阅读说明：本技术 基于转矩增益系数优化的风电机组最大功率点跟踪控制方法 (Wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization ) 是由 殷明慧 张欢 周连俊 陈载宇 彭云 杨炯明 卜京 邹云 顾伟 徐畅 李阳 于 2020-03-23 设计创作，主要内容包括：本发明公开了一种基于转矩增益系数优化的风电机组最大功率点跟踪控制方法,该方法在减小转矩增益的MPPT控制方法基础上,将风电机组运行在PSF法下的风能捕获效率作为湍流风速对MPPT影响的综合度量指标,离线遍历最优转矩增益系数与该指标的函数关系；在线运行时,周期性地获取该综合度量指标,并根据函数对转矩增益系数的最优设定值进行预估及更新；通过在机组主控PLC中构建运行PSF法的虚拟风电机组与实际机组同步运行的手段实现PSF法对应风能捕获效率的获取。本发明可实现多个指标对MPPT综合影响的单一指标刻画,简化直接数量关系的构建复杂程度；在保证风能捕获效率的同时,大幅降低算力资源要求。(The invention discloses a maximum power point tracking control method of a wind turbine generator based on torque gain coefficient optimization, which is characterized in that on the basis of an MPPT control method for reducing torque gain, wind energy capture efficiency of the wind turbine generator operated under a PSF method is used as a comprehensive measurement index of the influence of turbulent wind speed on MPPT, the functional relation between an optimal torque gain coefficient and the index is traversed off line, the comprehensive measurement index is periodically obtained during online operation, the optimal set value of the torque gain coefficient is estimated and updated according to a function, and the acquisition of the corresponding wind energy capture efficiency of the PSF method is realized by constructing a means of synchronous operation of a virtual wind turbine generator operated with the PSF method and an actual wind turbine generator in a main control P L C of the wind turbine generator.)

1. A wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization is characterized by comprising the following steps:

(1) offline construction of functional relationships

Step 1-1: aiming at a wind turbine generator to which the method is applied, acquiring pneumatic and structural parameters required for constructing a FAST model of the wind turbine generator;

step 1-2: in FAST software, finishing the construction of a wind turbine generator simulation model according to parameters;

step 1-3: a turbulent wind speed simulation method is adopted, and three characteristic indexes representing turbulent wind conditions are changed in sequence: mean wind speedThe turbulence intensity TI and the turbulence integral scale L generate turbulence wind speed sequences corresponding to different characteristic index combinations;

step 1-4: wind turbine generator optimal torque gain coefficient corresponding to each wind speed sequence based on FAST software traversalAnd wind energy capture efficiency when applying the PSF method

Step 1-5: fitting the result obtained by traversing as sample data to obtain an optimal torque gain coefficientAnd the average wind energy capture efficiency under the PSF methodFunctional relationship of

(2) Virtual wind turbine generator system construction

Step 2-1, embedding a FAST model of the wind turbine generator to be applied into an actual main control P L C of the wind turbine generator, and constructing a virtual generator set capable of synchronously operating with the actual generator set;

step 2-2, embedding a PSF method code for MPPT control of the virtual wind turbine generator in a master control P L C;

(3) on-line operation

Step 3-1: the set torque gain coefficient is excellentHas a chemical period of T_wInitializing the torque gain coefficient K of the actual wind turbine generator operation_dInitializing initial rotation speed omega of virtual wind turbine generator and actual wind turbine generator_bgnSetting the current time interval as n to be 1 for the lowest rotating speed of the MPPT stage;

step 3-2, reading a current actually measured wind speed value, carrying out MPPT control on an actual wind turbine generator by adopting a torque gain reduction method optimized by a torque gain coefficient, carrying out MPPT control on a virtual wind turbine generator in P L C by adopting a PSF method, and synchronously operating the two;

step 3-3: recording the operating data of the virtual wind turbine, including the rotor speed ω_rAcceleration of rotorElectromagnetic torque T of generator_e；

Step 3-4: judging the nth T_wWhether the time interval runs over; if so, calculating the average wind energy capture efficiency corresponding to the PSF method applied to the virtual wind turbine generator set at the current time period according to the recorded operation dataAnd substituting into the function relation constructed off-lineIn the process of pre-estimating the optimal torque gain coefficientOtherwise, returning to execute the step 3-2;

step 3-5: the optimal torque gain coefficient given in the step 3-4 is obtainedSetting a torque gain coefficient of an n +1 th time period of an actual wind turbine generator;

step 3-6: and n is n +1, jumping to the step 3-2, and entering the next operation period.

2. The wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization according to claim 1, wherein steps 1-4 are based on FAST software to traverse the wind turbine generator optimal torque gain coefficient corresponding to each wind speed sequenceAnd wind energy capture efficiency when applying the PSF methodWherein

In the formula, P_capRepresenting actual power captured by the wind turbine, P_wyIs the maximum wind power in the air, v is the wind speed, n_gFor gear ratio of gear box, T_eRepresenting electromagnetic torque, ω_rDenotes the rotational speed, J_tRepresenting the moment of inertia, rho representing the air density, and R representing the radius of the wind wheel; optimum torque gain factorI.e. K corresponding to the maximum efficiency of the average wind energy capture_d。

3. The wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization according to claim 1 or 2, wherein, in steps 1-3,the variation range of the wind speed sequence is 4-9 m/s, the step length is 1m/s, the turbulence intensity TI is changed according to A, B, C turbulence levels, the variation range of the integral scale L is 100-500 m, the step length is 50m, 162 parameter setting combined wind conditions can be obtained, and 10 wind speed sequences are generated corresponding to each wind condition.

4. The torque-based system of claim 3The maximum power point tracking control method of the wind turbine generator with optimized gain coefficient is characterized in that in the step 1-5, the optimal torque gain coefficient is fitted by taking 1620 groups of results obtained by traversal as sample dataAnd the average wind energy capture efficiency under the PSF methodFunctional relationship of

5. The wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization according to claim 1 or 2, wherein the torque gain coefficient optimization period T in step 3-1_wSetting the value to be 10 min-1 h, and initializing the torque gain coefficient K of the actual wind turbine generator operation_dIs 0.9K_opt～0.98K_optIn which K is_optIs the torque gain factor of the PSF method.

Technical Field

The invention belongs to the field of wind turbine generator control, and particularly relates to a wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization.

Background

In order to improve the Maximum Power Point Tracking (MPPT) performance of the wind turbine generator facing turbulent wind speed, an improved PSF method based on two ideas of torque gain and tracking interval adjustment is developed on the basis of applying the most extensive Power Signal Feedback (PSF) method. The two ideas are that the tracking performance of the high-energy wind speed area is improved by sacrificing the wind energy capturing efficiency of the low-energy wind speed area, and key adjusting parameters need to be reasonably set to balance loss and lifting amount, so that the maximization of the overall efficiency is realized. Research shows that the optimal value of the key adjusting parameter is influenced by wind condition characteristics (average wind speed, turbulence intensity and turbulence frequency) and factors such as unit aerodynamics and structural parameters. How to periodically estimate and update the optimal setting of the key parameters during operation based on the above mentioned changes of the influencing factors becomes a focus issue.

The problem currently exists in Adaptive Torque Control (ATC) and two types of solutions for constructing a quantitative relation between an optimal value of a key parameter and an influencing factor and guiding online operation. The self-adaptive torque control determines the direction and the magnitude of the disturbance of the next period according to the change of the wind energy capture efficiency after the key parameter of the disturbance on the basis of the method for reducing the torque gain. And the latter directly constructs a definite nonlinear function relation between the optimal torque curve adjustment quantity and three wind condition characteristics and unit parameters in an off-line traversal mode aiming at a specific unit. When the system runs online, the optimal set value of the key parameter can be estimated according to the wind condition information and the functional relation.

The self-adaptive algorithm does not need to know the parameters of the wind turbine generator in advance, has the advantages of strong universality and capability of being quickly implemented in batches, but has the problems of search non-convergence and search direction error in partial wind condition change scenes, so that the MPPT performance of the method is limited in practical application. The method for directly constructing the function relationship between the optimal torque curve adjustment quantity and the three wind condition characteristics to guide parameter online optimization avoids an iterative search process, can obtain higher wind energy capture efficiency and good wind condition adaptability, but the method consumes a large amount of time and calculation force to perform offline traversal work, is not easy to implement quickly in batches, and limits the engineering practicability. Therefore, how to combine high wind energy capture efficiency and quick implementation is a problem that needs to be further solved by the current MPPT control method.

Disclosure of Invention

The invention aims to provide a wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization, and the wind turbine generator maximum power point tracking control method can obtain higher wind energy capture efficiency on the premise of paying less calculation power and time cost.

The technical solution for realizing the purpose of the invention is as follows: a wind turbine generator maximum power point tracking control method based on torque gain coefficient optimization comprises the following steps:

(1) offline construction of functional relationships

Step 1-1: aiming at a wind turbine generator to which the method is applied, acquiring pneumatic and structural parameters required for constructing a FAST model of the wind turbine generator;

step 1-2: in FAST software, finishing the construction of a wind turbine generator simulation model according to parameters;

step 1-3: a turbulent wind speed simulation method is adopted, and three characteristic indexes representing turbulent wind conditions are changed in sequence: mean wind speedThe turbulence intensity TI and the turbulence integral scale L generate turbulence wind speed sequences corresponding to different characteristic index combinations;

step 1-4: wind turbine generator optimal torque gain coefficient corresponding to each wind speed sequence based on FAST software traversalAnd wind energy capture efficiency when applying the PSF method

Step 1-5: fitting the result obtained by traversing as sample data to obtain an optimal torque gain coefficientAnd the average wind energy capture efficiency under the PSF methodFunctional relationship of

(2) Building virtual wind turbine generator

Step 2-1, embedding a FAST model of the wind turbine generator to be applied into an actual main control P L C of the wind turbine generator, and constructing a virtual generator set capable of synchronously operating with the actual generator set;

step 2-2, embedding a PSF method code for MPPT control of the virtual wind turbine generator in a master control P L C;

(3) on-line operation

Step 3-1: setting the torque gain factor optimization period to T_wInitializing the torque gain coefficient K of the actual wind turbine generator operation_dInitializing initial rotation speed omega of virtual wind turbine generator and actual wind turbine generator_bgnSetting the current time interval as n to be 1 for the lowest rotating speed of the MPPT stage;

step 3-2, reading a current actually measured wind speed value, carrying out MPPT control on an actual wind turbine generator by adopting a torque gain reduction method optimized by a torque gain coefficient, carrying out MPPT control on a virtual wind turbine generator in P L C by adopting a PSF method, and synchronously operating the two;

step 3-3: recording the operating data of the virtual wind turbine, including the rotor speed ω_rAcceleration of rotorElectromagnetic torque T of generator_e；

Step 3-4: judging the nth T_wWhether the time interval runs over; if so, calculating the average wind energy capture efficiency corresponding to the PSF method applied to the virtual wind turbine generator set at the current time period according to the recorded operation dataAnd substituting into the function relation constructed off-lineIn the process of pre-estimating the optimal torque gain coefficientOtherwise, returning to execute the step 3-2;

step 3-5: the optimal torque gain coefficient given in the step 3-4 is obtainedSetting a torque gain coefficient of an n +1 th time period of an actual wind turbine generator;

step 3-6: and n is n +1, jumping to the step 3-2, and entering the next operation period.

Compared with the prior art, the invention has the following remarkable advantages: (1) book (I)The invention directly establishes the relation between the optimal gain coefficient and the wind condition characteristics, avoids the problems of self-adaptive iterative search process and possible search non-convergence, and has strong adaptability to the wind condition change; (2) the invention introduces a power curve method to correspond to the wind energy capture efficiencyThe method has the advantages that three wind condition characteristic indexes of average wind speed, turbulence intensity and turbulence frequency are replaced, the functional relation between the wind condition characteristic indexes and the optimal torque gain coefficient of the wind turbine generator in the MPPT stage is established, and compared with the existing off-line traversal and on-line optimization algorithms such as a neural network and a response surface model, the wind energy capturing efficiency is guaranteed, meanwhile, the requirements of computational resources and traversal time are greatly reduced, and the method has high engineering practicability.

The present invention is described in further detail below with reference to the attached drawing figures.

Drawings

Fig. 1 is a flowchart of a wind turbine maximum power point tracking control method based on torque gain coefficient optimization according to the present invention.

FIG. 2 is a schematic diagram of a statistical relationship between the optimal gain coefficient of the NRE L CART3 wind turbine generator and the average wind energy capture efficiency corresponding to the PSF method.

FIG. 3 is a graph comparing the performance of the method of the present invention with other methods.

Detailed Description

The invention belongs to the type of a method for constructing a direct quantitative relation between optimal adjustment parameters and MPPT influence factor description indexes to guide parameter online optimization, and has higher wind energy capture performance. The invention further realizes the single index depiction of the comprehensive influence of a plurality of indexes on MPPT, simplifies the construction difficulty of direct quantity relation, can obtain higher wind energy capture efficiency on the premise of paying less calculation resources and time cost, and has more engineering application value.

With reference to fig. 1, the method of the invention firstly establishes a functional relationship between the optimal torque gain coefficient and the corresponding wind energy capture efficiency of the PSF method offline, and periodically adjusts the torque curve gain coefficient of the wind turbine generator during the actual operation process according to the functional relationship. Aiming at the problem that the wind energy capture efficiency corresponding to the PSF method cannot be directly obtained by improving the MPPT control method in the actual operation of the wind turbine generator, the problem is solved by constructing a virtual wind turbine generator applying the PSF method in a controller and synchronously operating the virtual wind turbine generator and the actual wind turbine generator.

The method comprises the following steps of offline construction of a functional relation:

step 1-1: aiming at a wind turbine generator to which the method is applied, acquiring pneumatic and structural parameters required for constructing a FAST model of the wind turbine generator;

step 1-2: in FAST software, the construction of a wind turbine generator simulation model is completed according to parameters;

step 1-3: three characteristic indexes representing turbulent wind conditions are changed in sequence by adopting a turbulent wind speed simulation method, wherein the three characteristic indexes are respectively mean wind speedsThe turbulence intensity TI and the turbulence integral scale L, generate a sequence of turbulent wind speeds corresponding to different combinations of characteristic indicators, wherein,the variation range of the wind speed is 4-9 m/s, the step length is 1m/s, the turbulence intensity TI is changed according to A, B, C turbulence levels, the variation range of the integral scale L is 100-500 m, the step length is 50m, 162 parameters can be obtained in total to set combined wind conditions, and 10 wind speed sequences are generated corresponding to each wind condition;

step 1-4: wind turbine generator optimal torque gain coefficient corresponding to each wind speed sequence based on FAST software traversalAnd wind energy capture efficiency when applying the PSF methodWherein

In the formula, P_capRepresenting actual captured power, P, of the wind turbine_wyIs the maximum wind power in the air, v is the wind speed, n_gFor gear ratio, T, of the gearbox_eRepresenting electromagnetic torque, omega_rIndicates the rotational speed J_tRepresenting the moment of inertia, ρ representing the air density, and R the rotor radius. Optimum torque gain factorI.e. K corresponding to the maximum efficiency of the average wind energy capture_d；

Step 1-5: fitting an optimal torque gain coefficient by using 1620 sets of results obtained by traversing as sample dataAnd the average wind energy capture efficiency under the PSF methodFunctional relationship of

The virtual wind turbine generator set is constructed by the following steps:

step 2-1, embedding a FAST model of the wind turbine generator to be applied into an actual main control P L C of the wind turbine generator, and constructing a virtual generator set capable of synchronously operating with the actual generator set;

and 2-2, embedding a PSF method code for MPPT control of the virtual wind turbine generator in a master control P L C.

The online operation steps are as follows:

step 3-1: setting the torque gain factor optimization period to T_wInitializing the torque gain coefficient K of the actual wind turbine generator operation_dInitializing initial rotation speed omega of virtual wind turbine generator and actual wind turbine generator_bgnSetting the current time interval as n to be 1 for the lowest rotating speed of the MPPT stage; wherein the torque gain factor optimizes the period T_wSetting the value to be 10 min-1 h, and initializing the torque gain coefficient K of the actual wind turbine generator operation_dIs 0.9K_opt～0.98K_opt，K_optA torque gain factor for the PSF method;

step 3-2, reading a current actually measured wind speed value, carrying out MPPT control on an actual wind turbine generator by adopting a torque gain reduction method optimized by a torque gain coefficient, carrying out MPPT control on a virtual wind turbine generator in P L C by adopting a PSF method, and synchronously operating the two;

step 3-3: recording the operating data of the virtual wind turbine, including the rotor speed ω_rAcceleration of rotorElectromagnetic torque T of generator_e；

Step 3-4: judging the nth T_wWhether the session is running over. If so, calculating the average wind energy capture efficiency corresponding to the PSF method applied to the virtual wind turbine generator set at the current time period according to the recorded operation dataAnd substituting into the function relation constructed off-lineIn the process of pre-estimating the optimal torque gain coefficientOtherwise, returning to execute the step 3-2;

step 3-5: the optimal torque gain coefficient given in the step 3-4 is obtainedSetting a torque gain coefficient of an n +1 th time period of an actual wind turbine generator;

step 3-6: and n is n +1, jumping to the step 3-2, and entering the next operation period.

The invention belongs to the type of a method for constructing a direct quantitative relation between optimal adjustment parameters and MPPT influence factor characterization indexes to guide parameter online optimization, and can avoid efficiency reduction caused by non-convergence or direction error of iterative search of a self-adaptive algorithm. In addition, the invention realizes the single index depiction of the comprehensive influence of a plurality of indexes on the MPPT, and can simplify the construction complexity of the direct quantity relationship. Therefore, the wind energy capture efficiency can be ensured, the computational resource requirement is greatly reduced, and the engineering practicability is high.

The present invention is described in further detail below with reference to examples: