recorded as P Damage to The real active power P of the unit Active power ＝P-P Damage to . When the yaw error is θ:
P active power ＝P 0 ·cos n θ
Wherein P is 0 The yaw error is the active power of the unit when the yaw error is 0 degrees.
And (3) fitting a curve according to the yaw error sequence and the active power recorded in the collected wind turbine scada by adopting a least square method or a Fourier series approximation method, and determining the value of the undetermined coefficient n.
Step three: and (5) linearizing the model correction. Because the original unit linearization model does not comprise the input of a yaw error angle, the active power output by the model is corrected when the included angle part 0 between the inflow wind direction angle and the cabin position is included according to the second step.
Step four: yaw control algorithms are written using python or c++. The initial control algorithm is provided with a threshold value and delay time as parameter values in the original control strategy of the unit. In the control algorithm programming, the unit model in the third step is required to be programmed in a discretization mode, and the algorithm can calculate the yaw control process and the active power output of the unit under the wind condition input after a wind speed and wind direction sequence of a section is given. Wherein the yaw control process is represented by the absolute azimuth angle (relative to north) of the nacelle of the aggregate and the yaw flag. The yaw zone bit is a series of digital signals, when the yaw motor is started, the zone bit is set to be 0, and when the yaw motor executes yaw control action, the yaw zone bit is set to be 1. Finally, the algorithm counts the yaw execution times in the period by counting the number of times the yaw flag bit is triggered, as shown in fig. 4.
Step five: and establishing a multi-objective optimization solving problem. In the yaw control dynamic optimization method provided by the invention, the lifting power generation amount is not the only control target. According to the simulation experiment result of the yaw control algorithm, the yaw control times can be exponentially increased when the threshold value and the delay time are set to be too small, and the method is very unfavorable for healthy operation of the unit.
Constructing a minimum value optimization problem:
wherein f1 and f2 represent the mapping relation between the negative equivalent power generation amount and the yaw execution times relative to the threshold value and the delay time, and x is a controller parameter, namely the threshold value and the delay time. X represents the range of the threshold value and the delay time, the threshold value is 5-20 degrees, the delay time is 20s-210s, and natural numbers are taken. The maximum value of the yaw control execution times is set as a boundary condition in the optimization problem.
Step six: according to the pareto optimization theory, the optimal solution of the multi-objective optimization problem in the fifth step is often not unique, and the pareto optimal theory provides a solution idea of the optimal solution. Taking the minimum multi-objective optimization problem as an example according to the pareto optimal theory, the pareto fronts exist in the feasible domain of the solution, and the solutions of the pareto fronts can be called as the pareto optimal solution, as shown in fig. 5.
Aiming at the multi-objective optimization problem proposed in the step five, based on the solution corresponding to the initial value yaw threshold and the delay time, the yaw threshold and the delay time corresponding to the optimal solution on the pareto front edge are calculated by utilizing genetic algorithm for optimization, and are compared with the initial value. If the solution on the front edge is better than the initial solution, selecting the middle point on the front edge as the optimized solution to perform optimization iteration of the threshold value and the delay time, and if the initial value is also positioned on the front edge, not updating, thus completing the optimization solution of one period. And finally packaging all algorithms.
Step seven: a special yaw control dynamic optimization solving server is installed at a wind farm station, a plurality of optimization solving models constructed in the step six are stored in the server, independence among the models is guaranteed, each model corresponds to one unit, and optimization calculation among the models adopts a parallel mode.
The model takes wind speed and direction and current controller parameters as inputs, and outputs optimized controller parameters. The set optimization algorithm is performed once every 3 hours for one cycle. In addition, it should be noted that if the current unit is in the yaw control process when the optimization problem of the next cycle needs to be started after 3 hours, the optimization algorithm is not executed temporarily, and the optimization algorithm is executed after waiting 1 minute after the current yaw motion is completed.
Step eight: and establishing communication between the optimization solving server and the SCADA database of the wind power plant and between the optimization solving server and the master control of the wind power generation set, ensuring that the server can acquire historical data in the SCADA database, and updating a new threshold value and a new delay time calculated by the optimization solving algorithm into a master control system of the corresponding wind power generation set.
Step nine: the unit collects wind speed, wind direction, power and yaw execution process data in 3 hours every 3 hours, takes current threshold value, delay time and wind condition information as input, starts an optimization algorithm corresponding to the unit in a server, solves new optimal value and current value comparison, and if the new optimal value is superior to the current value, the new optimal value and the new optimal value are iteratively written into the main control by the new threshold value and the new optimal value.
The invention provides a yaw control dynamic optimization method capable of carrying out controller parameter updating iteration in a certain period according to the actual running environment of a wind turbine generator set on the basis of a common yaw control optimization method, and on-site deployment is realized through optimization problem modeling and optimization algorithm writing and encapsulation. The technical key points and the points to be protected of the invention are as follows:
(1) Dynamic optimization techniques for yaw controller parameters. In the existing yaw control optimization method, the original yaw control strategy is optimized or corrected once by analyzing problems existing in the current yaw control of the unit or introducing new prediction technology. Although the optimization method can obtain good optimization effects, the running environment of the wind turbine generator is complex and changeable, and the optimization result based on a certain set of historical running data can obtain ideal effects, but the optimization results are not optimal from the aspect of longer time scale. According to the dynamic optimization method provided by the invention, the periodic automatic starting calculation is realized by deploying the optimization solving process in the wind farm, so that the historical operation data in a short time can be collected at regular intervals in yaw control optimization, the optimization result is updated and iterated, and the self-adaptability of the yaw control of the unit is greatly improved.
(2) Packaging and field deployment of a yaw control optimization algorithm. The main control algorithm of the wind turbine generator is generally deployed in the main control plc of the wind turbine generator, and the available languages are very limited. According to the invention, the optimization solving algorithm is deployed into the high-performance server, so that the optimization problem can be solved by using higher-level languages such as Python, C++, and the like, high-speed operation can be realized, and the speed of optimization solving and new iteration is ensured.
(3) Independent calculation of yaw control optimization solution. Firstly, respectively modeling aiming at the unit requirements of different models in a wind power plant, and establishing different unit linearization models; secondly, the deployment of the optimization solving algorithm in the server is independent, and parallel calculation is performed during operation. The same control parameters are generally used among different units in yaw control, and the independent optimization calculation method provided by the invention can realize that different units use different yaw control parameters, so as to realize more ideal control effects.
The invention also provides a yaw control dynamic optimization system of the wind turbine, which comprises a model building module, a model correcting module, a first data processing module, a second data processing module and a third data processing module;
The model building module is used for building a wind turbine generator linearization model, obtaining a space expression of a turbine generator linearization state according to structural parameters of the wind turbine generator, and taking wind speed, pitch angle and rated torque of a generator as input and power and torque of the generator as output;
the model correction module is used for fitting a coefficient of wind direction deviation to power influence through the wind turbine linearization model and correcting power output by the wind turbine linearization model;
the first data processing module is used for calculating the yaw control process and power output of the wind turbine generator set under the wind condition input for the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
the second data processing module is used for establishing a multi-objective optimization solution problem which is used for improving the equivalent power generation amount and limiting the yaw execution times to be greatly increased, constructing a minimum value optimization problem, and searching for a pareto optimal solution by utilizing the pareto optimal theory to complete the dynamic optimization work of the yaw control of the wind turbine.
The third data processing module is used for storing a plurality of pareto optimal solution models in the server, wherein each pareto optimal solution model corresponds to one wind turbine generator of the wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and delay time calculated by the optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing dynamic optimization work of yaw control of the wind turbine generator.
The invention also provides a mobile terminal, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, such as a wind turbine yaw control dynamic optimization program.
The steps of the dynamic optimization method for yaw control of the wind turbine generator set are realized when the processor executes the computer program, for example:
establishing a wind turbine generator linearization model, obtaining a space expression of a turbine generator linearization state according to structural parameters of the wind turbine generator, and taking wind speed, pitch angle and rated torque of a generator as input and power and torque of the generator as output;
fitting a coefficient of wind direction deviation on power influence through a wind turbine linearization model, and correcting power output by the wind turbine linearization model;
calculating the yaw control process and power output of the wind turbine generator set under the wind condition input according to the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
establishing a multi-objective optimization solution problem which is mostly used for improving the equivalent power generation amount and limiting the great increase of the yaw execution times, constructing a minimum value optimization problem, and searching for a pareto optimal solution model by utilizing a pareto optimal theory;
And storing a plurality of pareto optimal solution models in a server, wherein each pareto optimal solution model corresponds to one wind turbine generator of a wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and a new delay time calculated by an optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing dynamic optimization work of yaw control of the wind turbine generator.
Alternatively, the processor may implement functions of each module in the above system when executing the computer program, for example:
the model building module is used for building a wind turbine generator linearization model, obtaining a space expression of a turbine generator linearization state according to structural parameters of the wind turbine generator, and taking wind speed, pitch angle and rated torque of a generator as input and power and torque of the generator as output;
the model correction module is used for fitting a coefficient of wind direction deviation to power influence through the wind turbine linearization model and correcting power output by the wind turbine linearization model;
the first data processing module is used for calculating the yaw control process and power output of the wind turbine generator set under the wind condition input for the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
The second data processing module is used for establishing a multi-objective optimization solution problem which is used for improving the equivalent power generation amount and limiting the yaw execution times to be greatly increased, constructing a minimum value optimization problem, and searching for a pareto optimal solution by utilizing the pareto optimal theory to complete the dynamic optimization work of the yaw control of the wind turbine.
The third data processing module is used for storing a plurality of pareto optimal solution models in the server, wherein each pareto optimal solution model corresponds to one wind turbine generator of the wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and delay time calculated by the optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing dynamic optimization work of yaw control of the wind turbine generator.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the mobile terminal. For example, the computer program may be divided into a model building module, a model modification module, a first data processing module, a second data processing module, and a third data processing module;
The model building module is used for building a wind turbine generator linearization model, obtaining a space expression of a turbine generator linearization state according to structural parameters of the wind turbine generator, and taking wind speed, pitch angle and rated torque of a generator as input and power and torque of the generator as output;
the model correction module is used for fitting a coefficient of wind direction deviation to power influence through the wind turbine linearization model and correcting power output by the wind turbine linearization model;
the first data processing module is used for calculating the yaw control process and power output of the wind turbine generator set under the wind condition input for the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
the second data processing module is used for establishing a multi-objective optimization solution problem which is used for improving the equivalent power generation amount and limiting the yaw execution times to be greatly increased, constructing a minimum value optimization problem, and searching for a pareto optimal solution by utilizing the pareto optimal theory to complete the dynamic optimization work of the yaw control of the wind turbine.
The third data processing module is used for storing a plurality of pareto optimal solution models in the server, wherein each pareto optimal solution model corresponds to one wind turbine generator of the wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and delay time calculated by the optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing dynamic optimization work of yaw control of the wind turbine generator.
The mobile terminal can be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The mobile terminal may include, but is not limited to, a processor, memory.
The processor may be a central processing unit (CentralProcessingUnit, CPU), other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the mobile terminal, connecting various parts of the entire mobile terminal using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the mobile terminal by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SmartMediaCard, SMC), secure digital (SecureDigital, SD) card, flash card (FlashCard), at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the wind turbine yaw control dynamic optimization method.
The mobile terminal integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product.
Based on such understanding, the present invention may implement all or part of the above-mentioned method, or may be implemented by instructing the relevant hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program, when executed by a processor, may implement the steps of the above-mentioned wind turbine yaw control dynamic optimization method. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc.
The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth.
It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The dynamic optimization method for yaw control of the wind turbine generator is characterized by comprising the following steps of:
step 1, a linearization model of a wind turbine generator is established, a space expression of a linearization state of the wind turbine generator is obtained according to structural parameters of the wind turbine generator, wind speed, pitch angle and rated torque of a generator are used as inputs, and power and torque of the generator are used as outputs;
step 2, fitting a coefficient of wind direction deviation on power influence through a wind turbine linearization model, and correcting power output by the wind turbine linearization model;
step 3, calculating the yaw control process and power output of the wind turbine generator set under the wind condition input according to the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
Step 4, establishing a multi-objective optimization solution problem which is mostly used for improving the equivalent power generation amount and limiting the great increase of the yaw execution times, constructing a minimum value optimization problem, and searching for a pareto optimal solution model by utilizing a pareto optimal theory;
and 5, storing a plurality of pareto optimal solution models in a server, wherein each pareto optimal solution model corresponds to one wind turbine generator of a wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and a new delay time calculated by an optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing the dynamic optimization work of yaw control of the wind turbine generator.
2. The method for dynamically optimizing yaw control of a wind turbine according to claim 1, wherein in step 1, a linear state space expression of the wind turbine is obtained according to structural parameters of the wind turbine, and the formula is as follows:
wherein A is a system state coefficient matrix, B is a system control coefficient matrix, C is an output state coefficient matrix, D is an output control coefficient matrix, x, u and y are vectors, wherein u is an input vector comprising wind speed, rated torque and pitch angle, and y is an output vector comprising power and generator rotation speed; x and->The current time state and the next time state of the system are respectively.
3. The method for dynamically optimizing yaw control of a wind turbine according to claim 1, wherein in step 2, a wind turbine linearization model fits a coefficient of wind direction deviation on power, and the specific process is as follows:
setting the wind energy capturing efficiency of the wind turbine generator set as P and the yaw error angle of the wind turbine generator set as theta, and adopting the following formula of the wind energy capturing efficiency:
wherein: p is wind energy captured by the unit, unit: w is a metal; ρ is the air density in units: kg/m3; r is the radius of the impeller of the wind turbine, and the unit is: m; v is the inflow wind speed in units of: m/s; θ is yaw error angle, unit: rad; n is a coefficient to be determined;
wherein, the energy loss generated in the process of converting wind energy captured by the wind turbine generator into active power is recorded as P Damage to The real active power of the unit is: p (P) Active power ＝P-P Damage to ；
Wherein, when yaw error is θ:
P active power ＝P 0 ·cos n θ
Wherein P is 0 The yaw error is the active power of the wind turbine generator set when the yaw error is 0 degrees;
And (3) fitting a curve according to the yaw error sequence and the active power recorded in the collected wind turbine scada by adopting a least square method or a Fourier series approximation method, and determining the value of the undetermined coefficient n.
4. The dynamic optimization method for yaw control of a wind turbine generator according to claim 1, wherein in step 3, a yaw control algorithm is written by using python or c++ to calculate a yaw control process and a power output of the wind turbine generator under the wind condition input for the power output by the corrected wind turbine generator linearization model, wherein the yaw control process is represented by an absolute azimuth angle of a nacelle of the wind turbine generator and a yaw zone bit; the yaw zone bit is a series of digital signals, when the yaw motor is started, the yaw zone bit is set to be 0, and when the yaw motor executes yaw control action, the yaw zone bit is set to be 1; and counting the yaw execution times in the time period by counting the times of triggering the yaw zone bit.
5. The method for dynamically optimizing yaw control of a wind turbine according to claim 1, wherein in step 4, a minimum value optimization problem formula is constructed as follows:
wherein f 1 、f 2 Representing the mapping relation between the negative equivalent power generation amount and the yaw execution times relative to the threshold value and the delay time; x is a controller parameter, namely a threshold value and a delay time; x represents the range of the threshold value and the delay time, the threshold value is 5-20 degrees, the delay time is 20s-210s, natural numbers are taken, and the maximum value of the yaw control execution times is set as the boundary condition in the minimum value optimization problem.
6. The method for dynamically optimizing yaw control of a wind turbine generator according to claim 5, wherein in step 4, a multi-objective optimization solution problem is established for increasing equivalent power generation amount and limiting the number of yaw execution times to be greatly increased, and a pareto optimal solution is sought by utilizing a pareto optimal theory, and the specific process is as follows:
based on the solution corresponding to the initial value yaw threshold and the delay time, optimizing by utilizing a genetic algorithm, and calculating the yaw threshold and the delay time corresponding to the optimal solution on the pareto front edge to be compared with the initial value; if the solution on the front edge is better than the initial solution, selecting the middle point on the front edge as the optimized solution to perform optimization iteration of the threshold value and the delay time, and if the initial value is also positioned on the front edge, not updating, so as to complete the optimization solution of one period; and finally packaging all algorithms.
7. The method for dynamically optimizing yaw control of a wind turbine according to claim 1, wherein in step 5, a specific process of updating the new threshold value and delay time calculated by the optimization solution algorithm to the main control system of the corresponding wind turbine is as follows:
setting the data optimization period as x hours, acquiring wind speed, wind direction, power and yaw execution process data in x hours every x hours, taking the current threshold value, delay time and wind condition information as input, starting an optimization algorithm corresponding to the unit in a server, solving a new optimal value and current value comparison, and if the new optimal value is superior to the current value, iteratively writing the new optimal value and the new optimal value into a main control system by using the new threshold value and the new delay time.
8. A wind turbine yaw control dynamic optimization system, comprising:
the model building module is used for building a wind turbine generator linearization model, obtaining a space expression of a turbine generator linearization state according to structural parameters of the wind turbine generator, and taking wind speed, pitch angle and rated torque of a generator as input and power and torque of the generator as output;
the model correction module is used for fitting a coefficient of wind direction deviation to power influence through the wind turbine linearization model and correcting power output by the wind turbine linearization model;
the first data processing module is used for calculating the yaw control process and power output of the wind turbine generator set under the wind condition input for the power output by the corrected wind turbine generator set linearization model by using a yaw control algorithm;
the second data processing module is used for establishing a multi-objective optimization solution problem which is used for improving the equivalent power generation amount and limiting the yaw execution times to be greatly increased, constructing a minimum value optimization problem, and searching for a pareto optimal solution by utilizing a pareto optimal theory to complete the dynamic optimization work of the yaw control of the wind turbine;
the third data processing module is used for storing a plurality of pareto optimal solution models in the server, wherein each pareto optimal solution model corresponds to one wind turbine generator of the wind power plant, setting a data optimization period, establishing communication between the optimization solution server and a SCADA database of the wind power plant and a main control of the wind turbine generator, acquiring historical data in the SCADA database by the server, updating a new threshold value and delay time calculated by the optimization solution algorithm into a main control system of the corresponding wind turbine generator, and completing dynamic optimization work of yaw control of the wind turbine generator.
9. A mobile terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of a method for dynamic optimization of yaw control of a wind turbine according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of a method for dynamic optimization of yaw control of a wind turbine according to any one of claims 1-7.
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| CN117989054B (en) * | 2024-04-03 | 2024-06-07 | 东方电气风电股份有限公司 | Domestic fan intelligent control method, system and equipment |
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| CN117989054B (en) * | 2024-04-03 | 2024-06-07 | 东方电气风电股份有限公司 | Domestic fan intelligent control method, system and equipment |
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