A Broadband Impedance Identification Method and System for New Energy Power Stations Based on an Improved Tuna Swarm Optimization Algorithm

Abstract

This invention discloses a broadband impedance identification method and system for new energy power plants based on an improved tuna swarm optimization algorithm, belonging to the field of broadband oscillation technology. The method includes: establishing a mathematical model of the wind and solar power output and energy storage system; performing impedance scanning on the new energy units using a voltage perturbation injection method, collecting the three-phase voltage and three-phase current at the ports of the tested units to obtain the impedance characteristics of the new energy units; constructing a tuna swarm optimization algorithm and improving the algorithm by introducing a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism; identifying the parameters of the broadband impedance model of the new energy power plant using the improved tuna swarm optimization algorithm; applying the identified parameters to the simulation model, and searching for the optimal parameter combination through algorithm iteration to obtain the accurate values ​​of each parameter to be identified. This invention achieves online identification of broadband impedance parameters of new energy power plants without requiring prior knowledge of the detailed system structure and control parameters.

Description

Technical Field

[0001] This invention relates to the field of broadband oscillation technology, and in particular to a broadband impedance identification method and system for new energy power plants based on an improved tuna swarm optimization algorithm. Background Technology

[0002] With the large-scale grid connection of renewable energy sources such as wind and solar power, power systems with a high proportion of new energy integration exhibit the "dual high" characteristics of strong uncertainty and high power electronics. In this "dual high" power system, the interaction between the grid and the converter can easily induce broadband oscillations ranging from several hertz to several kilohertz, seriously threatening the safe and stable operation of the grid. Impedance analysis is an important means of analyzing the stability of converter-grid interconnection systems. It assesses system stability by obtaining the impedance characteristics of the grid-connected inverter and the grid. Since the impedance characteristics of the power system under actual operating conditions differ from those under offline conditions, online measurement of the grid impedance is necessary to accurately assess the system stability under operating conditions.

[0003] Traditional online power grid impedance measurement methods generally suffer from slow convergence speed, weak anti-interference capability, and sensitivity to noise and operating condition fluctuations, making it difficult to achieve high-precision, fast, and stable parameter identification in "high-temperature and high-power" scenarios. If the identification results deviate from the true values, the accuracy of the impedance model will be reduced, thereby affecting the reliability of broadband oscillation early warning and stability criteria. Some existing technologies use the tuna swarm optimization algorithm for parameter identification, but the traditional tuna swarm optimization algorithm has the drawbacks of slow convergence speed and easy getting trapped in local optima, limiting its application effect in complex power grid scenarios.

[0004] Therefore, there is an urgent need for a broadband impedance identification method for new energy power plants that has strong anti-interference capabilities, high identification accuracy, and can adapt to the operating characteristics of "high-voltage and high-efficiency" power systems. Summary of the Invention

[0005] The purpose of this invention is to provide a broadband impedance identification method and system for new energy power stations based on an improved tuna swarm optimization algorithm, so as to solve the problems mentioned in the background art.

[0006] The first aspect of this invention is achieved through the following technical solution: A broadband impedance identification method for new energy power plants based on an improved tuna swarm optimization algorithm, the method comprising the following steps: Step S1: Establish a mathematical model of the wind and solar power output and energy storage system; Step S2: Use the voltage perturbation injection method to perform impedance scanning on the new energy unit, collect the three-phase voltage and three-phase current at the port of the unit under test, and obtain the impedance characteristics of the new energy unit. Step S3: Construct a tuna swarm optimization algorithm, and improve the tuna swarm optimization algorithm by introducing a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism. The improved algorithm has global search capability in the early stage of iteration and local convergence capability in the later stage of iteration, and increases population diversity through the Gaussian mutation mechanism. Step S4: Identify the parameters of the broadband impedance model of the new energy power station using the improved tuna swarm optimization algorithm; Step S5: Apply the identified parameters to the constructed simulation model for analysis, and search for the optimal parameter combination through algorithm iteration to obtain the accurate values ​​of each parameter to be identified.

[0007] Furthermore, step S1 specifically includes: Establish a photovoltaic power output model, in which the photovoltaic output active power for:

[0008] in, This refers to the rated light intensity of photovoltaics. This refers to the rated capacity of the photovoltaic system. Photovoltaic output reactive power for:

[0009] in, The power factor angle is used; the probability distribution of light intensity is described by the Beta distribution model, and the calculation formula is as follows:

[0010]

[0011]

[0012] Where Γ is the gamma function; This represents the maximum light intensity. α and β For shape parameters; μ This represents the average light intensity. σ The standard deviation of light intensity; cumulative distribution function of light intensity probability for:

[0013] Light intensity E The output of photovoltaic power generation can be expressed as:

[0014]

[0015]

[0016] in, S This represents the sum of the areas of the photovoltaic panels; η The efficiency of converting light into electricity; The conversion efficiency of a monocrystalline silicon solar cell; The light intensity at which the efficiency of photo-to-electric conversion is saturated; The wind power output model is established, and the calculation formula is as follows:

[0017] in, c It is a scale parameter; k These are shape parameters; v It is the average wind speed; The cumulative distribution function is:

[0018] The relationship between the output power of a wind power system and wind speed is expressed as follows:

[0019] in, This refers to the rated speed of the fan; The cut-in speed of the wind turbine; This refers to the cut-out speed of the fan; This refers to the rated output active power of the fan. An energy storage system model is established, employing a comprehensive energy storage model to simulate the power regulation characteristics of the energy storage device. The energy storage model is represented as follows:

[0020]

[0021] in, The charging power for energy storage; The discharge power of the stored energy; for t The charging coefficient for continuous energy storage; for t The charging coefficient for continuous energy storage; , These represent the maximum and minimum values ​​of the energy storage charging power, respectively. , These represent the maximum and minimum values ​​of the energy storage discharge power, respectively. For energy storage systems in t Battery level at any given time; , These represent the maximum and minimum values ​​of the energy storage system capacity. The charging efficiency ratio of the energy storage system; The discharge efficiency ratio of the energy storage system; Mutual exclusion constraints and charge / discharge coefficient constraints can be expressed as: .

[0022] Furthermore, step S2 specifically includes: When the system is in steady-state operation, a voltage disturbance of a specific frequency is injected. Measure the generated current response Then the impedance at that frequency is:

[0023] Preset measurement cycle T When the new energy power generation unit is running at each of its preset operating points, a positive-sequence voltage disturbance corresponding to each first preset frequency is injected into the new energy power generation unit through a disturbance injection device. At the same time, the first three-phase voltage signal and the first three-phase current signal at each preset measurement point are collected. The positive-sequence voltage disturbance is injected for a preset duration. T d ( T d < T / 2), stop injecting positive sequence voltage disturbance; Within the preset measurement period T Within the system, when the new energy power generation unit is operating at each preset operating point, a negative sequence voltage disturbance with a frequency corresponding to each second preset frequency is injected into the new energy power generation unit using a disturbance injection device. Simultaneously, the second and third phase voltage signals and second and third phase current signals at each preset measurement point are collected until the continuous injection duration of the negative sequence voltage disturbance reaches a certain value. T d At that time, the injection of negative sequence voltage disturbance is stopped.

[0024] Furthermore, the improvement of the tuna swarm optimization algorithm in step S3 specifically includes: A tuna swarm optimization algorithm framework was constructed, and population initialization was performed. The tuna swarm optimization algorithm generates an initial population randomly, as follows:

[0025] Where rand is a random number in the interval [0, 1]; n This represents the tuna population size. , These are the upper and lower bounds of the search space for the optimization algorithm, respectively. For the first i One initial tuna individual; Based on the spiral swimming characteristics of tuna during predation, a mathematical model is used to describe this spiral predation behavior:

[0026]

[0027]

[0028]

[0029]

[0030] in, a Represents a constant; t max This represents the maximum number of iterations. , Weighting coefficients used to control individual movement; , They represent the first i , i- 1 t Next iteration individual. Indicates the first t The best individual in the next iteration of the population; The parabolic foraging strategy is adopted, and the specific mathematical model is as follows:

[0031]

[0032] in, F It can be -1 or 1; An improved tuna swarm optimization algorithm is introduced by incorporating a dynamic nonlinear weight adjustment strategy. The updated position representation after the improvement is as follows:

[0033]

[0034]

[0035] in, μ This is a non-linear weighting adjustment factor; The improved weighting coefficients; When the number of algorithm iterations exceeds the preset time parameter, a Gaussian mutation mechanism is introduced at the position of the current optimal tuna individual to obtain the mutated individual. , represented as:

[0036] in, This represents a Gaussian distributed random vector with a mean of 0 and a standard deviation of 1. Individuals that have mutated within a tuna population; The time parameter for the start of Gaussian mutation; Gau The Gaussian mutation threshold is related to the number of iterations.

[0037] Furthermore, the dynamic nonlinear weight adjustment strategy uses a larger weight factor in the early stage of the iteration than in the later stage.

[0038] Furthermore, the Gaussian mutation mechanism increases the diversity of population individual positions by applying random perturbations to the current best individual.

[0039] Furthermore, step S4 specifically includes: Voltage and current signals from the ports of new energy power stations are collected over a wide frequency range. Interference is suppressed through signal preprocessing, and then the improved tuna swarm optimization algorithm is used to fit the impedance model to obtain impedance parameters over a wide frequency range.

[0040] Furthermore, the method also includes: using a benchmark function to compare and test the improved tuna swarm optimization algorithm with the classic tuna swarm optimization algorithm, and obtaining the comparison results of the improved algorithm in terms of accuracy and stability by comparing the optimization convergence curves.

[0041] Furthermore, step S5 specifically includes: In the constructed simulation model, the reference model is compared with the model identified by the improved tuna swarm optimization algorithm; the generalized Nyquist curves of the two are plotted, and the system is determined to be stable when the overlap between the feature value trajectory of the reference model and the feature value trajectory of the identified model meets the preset conditions.

[0042] The second aspect of the present invention is achieved through the following technical solution: A broadband impedance identification system for new energy power plants based on an improved tuna swarm optimization algorithm is disclosed. This system is used to execute the broadband impedance identification method for new energy power plants based on the improved tuna swarm optimization algorithm proposed in the first aspect. The system includes: Photovoltaic power generation modules are used to convert solar energy into electrical energy; The wind turbine output module is used to convert wind energy into electrical energy; Energy storage systems are used to store and release electrical energy; The data acquisition module is used to acquire the three-phase voltage and three-phase current signals generated by the injection of voltage disturbances at the port of the new energy unit. The impedance identification module is equipped with an improved tuna swarm optimization algorithm. The improved tuna swarm optimization algorithm introduces a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism on the basis of the traditional tuna swarm optimization algorithm. The impedance identification module is used to receive the signal from the data acquisition module and use the improved tuna swarm optimization algorithm to identify the broadband impedance parameters of the new energy power station and output the impedance model parameters. The model analysis module is used to substitute the identification model parameters output by the impedance identification module into the simulation model to obtain the accurate values ​​of each parameter to be identified and to perform stability analysis.

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: This invention establishes a mathematical model of wind and solar power output and energy storage systems, providing a model foundation for impedance identification that conforms to the characteristics of "high-voltage and high-efficiency" power systems. A voltage perturbation injection method is used to achieve non-intrusive extraction of impedance characteristics. By introducing a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism, the traditional tuna swarm optimization algorithm is improved. This allows the algorithm to have a large weight factor in the early stages of iteration for global search, while the weight factor gradually decreases in the later stages for refined local search. Simultaneously, Gaussian mutation applies random perturbations to the optimal individuals, increasing population diversity and overcoming the shortcomings of the traditional tuna swarm optimization algorithm, such as slow convergence speed and susceptibility to local optima. Applying the improved algorithm to impedance parameter identification enables high-precision online identification without prior knowledge of the system's detailed structure and control parameters. The identification results can provide reliable parameter support for broadband oscillation early warning, stability analysis, and safety control strategy formulation for new energy grid-connected systems. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 The flowchart of a broadband impedance identification method for new energy power stations based on an improved tuna swarm optimization algorithm provided by the present invention is shown below.

[0046] Figure 2 This is a schematic diagram of the wind-solar-storage complementary power generation structure in this invention.

[0047] Figure 3 This is a schematic diagram of the improved tuna swarm optimization algorithm in this invention.

[0048] Figure 4 This is a schematic diagram comparing the convergence curves of the algorithm's baseline function optimization in this invention.

[0049] Figure 5 This is a schematic diagram of the system stability analysis results in this invention.

[0050] Figure 6 This is a schematic diagram of the admittance curve fitting results in this invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0052] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0053] It should be understood that the invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0054] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising” and / or “including,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0055] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0056] Example 1 See Figures 1-4A broadband impedance identification method for new energy power stations based on an improved tuna swarm optimization algorithm includes the following steps: Step 1: Establish a mathematical model for wind and solar power output and energy storage system. A schematic diagram of the wind-solar-storage complementary power generation structure is shown below. Figure 2 As shown.

[0057] Establish a photovoltaic power output model, in which the photovoltaic output active power for:

[0058] in, This refers to the rated light intensity of photovoltaics. This refers to the rated capacity of the photovoltaic system. Photovoltaic output reactive power for:

[0059] in, The power factor angle is used; the probability distribution of light intensity is described by the Beta distribution model, and the calculation formula is as follows:

[0060]

[0061]

[0062] Where Γ is the gamma function; This represents the maximum light intensity. α and β For shape parameters; μ This represents the average light intensity. σ The standard deviation of light intensity; cumulative distribution function of light intensity probability for:

[0063] Light intensity E The output of photovoltaic power generation can be expressed as:

[0064]

[0065]

[0066] in, S This represents the sum of the areas of the photovoltaic panels; η The efficiency of converting light into electricity; The conversion efficiency of a monocrystalline silicon solar cell; The light intensity at which the efficiency of photo-to-electric conversion is saturated; The wind power output model is established, and the calculation formula is as follows:

[0067] in, c It is a scale parameter; k These are shape parameters; v It is the average wind speed; The cumulative distribution function is:

[0068] The relationship between the output power of a wind power system and wind speed is expressed as follows:

[0069] in, This refers to the rated speed of the fan; The cut-in speed of the wind turbine; This refers to the cut-out speed of the fan; This refers to the rated output active power of the fan. An energy storage system model is established, employing a comprehensive energy storage model to simulate the power regulation characteristics of the energy storage device. The energy storage model is represented as follows:

[0070]

[0071] in, The charging power for energy storage; The discharge power of the stored energy; for t The charging coefficient for continuous energy storage; for t The charging coefficient for continuous energy storage; , These represent the maximum and minimum values ​​of the energy storage charging power, respectively. , These represent the maximum and minimum values ​​of the energy storage discharge power, respectively. For energy storage systems in t Battery level at any given time; , These represent the maximum and minimum values ​​of the energy storage system capacity. The charging efficiency ratio of the energy storage system; The discharge efficiency ratio of the energy storage system; Mutual exclusion constraints and charge / discharge coefficient constraints can be expressed as: .

[0072] Step S2: Using the voltage perturbation injection method, impedance scanning is performed on the new energy unit to collect the three-phase voltage and three-phase current at the port of the unit under test, and the impedance characteristics of the new energy unit are obtained as follows: When the system is in steady-state operation, a voltage disturbance of a specific frequency is injected. Measure the generated current response Then the impedance at that frequency is:

[0073] Preset measurement cycle T When the new energy power generation unit is running at each of its preset operating points, a positive-sequence voltage disturbance corresponding to each first preset frequency is injected into the new energy power generation unit through a disturbance injection device. At the same time, the first three-phase voltage signal and the first three-phase current signal at each preset measurement point are collected. The positive-sequence voltage disturbance is injected for a preset duration. T d ( T d < T / 2), stop injecting positive sequence voltage disturbance; Within the preset measurement period T Within the system, when the new energy power generation unit is operating at each preset operating point, a negative sequence voltage disturbance with a frequency corresponding to each second preset frequency is injected into the new energy power generation unit using a disturbance injection device. Simultaneously, the second and third phase voltage signals and second and third phase current signals at each preset measurement point are collected until the continuous injection duration of the negative sequence voltage disturbance reaches Td, at which point the injection of the negative sequence voltage disturbance stops. This allows for the acquisition of wideband impedance characteristic curves of the converter under different frequencies and operating conditions.

[0074] Step S3: Construct a tuna swarm optimization algorithm and improve the algorithm by introducing a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism.

[0075] This paper analyzes the principle of the improved tuna swarm optimization algorithm. To address the shortcomings of the traditional tuna swarm optimization algorithm, such as slow convergence speed and easy getting trapped in local optima, the algorithm's global search capability and convergence speed are improved by introducing a nonlinear weight adjustment strategy, optimizing the population initialization method and the predation behavior update rule, and avoiding premature convergence.

[0076] (1) Tuna Swarm Optimization Algorithm The core framework of the tuna swarm optimization algorithm was constructed, and its key parts are as follows: 1) Population initialization The tuna swarm optimization algorithm generates an initial population randomly, which can be represented as:

[0077] Where rand is a random number in the interval [0, 1]; n This represents the tuna population size. , These are the upper and lower bounds of the search space for the optimization algorithm, respectively. For the first i The initial individual tuna.

[0078] 2) Spiral foraging strategy Based on the spiral swimming characteristics of tuna during predation, a mathematical model is used to describe this spiral predation behavior:

[0079]

[0080]

[0081]

[0082]

[0083] in, a Represents a constant; t max This represents the maximum number of iterations. , Weighting coefficients used to control individual movement; , They represent the first i , i- 1 t Next iteration individual. Indicates the first t The best individual in the next iteration of the population.

[0084] 3) Parabolic foraging strategy Tuna exhibit a parabolic trajectory during foraging. When they cannot find food using this trajectory, they will conduct a localized search around themselves. These two foraging methods occur simultaneously, and the algorithm design assumes a 50% probability for each method. The specific mathematical model is as follows:

[0085]

[0086] in, F It can be -1 or 1.

[0087] (2) Improved tuna swarm optimization algorithm 1) Dynamic nonlinear weight adjustment strategy Tuna swarm optimization algorithm , This results in weak global search capabilities in the early stages and low local convergence efficiency in the later stages. To avoid this problem, a large factor is used in the early stages to enhance the exploration and avoid local optima. The factor is then gradually reduced in the later stages to improve convergence accuracy. The improved update location is:

[0088]

[0089]

[0090] in, μ This is a non-linear weighting adjustment factor; These are the improved weighting coefficients.

[0091] 2) Gaussian mutation Introducing a Gaussian-distributed random perturbation increases population diversity and improves the population's search capability. The Gaussian mutation applied to the position of the current optimal tuna individual can be represented as:

[0092] in, This represents a Gaussian distributed random vector with a mean of 0 and a standard deviation of 1. Individuals that have mutated within a tuna population; The time parameter for the start of Gaussian mutation; Gau The Gaussian mutation threshold is related to the number of iterations. The flowchart of the improved tuna swarm optimization algorithm is as follows: Figure 3 As shown.

[0093] Step S4: Use the improved tuna swarm optimization algorithm to identify the parameters of the broadband impedance model of the new energy power station.

[0094] To address the shortcomings of traditional impedance modeling methods, such as the need to obtain detailed system structure and control parameters in advance, the complexity of the modeling process, and the inconvenience of online application, impedance analysis is used to study the broadband oscillation problem of "high-voltage and high-efficiency" power systems.

[0095] Voltage and current signals from a wide frequency range at the station port are collected. Interference is suppressed through signal preprocessing, and then the impedance model is fitted using an improved tuna swarm optimization algorithm. Finally, the impedance parameters in a wide frequency range are accurately obtained, providing a basis for system oscillation early warning and stability analysis.

[0096] To evaluate the effectiveness of the improved algorithm, a benchmark function is used to compare the improved tuna swarm optimization algorithm with the classic tuna swarm optimization algorithm. The function is defined as follows: The theoretical optimal value is 0. The convergence curve of the benchmark function optimization is shown in the figure below. Figure 4As shown, the improved tuna swarm optimization algorithm surpasses the classic tuna swarm optimization algorithm in both accuracy and stability.

[0097] Step S5: Apply the identified parameters to the constructed simulation model for analysis, and search for the optimal parameter combination through algorithm iteration to obtain the accurate values ​​of each parameter to be identified.

[0098] The improved tuna swarm optimization algorithm was applied to the constructed simulation model for analysis. Through iterative searching of the optimal parameter combination, the precise values ​​of each parameter to be identified were obtained. This method, based on the original tuna swarm optimization algorithm, improves the algorithm's global search capability and convergence speed by introducing a nonlinear weight adjustment strategy, optimizing the population initialization method, and refining the predation behavior update rules. It avoids premature convergence and overcomes the technical difficulties of slow convergence and susceptibility to local optima inherent in traditional tuna swarm optimization algorithms. It also solves the technical problems of traditional impedance modeling methods, such as reliance on detailed system parameters, complex modeling, and difficulty in online application, significantly improving the accuracy and efficiency of impedance identification for new energy power plants.

[0099] Example 2 A broadband impedance identification system for new energy power stations based on an improved tuna swarm optimization algorithm, the system comprising: Photovoltaic power generation modules are used to convert solar energy into electrical energy; The wind turbine output module is used to convert wind energy into electrical energy; Energy storage systems are used to store and release electrical energy; The data acquisition module is used to acquire the three-phase voltage and three-phase current signals generated by the injection of voltage disturbances at the port of the new energy unit. The impedance identification module is equipped with an improved tuna swarm optimization algorithm. The improved tuna swarm optimization algorithm introduces a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism on the basis of the traditional tuna swarm optimization algorithm. The impedance identification module is used to receive the signal from the data acquisition module and use the improved tuna swarm optimization algorithm to identify the broadband impedance parameters of the new energy power station and output the impedance model parameters. The model analysis module is used to substitute the identification model parameters output by the impedance identification module into the simulation model to obtain the accurate values ​​of each parameter to be identified and to perform stability analysis.

[0100] Example 3 Figure 5-6 As shown, the constructed simulation model compares the generalized Nyquist curves of the reference model and the identified model. For example, the curves... Figure 5 As shown, the feature value trajectory of the reference model is basically consistent with the feature value trajectory of the identification model, and from Figure 5The results show that the system is stable, proving that the proposed method has a certain degree of disturbance rejection capability. To verify the effectiveness and practicality of the invention, the admittance characteristic points were obtained by frequency sweeping using a hardware-in-the-loop simulation platform and fitted with the proposed method. The fitted curve is shown in the figure. Figure 6 As shown in the figure, the fitting results indicate that the curve obtained based on the measured data still possesses high accuracy in the hardware-in-the-loop simulation experimental platform. Although the fitted curve deviates somewhat from the reference mathematical model curve due to the actual measurement error of admittance, the overall trend is basically consistent, indicating that the method proposed in this invention has good anti-disturbance capability and meets the requirements of this invention.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A broadband impedance identification method for new energy power stations based on an improved tuna swarm optimization algorithm, characterized in that, The method includes the following steps: Step S1: Establish a mathematical model of the wind and solar power output and energy storage system; Step S2: Use the voltage perturbation injection method to perform impedance scanning on the new energy unit, collect the three-phase voltage and three-phase current at the port of the unit under test, and obtain the impedance characteristics of the new energy unit. Step S3: Construct a tuna swarm optimization algorithm, and improve the tuna swarm optimization algorithm by introducing a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism. The improved algorithm has global search capability in the early stage of iteration and local convergence capability in the later stage of iteration, and increases population diversity through the Gaussian mutation mechanism. Step S4: Identify the parameters of the broadband impedance model of the new energy power station using the improved tuna swarm optimization algorithm; Step S5: Apply the identified parameters to the constructed simulation model for analysis, and search for the optimal parameter combination through algorithm iteration to obtain the accurate values ​​of each parameter to be identified.

2. The broadband impedance identification method for new energy power stations based on the improved tuna swarm optimization algorithm according to claim 1, characterized in that, Step S1 specifically includes: Establish a photovoltaic power output model, in which the photovoltaic output active power for: in, This refers to the rated light intensity of photovoltaics. This refers to the rated capacity of the photovoltaic system. Photovoltaic output reactive power for: in, The power factor angle is used; the probability distribution of light intensity is described by the Beta distribution model, and the calculation formula is as follows: Where Γ is the gamma function; This represents the maximum light intensity. α and β For shape parameters; μ This represents the average light intensity. σ The standard deviation of light intensity; cumulative distribution function of light intensity probability for: Light intensity E The output of photovoltaic power generation can be expressed as: in, S This represents the sum of the areas of the photovoltaic panels; η The efficiency of converting light into electricity; The conversion efficiency of a monocrystalline silicon solar cell; The light intensity at which the efficiency of photo-to-electric conversion is saturated; The wind power output model is established, and the calculation formula is as follows: in, c It is a scale parameter; k These are shape parameters; v It is the average wind speed; The cumulative distribution function is: The relationship between the output power of a wind power system and wind speed is expressed as follows: in, This refers to the rated speed of the fan; The cut-in speed of the wind turbine; This refers to the cut-out speed of the fan; This refers to the rated output active power of the fan. An energy storage system model is established, employing a comprehensive energy storage model to simulate the power regulation characteristics of the energy storage device. The energy storage model is represented as follows: in, The charging power for energy storage; The discharge power of the stored energy; for t The charging coefficient for continuous energy storage; for t The charging coefficient for continuous energy storage; , These represent the maximum and minimum values ​​of the energy storage charging power, respectively. , These represent the maximum and minimum values ​​of the energy storage discharge power, respectively. For energy storage systems in t Battery level at any given time; , These represent the maximum and minimum values ​​of the energy storage system capacity. The charging efficiency ratio of the energy storage system; The discharge efficiency ratio of the energy storage system; Mutual exclusion constraints and charge / discharge coefficient constraints can be expressed as: 。 3. The broadband impedance identification method for new energy power stations based on the improved tuna swarm optimization algorithm according to claim 1, characterized in that, Step S2 specifically includes: When the system is in steady-state operation, a voltage disturbance of a specific frequency is injected. Measure the generated current response Then the impedance at that frequency is: Preset measurement cycle T When the new energy power generation unit is running at each of its preset operating points, a positive-sequence voltage disturbance corresponding to each first preset frequency is injected into the new energy power generation unit through a disturbance injection device. At the same time, the first three-phase voltage signal and the first three-phase current signal at each preset measurement point are collected. The positive-sequence voltage disturbance is injected for a preset duration. T d ( T d < T / 2), stop injecting positive sequence voltage disturbance; Within the preset measurement period T Within the system, when the new energy power generation unit is operating at each preset operating point, a negative sequence voltage disturbance with a frequency corresponding to each second preset frequency is injected into the new energy power generation unit using a disturbance injection device. Simultaneously, the second and third phase voltage signals and second and third phase current signals at each preset measurement point are collected until the continuous injection duration of the negative sequence voltage disturbance reaches a certain value. T d At that time, the injection of negative sequence voltage disturbance is stopped.

4. The broadband impedance identification method for new energy power stations based on the improved tuna swarm optimization algorithm according to claim 1, characterized in that, The improvement of the tuna swarm optimization algorithm in step S3 specifically includes: A tuna swarm optimization algorithm framework was constructed, and population initialization was performed. The tuna swarm optimization algorithm generates an initial population randomly, as follows: Where rand is a random number in the interval [0, 1]; n This represents the tuna population size. , These are the upper and lower bounds of the search space for the optimization algorithm, respectively. For the first i One initial tuna individual; Based on the spiral swimming characteristics of tuna during predation, a mathematical model is used to describe this spiral predation behavior: in, a Represents a constant; t max This represents the maximum number of iterations. , Weighting coefficients used to control individual movement; , They represent the first i , i- 1 t Next iteration individual. Indicates the first t The best individual in the next iteration of the population; The parabolic foraging strategy is adopted, and the specific mathematical model is as follows: in, F It can be -1 or 1; An improved tuna swarm optimization algorithm is introduced by incorporating a dynamic nonlinear weight adjustment strategy. The updated position representation after the improvement is as follows: in, μ This is a non-linear weighting adjustment factor; The improved weighting coefficients; When the number of algorithm iterations exceeds a preset time parameter, a Gaussian mutation mechanism is introduced at the position of the current optimal tuna individual to obtain the mutated individual. , is represented as: in, This represents a Gaussian distributed random vector with a mean of 0 and a standard deviation of 1. Individuals that have mutated within the tuna population; The time parameter for the start of Gaussian mutation; Gau The Gaussian mutation threshold is related to the number of iterations.

5. A broadband impedance identification method for new energy power stations based on an improved tuna swarm optimization algorithm according to claim 4, characterized in that, The dynamic nonlinear weight adjustment strategy uses a larger weight factor in the early stage of the iteration than in the later stage.

6. The broadband impedance identification method for new energy power stations based on the improved tuna swarm optimization algorithm according to claim 4, characterized in that, The Gaussian mutation mechanism increases the diversity of individual positions in the population by applying random perturbations to the current best individual.

7. A broadband impedance identification method for new energy power stations based on an improved tuna swarm optimization algorithm according to claim 1, characterized in that, Step S4 specifically includes: Voltage and current signals from the ports of new energy power stations are collected over a wide frequency range. Interference is suppressed through signal preprocessing, and then the improved tuna swarm optimization algorithm is used to fit the impedance model to obtain impedance parameters over a wide frequency range.

8. A broadband impedance identification method for new energy power stations based on an improved tuna swarm optimization algorithm according to claim 1, characterized in that, The method further includes: using a benchmark function to compare the improved tuna swarm optimization algorithm with the classic tuna swarm optimization algorithm, and obtaining the comparison results of the improved algorithm in terms of accuracy and stability by comparing the optimization convergence curves.

9. A broadband impedance identification method for new energy power stations based on an improved tuna swarm optimization algorithm according to claim 1, characterized in that, Step S5 specifically includes: In the constructed simulation model, the reference model is compared with the model identified by the improved tuna swarm optimization algorithm; the generalized Nyquist curves of the two are plotted, and the system is determined to be stable when the overlap between the feature value trajectory of the reference model and the feature value trajectory of the identified model meets the preset conditions.

10. A broadband impedance identification system for new energy power stations based on an improved tuna swarm optimization algorithm, characterized in that, The aforementioned system is used to execute a broadband impedance identification method for new energy power plants based on an improved tuna swarm optimization algorithm. The system includes: Photovoltaic power generation modules are used to convert solar energy into electrical energy; The wind turbine output module is used to convert wind energy into electrical energy; Energy storage systems are used to store and release electrical energy; The data acquisition module is used to acquire the three-phase voltage and three-phase current signals generated by the injection of voltage disturbances at the port of the new energy unit. The impedance identification module is equipped with an improved tuna swarm optimization algorithm. The improved tuna swarm optimization algorithm introduces a dynamic nonlinear weight adjustment strategy and a Gaussian mutation mechanism on the basis of the traditional tuna swarm optimization algorithm. The impedance identification module is used to receive the signal from the data acquisition module and use the improved tuna swarm optimization algorithm to identify the broadband impedance parameters of the new energy power station and output the impedance model parameters. The model analysis module is used to substitute the identification model parameters output by the impedance identification module into the simulation model to obtain the accurate values ​​of each parameter to be identified, and to perform stability analysis.

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