A wind power inertia parameter optimization method based on a particle swarm algorithm

Abstract

The present application relates to a kind of wind power inertia parameter optimization method based on particle swarm algorithm, belong to the field of power system frequency modulation.The method mainly includes the following steps: establishing power system simulation model;System frequency optimization objective function and constraint condition are constructed;The relationship between the objective function and frequency modulation parameter is solved;Optimal wind turbine frequency modulation parameter is solved by particle swarm algorithm.The present application comprehensively considers the frequency minimum point in the process of system frequency first drop and second drop to optimize wind power frequency modulation parameter, and has the advantages of high accuracy, good stability and high efficiency, is conducive to better support system frequency.

Description

Technical Field

[0001] This invention belongs to the field of power system frequency regulation technology, and relates to a method for optimizing wind turbine frequency regulation parameters, specifically a method for optimizing wind power inertia parameters based on particle swarm optimization algorithm. Background Technology

[0002] In recent years, with advancements in science and technology and increasing environmental pressures, the application of renewable energy in power systems has developed rapidly. However, the randomness and volatility of wind power, along with the lack of inertial support in wind turbines, make power systems with a high proportion of wind power less stable than traditional power grids, thus hindering further increases in wind power penetration. To enable wind turbines to possess the inertia of traditional synchronous turbines, advanced converter control strategies are employed to provide what is known as virtual inertia.

[0003] The current virtual inertia control is accomplished by combining virtual inertia control and droop control. However, wind turbines using virtual inertia control cause a secondary frequency drop in the power grid after exiting frequency regulation, resulting in another impact on the grid. Existing tuning methods consider three wind turbine frequency regulation parameters: droop control coefficient, inertia control coefficient, and frequency exit time, with the optimization objective being to minimize the difference between the first and second frequency drops. However, this calculation method, which involves manually adjusting the frequency regulation parameters, is extremely time-consuming and labor-intensive. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies, optimize wind turbine frequency regulation parameters to provide effective frequency support for the system, and improve the tuning speed of wind turbine parameters. A wind turbine inertia parameter optimization method based on particle swarm optimization is proposed, which is achieved through the following steps:

[0005] Step 1: Use simulation software to establish a power system model for primary frequency regulation analysis of integrated inertial control of wind power;

[0006] Step 2: Construct a frequency regulation optimization function with the minimum value of the sum of the maximum deviation of the first frequency drop and the maximum deviation of the second frequency drop under the wind power integrated inertial control mode as the optimization objective, and use it as a standard to measure the frequency regulation performance of wind power.

[0007] The wind power integrated inertial control frequency regulation optimization function used in this invention is:

[0008] (1)

[0009] in, Maximum deviation of frequency drop To maximize the deviation of the second frequency drop, while satisfying the above optimization objective function (1), the following two constraints must also be met:

[0010] a) The per-unit value of the lowest rotor speed of the wind turbine should satisfy the following formula:

[0011]

[0012] b) Exit FM time It should be greater than the time when the lowest point of the frequency drop occurs. :

[0013] .

[0014] Step 3: Use the particle swarm optimization algorithm to solve the objective function and optimize the integrated inertial control parameters of the wind turbine.

[0015] The particle swarm optimization algorithm solves the objective function by including the following steps:

[0016] Step 101: Set the particle swarm size, maximum number of iterations, iteration precision, maximum velocity, and minimum velocity; update the maximum and minimum positions of the particle swarm; initialize the positions and velocities of each particle in the particle swarm;

[0017] Step 102: Construct a fitness function based on the virtual inertia control objective function and calculate the fitness value of each particle in the particle swarm;

[0018] Step 103: Compare the fitness values ​​of each particle in the particle swarm, and update the individual extreme values ​​and the global extreme values;

[0019] Step 104: Determine whether the individual extreme values ​​and global extreme values ​​of each particle in the particle swarm meet the termination condition. If not, update the position and velocity of each particle and then return to step 102; if they meet the condition, proceed to step 105.

[0020] Step 105: Determine the parameters of the integrated inertial control and output the results.

[0021] The function value OF in equation (1) is used as the fitness value in the particle swarm optimization algorithm to determine whether the optimization algorithm terminates. If the termination condition is met, the optimization process ends, and the particle position corresponding to the optimal objective function is the reference value of the first frequency tuning parameter after optimization. If the termination condition is not met, the particle swarm is updated and the next iteration continues.

[0022] As described above, the wind power inertia parameter optimization method based on particle swarm optimization provided by this invention has the following effects:

[0023] The wind turbine inertia parameter optimization method based on particle swarm optimization in this invention introduces intelligent algorithms into the research on primary frequency regulation parameter optimization, solves the inaccuracies of manual calculation and adjustment of parameters, accelerates the speed of wind turbine frequency regulation parameter tuning, and reduces the adverse effects of wind turbines on the power grid during the entire frequency regulation process. Attached Figure Description

[0024] Figure 1 This is a power system model diagram used in the simulation verification of an embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of the solution process of the present invention;

[0026] Figure 3 The image shows the frequency response curves before and after optimization in this invention. Detailed Implementation

[0027] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0028] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings.

[0029] Please see Figure 2 This invention provides a method for optimizing the primary frequency regulation parameters of a doubly-fed wind turbine generator based on the particle swarm optimization algorithm, comprising the following steps:

[0030] Step 1: Establish a power system model for primary frequency regulation analysis using simulation software. In this embodiment, the wind power frequency regulation control strategy is integrated inertial control. The model is constructed as follows: Figure 1 As shown, its parameter settings are shown in Table 1;

[0031] Table 1 Parameters of Multi-Unit Grid-Connected Units

[0032] Unit type Synchronous generator (G1) Doubly fed wind turbine (G2) Unit capacity 900MW 1.5MW Number of units 1 unit 100 units

[0033] Step 2: Construct a frequency regulation optimization function with the minimum value of the sum of the maximum deviation of the first frequency drop and the maximum deviation of the second frequency drop under the wind power integrated inertial control mode as the optimization objective, and use it as a standard to measure the frequency regulation performance of wind power.

[0034] The wind power integrated inertial control frequency regulation optimization function used in this invention is:

[0035] (1)

[0036] in, Maximum deviation of frequency drop To maximize the deviation of the second frequency drop, while satisfying the above optimization objective function (1), the following two constraints must also be met:

[0037] a) The per-unit value of the lowest rotor speed of the wind turbine should satisfy the following formula:

[0038]

[0039] b) Exit FM time It should be greater than the time when the lowest point of the frequency drop occurs. :

[0040] .

[0041] Step 3: Use the particle swarm optimization algorithm to solve the objective function and optimize the integrated inertial control parameters of the wind turbine.

[0042] The particle swarm optimization algorithm solves the objective function by including the following steps:

[0043] Step 101: Set the particle swarm size, maximum number of iterations, iteration precision, maximum velocity, and minimum velocity; update the maximum and minimum positions of the particle swarm; initialize the positions and velocities of each particle in the particle swarm;

[0044] Step 102: Construct a fitness function based on the virtual inertia control objective function and calculate the fitness value of each particle in the particle swarm;

[0045] Step 103: Compare the fitness values ​​of each particle in the particle swarm, and update the individual extreme values ​​and the global extreme values;

[0046] Step 104: Determine whether the individual extreme values ​​and global extreme values ​​of each particle in the particle swarm meet the termination condition. If not, update the position and velocity of each particle and then return to step 102; if they meet the condition, proceed to step 105.

[0047] Step 105: Determine the parameters of the integrated inertial control and output the results.

[0048] The function value OF in equation (1) is used as the fitness value in the particle swarm optimization algorithm to determine whether the optimization algorithm terminates. If the termination condition is met, the optimization process ends, and the particle position corresponding to the optimal objective function is the reference value of the first frequency tuning parameter after optimization. If the termination condition is not met, the particle swarm is updated and the next iteration continues.

[0049] Based on the power system network constructed in this embodiment, a 60-second period is set as the moment of load surge. The results of frequency regulation are recorded respectively: no wind power participation, wind power manually set parameters for frequency regulation, and parameters obtained using the particle swarm optimization algorithm for frequency regulation. Figure 3 As shown.

[0050] Depend on Figure 3 Analysis shows that the frequency deviation of wind power using integrated inertial control meets the national standard range throughout the frequency regulation process. Moreover, the results obtained by this method are better than those of manual frequency regulation. The lowest point of the first frequency drop after optimization is higher than that of manual parameter setting, and the difference between the two frequency drops is also smaller.

[0051] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A wind power inertia parameter optimization method based on particle swarm optimization algorithm, used to suppress the secondary frequency drop in the power grid caused by wind turbines with virtual inertia control exiting frequency regulation, characterized in that: The process includes the following steps: Step 1: Establish a power system model for primary frequency regulation analysis using simulation software; Step 2: Construct a frequency regulation optimization function with the minimum sum of the maximum deviations of the primary and secondary frequency drops under the wind power integrated inertial control mode as the optimization objective, serving as a standard for evaluating wind power frequency regulation performance; Step 3: Use the particle swarm optimization algorithm to solve the objective function and optimize the integrated inertial control parameters of the wind turbine: The wind power integrated inertial control frequency regulation optimization function is as follows: (1) Among them, Maximum deviation of frequency drop To determine the maximum deviation of the second frequency drop, while satisfying the above optimization objective function (1), the following two constraints must also be met: a) The per-unit value of the lowest rotor speed of the wind turbine should satisfy the following formula: b) Exit FM time It should be greater than the time when the lowest point of the frequency drop occurs. : .

2. The wind power inertia parameter optimization method based on particle swarm optimization algorithm according to claim 1, characterized in that: The specific steps for solving the objective function using the particle swarm optimization algorithm are as follows: Step 101: Set the particle swarm size, maximum number of iterations, iteration precision, maximum velocity, and minimum velocity; update the maximum and minimum positions of the particle swarm; initialize the positions and velocities of each particle in the particle swarm; Step 102: Construct a fitness function based on the virtual inertia control objective function and calculate the fitness value of each particle in the particle swarm; Step 103: Compare the fitness values ​​of each particle in the particle swarm and update the individual extreme values ​​and the global extreme values; Step 104: Determine whether the individual extreme values ​​and the global extreme values ​​of each particle in the particle swarm meet the termination condition. If not, update the positions and velocities of each particle and return to Step 102; if they meet, execute Step 105; Step 105: Determine the parameters of the integrated inertial control and output the results.

3. The wind power inertia parameter optimization method based on particle swarm optimization algorithm according to claim 2, characterized in that: The function value OF in equation (1) is used as the fitness value in the particle swarm optimization algorithm to determine whether the optimization algorithm terminates. If the termination condition is met, the optimization process ends, and the particle position corresponding to the optimal objective function is the reference value of the first frequency tuning parameter after optimization. If the termination condition is not met, the particle swarm is updated and the next iteration continues.

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