Wind generating set parameter identification method based on catfish particle swarm optimization algorithm

Abstract

The invention discloses a catfish particle swarm optimization algorithm-based wind generating set parameter identification method. The method comprises the following steps of: constructing a direct-driven and double-fed wind generating set model in a PSCAD (Power System Computer Aided Design); adjusting control parameters of the built direct-driven and doubly-fed wind generating set identification model to obtain output data of the wind generating set; classifying the parameters according to the parameter sensitivity; constructing an error model of the target model and the identification model for the high-sensitivity parameters; and coding the control parameters, and carrying out iterative optimization operation on the control parameters by adopting a catfish particle swarm optimization algorithm to obtain an identification result of the control parameters of the direct-driven and double-fed wind generating sets. According to the method, the particle swarm optimization algorithm introducing the catfish operator is utilized, so that particles are prevented from falling into local optimum in parameter identification, the convergence speed of the particles is increased, the iterative optimization speed and effect of the algorithm are improved, and reliable identification of the control parameters of the direct-driven and doubly-fed wind generating sets can be realized.

Description

Technical Field

[0001] The present invention relates to the field of wind power generation system parameter identification, and in particular to a wind power generation set parameter identification method based on a catfish particle swarm optimization algorithm. Background Art

[0002] Under the strategic goal of "30·60 dual carbon" in my country, my country's power grid has vigorously developed and built a new power system, which is an important measure to respond to strategic goals and achieve energy conservation and emission reduction. Against the backdrop of continuous innovation in power technology, continuous development of energy structure and continuous optimization and progress of resources, a high proportion of renewable energy and a high proportion of power electronic devices ("double highs") are connected to the power system, which not only promotes the improvement of my country's cross-regional power transmission capacity, but also promotes the transformation of the power system to a new power system. The characteristics of the new power system are different from those of the traditional power system. Its scale, physical structure composition and operating characteristics under double high access will also change greatly. Simulation models and parameters that are more in line with reality are of great research significance to the power system. The control parameters of wind power generation systems are the core technology of equipment manufacturers. Equipment manufacturers will recompile and encapsulate the core models of their equipment, apply them to simulation in the form of packaging or linking, or use "black box" models to participate in power grid simulation operations. Therefore, due to the restrictions of intellectual property protection, scholars often cannot obtain specific control parameters. However, there are hundreds of new energy models, and the control characteristics of generator sets of different models from different manufacturers are very different, and there are many key parameters of the units. The "black box" model encapsulated with source code can only simulate the impact of new energy units after access to the large power grid system, and it is difficult to analyze the inherent control coupling characteristics of new energy units. Therefore, the use of parameter identification methods to identify the unknown control parameters of the wind turbine black box model is the basis for accurate modeling of new energy power generation systems, and is the basis and key technology for analyzing the impact of large-scale new energy access on the stability, reliability and economy of the power system. The present invention proposes a method for realizing wind turbine parameter identification based on the particle swarm algorithm for the direct-drive and doubly-fed wind turbine generator model established in PSCAD, and identifies the control parameters of the wind turbine model in PSCAD. Summary of the invention

[0003] In order to solve the problems of the prior art, the present invention proposes a method for wind turbine generator set parameter identification based on catfish particle swarm optimization algorithm.

[0004] A method for identifying parameters of a wind turbine generator set based on a catfish particle swarm optimization algorithm, the method comprising: Build direct-drive and doubly-fed wind turbine models in PSCAD; Adjust the control parameters of the constructed direct-drive and doubly-fed wind turbine identification models to obtain wind turbine output data; Classify parameters according to their sensitivity; Construct error models of target model and identification model for highly sensitive parameters; The control parameters are encoded and the catfish particle swarm optimization algorithm is used to iteratively optimize the control parameters to obtain the identification results of the control parameters of direct-drive and doubly-fed wind turbine generator sets.

[0005] Furthermore, the direct-drive and doubly-fed wind turbine generator model is constructed in PSCAD, specifically including: Establish equivalent circuits and corresponding models of direct-drive and doubly-fed wind turbine generator sets, including direct-drive and doubly-fed wind turbine generators and mechanical control models, grid-side converters, and machine-side converters; A control model of a direct-drive and doubly-fed wind turbine generator set is established, including a grid-side converter and a machine-side converter control system model; the grid-side converter adopts dual-loop control, the outer loop is controlled by DC voltage, and the inner loop is controlled by current.

[0006] Furthermore, the parameters are classified according to parameter sensitivity, specifically including: The parameters are classified by using the trajectory sensitivity analysis method; the trajectory sensitivity analysis method is to first classify the parameters by using the constant speed method in the parameter sensitivity analysis, and the high-sensitivity parameters are listed as the parameters to be identified; the parameters with low sensitivity are not identified, but are fixed or set as empirical values, and then step-by-step identification is performed; The formula for trajectory sensitivity is: ; In the formula, is the trajectory sensitivity; is the parameter to be identified; is the parameter change; is the output change; The output results of the wind turbine generator set are analyzed using correlation analysis to analyze the control parameters that have the greatest impact on each type of output.

[0007] Furthermore, the error model of the target model and the identification model is constructed for the highly sensitive parameters, specifically including: Adjust the target model and identification model, set a unified operating condition, and obtain the output results of the target model and identification model; The output results of the identification model and the target model are compared to establish the fitting effect of the error model evaluation parameters. The specific formula is as follows: ; Where: is the number of samples; For the The output result of the target model of each point; For the The output result of the identification model of each point; Model tracking error The closer the value is to 0, the more the parameters of the identification model fit the target model. Otherwise, the error is larger and the recognition effect of the parameters is not ideal. set up If the threshold range of If the threshold is exceeded, it means that the identification model parameter settings are not reasonable enough and the steps need to be repeated. Make parameter corrections.

[0008] Furthermore, the control parameters are encoded and processed, and the catfish particle swarm optimization algorithm is used to iteratively optimize the control parameters to obtain identification results of the control parameters of the direct-drive and doubly-fed wind turbine generator sets, specifically including: The population is processed by real number coding, and the position vector of the particle represents the sequence of parameter values ​​and time; The catfish particle swarm optimization algorithm is used to optimize and solve the control parameters; the catfish particle swarm optimization algorithm is used, specifically including: Initialize the initial speed and position of the particle swarm. During the iterative optimization process of the catfish particle swarm algorithm, it will be affected by the individual optimal value and the global optimal value. The single speed and position update formula of the particle population is as follows: ; ; Where: Update speed for particles; represents the particle position; Number the particles; is the search dimension; is the number of iterations; is the inertia weight; and is the acceleration factor, where is the local optimization acceleration coefficient, is the global optimization acceleration coefficient; and is the disturbance coefficient, where is the local optimal perturbation coefficient, is the global optimal perturbation coefficient; , for A random number between For the Individuals go through The optimal position of the iteration; Is the particle group passing through The global optimal position of the iteration; and is the catfish operator, and the formula is as follows: ; ; Where: is the deviation between the current particle position and the individual optimal position; is the deviation between the current particle position and the global optimal position; and is the deviation threshold; when the deviation value is greater than the set deviation threshold, the value of the catfish operator is 1, indicating that the particle will continue to perform the optimization algorithm; when the deviation value is less than the set deviation threshold, it means that the particle falls into a local optimal state and the catfish operator is needed to break this local optimal state; The fitness of the particles is calculated using the error between the target model and the identification model of the wind turbine as the fitness function; the optimal value of the individual position and the optimal global position can be obtained based on the obtained fitness value; Use equations (2) and (3) to update the position and velocity of the particle; The calculated fitness value of the particle at the new position updates the optimal value of the individual position and the optimal value of the global position; follow the following formula: ; ; When the iteration reaches the predetermined number of times, the optimal position of the particle is output, otherwise the optimization continues from the initial speed and position of the initialized particle swarm; Determine whether the parameters obtained by identification can reproduce the output of the target model. If they are satisfied, the parameters are the identification results. If not, readjust and correct the initial speed and position of the initialized particle swarm and repeat the above steps. During the optimization iteration process, the current speed and position of the particle may exceed the boundary conditions. In this case, the boundary range is used as the new position or speed of the particle: ; .

[0009] On the other hand, the present invention also proposes a wind turbine generator parameter identification system based on catfish particle swarm optimization algorithm, comprising: The model building module is configured to: build direct-drive and doubly-fed wind turbine models in PSCAD; The temporary parameter determination module is configured to: adjust the control parameters of the constructed direct-drive and doubly-fed wind turbine generator identification models to obtain wind turbine generator output data; The classification module is configured to: classify the parameters according to the sensitivity of the parameters; Building an error model, configured to: build an error model of a target model and an identification model for a parameter with high sensitivity; The parameter acquisition module is configured to: encode the control parameters, and use the catfish particle swarm optimization algorithm to iteratively optimize the control parameters to obtain the identification results of the control parameters of the direct-drive and doubly-fed wind turbine generator sets.

[0010] On the other hand, the present invention also provides an electronic device, The invention comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the above method is implemented.

[0011] On the other hand, the present invention further proposes a computer-readable storage medium for storing computer instructions, which complete the above method when executed by a processor.

[0012] Beneficial effects of the present invention: The present invention introduces the "catfish effect" and adds disturbances to particles trapped in the local optimum, which can not only make up for the situation that the traditional particle swarm algorithm is prone to premature convergence, jump out of the local optimum, and further improve the recognition accuracy of the control parameters of the wind turbine generator set, but also can improve the vitality and diversity of the particles through the catfish operator, further improve the convergence of the algorithm, and accelerate the speed of parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention has the following accompanying drawings: Figure 1 It is a process roadmap of the wind turbine generator set parameter identification method based on the catfish particle swarm optimization algorithm of the present invention; Figure 2 This is a step diagram of an identification algorithm for a wind turbine generator set parameter identification method based on a catfish particle swarm optimization algorithm according to the present invention. DETAILED DESCRIPTION

[0014] The following is combined with Figure 1-2 The present invention is described in further detail.

[0015] Step A. Build direct-drive and doubly-fed wind turbine models in PSCAD.

[0016] A1. Establish the equivalent circuit and corresponding model of direct-drive and doubly-fed wind turbine generator sets, including direct-drive and doubly-fed wind turbine generators and mechanical control models, grid-side converters, and machine-side converters.

[0017] A2. Establish the control model of direct-drive and doubly-fed wind turbine generator sets, including the grid-side converter and the machine-side converter control system model. The grid-side converter adopts dual-loop control, the outer loop is controlled by DC voltage, and the inner loop is controlled by current.

[0018] Step B: Adjust the control parameters of the constructed direct-drive and doubly-fed wind turbine generator identification models to obtain a certain amount of wind turbine generator output data.

[0019] Step C. Classify the parameters according to their sensitivity.

[0020] C1. There are too many control parameters for wind turbines, and the trajectory sensitivity analysis method is used to classify the parameters.

[0021] C2. The analysis method of trajectory sensitivity is to first use the constant speed method in parameter sensitivity analysis to classify the parameters, and list the high-sensitivity parameters as parameters to be identified; the parameters with low sensitivity are not identified, but are fixed or set as empirical values, and then step-by-step identification is performed.

[0022] C3. The formula for trajectory sensitivity is: ; In the formula, is the trajectory sensitivity; is the parameter to be identified; is the parameter change; is the output change.

[0023] C4. The output results of wind turbines are diverse and complex. Correlation analysis is used to analyze the control parameters that have the greatest impact on each type of output.

[0024] Step D: Construct the error model of the target model and the identification model for the highly sensitive parameters.

[0025] D1. Adjust the target model and identification model, set a unified operating condition, and obtain the output results of the target model and identification model.

[0026] D2. Compare the output results of the identification model and the target model to establish the fitting effect of the error model evaluation parameters. The specific formula is as follows: ; Where: is the number of samples; For the The output result of the target model of each point; For the The output result of the identification model of each point; Model tracking error The closer the value is to 0, the more the parameters of the identification model fit the target model. Otherwise, the error is larger and the recognition effect of the parameter is not ideal.

[0027] D3. Settings If the threshold range of If the threshold is exceeded, it means that the identification model parameter settings are not reasonable enough and the steps need to be repeated. Make parameter corrections.

[0028] Step E: Encode the control parameters, and use the catfish particle swarm optimization algorithm to iteratively optimize the control parameters to obtain identification results of the control parameters of the direct-drive and doubly-fed wind turbine generator sets.

[0029] E1. The population is processed using real number coding, and the particle position vector represents the sequence of parameter values ​​and time.

[0030] E2. The catfish particle swarm optimization algorithm is used to optimize the control parameters. The method is as follows: E3. Initialize the initial speed and position of the particle swarm. During the iterative optimization process of the catfish particle swarm algorithm, it will be affected by the individual optimal value and the global optimal value. The single speed and position update formula of the particle population is as follows: ; ; Where: Update speed for particles; represents the particle position; Number the particles; is the search dimension; is the number of iterations; is the inertia weight; and is the acceleration factor, where is the local optimization acceleration coefficient, is the global optimization acceleration coefficient; and is the disturbance coefficient, where is the local optimal perturbation coefficient, is the global optimal perturbation coefficient; for A random number between . For the Individuals go through The optimal position of the iteration; Is the particle group passing through The global optimal position of the iteration; and is the catfish operator, and the formula is as follows: ; ; Where: is the deviation between the current particle position and the individual optimal position; is the deviation between the current particle position and the global optimal position; and is the deviation threshold. When the deviation value is greater than the set deviation threshold, the value of the catfish operator is 1, indicating that the particle will continue to optimize the algorithm. When the deviation value is less than the set deviation threshold, it means that the particle falls into a local optimal state and the catfish operator is needed to break this local optimal state.

[0031] E4. The fitness of the particles is calculated using the error between the target model of the wind turbine generator set and the identification model as the fitness function. The optimal value of the individual position and the optimal global position can be obtained based on the obtained fitness value.

[0032] E5. Use equations (2) and (3) to update the position and velocity of the particle.

[0033] E6. Calculate the fitness value of the particle at the new position, and update the optimal value of the individual position and the optimal value of the global position. Follow the following formula: ; ; E7. When the iteration reaches the predetermined number of times, the optimal position of the particle is output, otherwise the optimization continues from E3.

[0034] E8. Determine whether the parameters obtained by identification can reproduce the output of the target model. If so, the parameters are the identification results. If not, readjust and correct from E3 and repeat the above steps.

[0035] E9. During the optimization iteration process, the current velocity and position of the particle may exceed the boundary conditions. In this case, the boundary range is used as the new position or velocity of the particle: ; ; The above implementation modes are only used to illustrate the present invention, but not to limit the present invention. Ordinary technicians in the relevant technical field can make various changes and deformations without departing from the essence and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The scope of patent protection of the present invention should be limited by the claims.

[0036] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A wind turbine generator parameter identification method based on catfish particle swarm optimization algorithm, characterized in that: The method comprises: Build direct-drive and doubly-fed wind turbine models in PSCAD; Adjust the control parameters of the constructed direct-drive and doubly-fed wind turbine generator identification models to obtain wind turbine generator output data; Classify parameters according to their sensitivity; Construct error models of target model and identification model for highly sensitive parameters; The control parameters are encoded and the catfish particle swarm optimization algorithm is used to iteratively optimize the control parameters to obtain the identification results of the control parameters of the direct-drive and doubly-fed wind turbine generator sets.

2. A method for wind turbine generator parameter identification based on catfish particle swarm optimization algorithm as claimed in claim 1, characterized in that: The direct-drive and doubly-fed wind turbine generator model is constructed in PSCAD, specifically including: Establish equivalent circuits and corresponding models of direct-drive and doubly-fed wind turbine generator sets, including direct-drive and doubly-fed wind turbine generators and mechanical control models, grid-side converters, and machine-side converters; A control model of a direct-drive and doubly-fed wind turbine generator set is established, including a grid-side converter and a machine-side converter control system model; the grid-side converter adopts dual-loop control, the outer loop is controlled by DC voltage, and the inner loop is controlled by current.

3. A method for wind turbine generator parameter identification based on catfish particle swarm optimization algorithm as claimed in claim 2, characterized in that: The parameters are classified according to parameter sensitivity, specifically including: The parameters are classified by using the trajectory sensitivity analysis method; the trajectory sensitivity analysis method is to first classify the parameters by using the constant speed method in the parameter sensitivity analysis, and the high-sensitivity parameters are listed as the parameters to be identified; the parameters with low sensitivity are not identified, but are fixed or set as empirical values, and then step-by-step identification is performed; The formula for trajectory sensitivity is: ; In the formula, is the trajectory sensitivity; is the parameter to be identified; is the parameter change; is the output change; The output results of the wind turbine generator set are analyzed using correlation analysis to analyze the control parameters that have the greatest impact on each type of output.

4. A method for wind turbine generator parameter identification based on catfish particle swarm optimization algorithm as claimed in claim 3, characterized in that: The error model of the target model and the identification model constructed for the highly sensitive parameters specifically includes: Adjust the target model and identification model, set a unified operating condition, and obtain the output results of the target model and identification model; The output results of the identification model and the target model are compared to establish the fitting effect of the error model evaluation parameters. The specific formula is as follows: ; Where: is the number of samples; For the The output result of the target model of each point; For the The output result of the identification model of each point; Model tracking error The closer the value is to 0, the more the parameters of the identification model fit the target model. Otherwise, the error is larger and the recognition effect of the parameters is not ideal. set up If the threshold range of If the threshold is exceeded, it means that the identification model parameter settings are not reasonable enough and the steps need to be repeated. Make parameter corrections.

5. A method for wind turbine generator parameter identification based on catfish particle swarm optimization algorithm as claimed in claim 4, characterized in that: The encoding process of the control parameters and the iterative optimization operation of the control parameters using the catfish particle swarm optimization algorithm to obtain the identification results of the control parameters of the direct-drive and doubly-fed wind turbine generator sets specifically include: The population is processed by real number coding, and the position vector of the particle represents the sequence of parameter values ​​and time; The catfish particle swarm optimization algorithm is used to optimize and solve the control parameters; the catfish particle swarm optimization algorithm is used, specifically including: Initialize the initial speed and position of the particle swarm. During the iterative optimization process of the catfish particle swarm algorithm, it will be affected by the individual optimal value and the global optimal value. The single speed and position update formula of the particle population is as follows: ; ; Where: Update speed for particles; represents the particle position; Number the particles; is the search dimension; is the number of iterations; is the inertia weight; and is the acceleration factor, where is the local optimization acceleration coefficient, is the global optimization acceleration coefficient; and is the disturbance coefficient, where is the local optimal perturbation coefficient, is the global optimal perturbation coefficient; for A random number between For the Individuals go through The optimal position of the iteration; Is the particle group passing through The global optimal position of the iteration; and is the catfish operator, and the formula is as follows: ; ; Where: is the deviation between the current particle position and the individual optimal position; is the deviation between the current particle position and the global optimal position; and is the deviation threshold; when the deviation value is greater than the set deviation threshold, the value of the catfish operator is 1, indicating that the particle will continue to perform the optimization algorithm; when the deviation value is less than the set deviation threshold, it means that the particle falls into a local optimal state and the catfish operator is needed to break this local optimal state; The fitness of the particles is calculated using the error between the target model and the identification model of the wind turbine as the fitness function; the optimal value of the individual position and the optimal global position are obtained based on the obtained fitness value; Use equations (2) and (3) to update the position and velocity of the particle; The calculated fitness value of the particle at the new position updates the optimal value of the individual position and the optimal value of the global position; follow the following formula: ; ; When the iteration reaches the predetermined number of times, the optimal position of the particle is output, otherwise the optimization continues from the initial speed and position of the initialized particle swarm; Determine whether the parameters obtained by identification can reproduce the output of the target model. If they are satisfied, the parameters are the identification results. If not, readjust and correct the initial speed and position of the initialized particle swarm and repeat the above steps. During the optimization iteration process, there may be a situation where the current speed and position of the particle exceeds the boundary conditions. In this case, the boundary range is used as the new position or new speed of the particle: ; 。 6. A wind turbine parameter identification system based on catfish particle swarm optimization algorithm, characterized in that: include: The model building module is configured to: build direct-drive and doubly-fed wind turbine models in PSCAD; The temporary parameter determination module is configured to: adjust the control parameters of the constructed direct-drive and doubly-fed wind turbine generator identification models to obtain wind turbine generator output data; The classification module is configured to: classify the parameters according to the sensitivity of the parameters; Building an error model, configured to: build an error model of a target model and an identification model for a parameter with high sensitivity; The parameter acquisition module is configured to: encode the control parameters, and use the catfish particle swarm optimization algorithm to iteratively optimize the control parameters to obtain the identification results of the control parameters of the direct-drive and doubly-fed wind turbine generator sets.

7. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 5 is completed.

8. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 5.

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