A method for suppressing oscillation of PMSG wind power system connected to weak power grid

Abstract

The embodiment of the present application proposes an oscillation suppression method for a grid-type PMSG wind power system connected to a weak power grid. By establishing a Phillips-Heffron-like model of the active-frequency link of the grid-type PMSG wind power system, the influence of key parameters in the system closed-loop transfer function on the subsynchronous oscillation mode is analyzed. In combination with a quantum particle swarm algorithm, an additional damping controller is used to suppress subsynchronous oscillations by maximizing the total damping coefficient of the system. The present invention includes obtaining basic system parameters and weak power grid data, establishing a model and analyzing it, collecting real-time data and identifying anomalies, optimizing control parameters using fault data and a quantum particle swarm algorithm, and monitoring and optimizing the execution effect of the additional damping controller. The present invention effectively improves the stability and grid-connected capability of the grid-type PMSG wind power system in a weak power grid environment.

Description

Technical Field

[0001] The present application relates to the technical field of oscillation data processing, and in particular to a method for analyzing and suppressing subsynchronous oscillations of a grid-connected PMSG wind power system and a weak power grid. Background Art

[0002] The PMSG wind power system is an efficient and stable wind power generation system. The PMSG wind power system simulates the rotor motion equation of the synchronous generator set to directly control its output voltage amplitude and phase, thereby supporting the grid frequency and voltage stability, and providing inertia and damping support for the PMSG wind power system. The PMSG wind power system has the ability to operate in an isolated island, and can flexibly switch between grid-connected and independent operation. It is suitable for a high-proportion new energy power system with weak strength and low inertia.

[0003] As an important component of current clean energy, wind power generation has a gradually increasing penetration rate in the power system. With the development of new energy power systems, the proportion of power electronic systems has gradually increased, causing the power grid to gradually change from a strong power grid to a weak power grid, resulting in a decrease in the system inertia level. The virtual synchronous machine control in the grid-type control can simulate the dynamic characteristics of traditional motors and provide good damping and inertia support. However, the virtual synchronous machine control connected to the grid through a series compensation capacitor may also generate the risk of subsynchronous oscillation. In order to solve the subsynchronous oscillation problem of a grid-type PMSG wind turbine, the present invention provides an oscillation suppression method for a grid-type PMSG wind power system connected to a weak power grid. Summary of the invention

[0004] The embodiment of the present application proposes an oscillation suppression method for a grid-type PMSG wind power system connected to a weak power grid, which is used to solve the subsynchronous oscillation problem of a grid-type PMSG wind turbine.

[0005] The present invention provides an oscillation suppression method for a grid-type PMSG wind power system connected to a weak power grid, comprising:

[0006] Step S101, obtaining basic parameters of a grid-type PMSG wind power system and basic data of a weak power grid, wherein the basic data of the weak power grid includes parameter data of the weak power grid, operation data of the weak power grid and fault data of the weak power grid;

[0007] Step S102, using the basic parameters of the grid-type PMSG wind power system and the parameter data of the weak power grid, a Phillips-Heffron-like model is established, and the second-order oscillation equation of the active power-frequency link is derived according to the basic parameters of the grid-type PMSG wind power system and the parameter data of the weak power grid and the dynamic process of the grid-type PMSG wind power system, and the properties of the Phillips-Heffron model are analyzed by using the second-order oscillation equation of the active power-frequency link. The properties of the Phillips-Heffron model analyzed by using the second-order oscillation equation of the active power-frequency link include determining whether the grid-type PMSG wind power system is in a dominant subsynchronous oscillation mode;

[0008] Step S103, collecting real-time data of the wind power system from various sensors installed in the grid-type PMSG wind power system, the real-time data of the wind power system including active power of the wind power system, reactive power of the wind power system, voltage of the wind power system, and current of the wind power system, identifying abnormal data in the real-time data of the wind power system, and obtaining the identified real-time data of the wind power system;

[0009] Step S104, using the fault data of the weak power grid to analyze the response characteristics of the PMSG wind power system to the fault, obtaining the fault data analysis results, and optimizing the control parameters of the PMSG wind power system and the weak power grid using the quantum particle swarm algorithm according to the fault data analysis results to obtain the maximum total damping coefficient of the system;

[0010] Step S105, substitute the maximized system total damping coefficient into the additional damping controller in the grid-type PMSG wind power system, the additional damping controller executes the maximized system total damping coefficient to suppress subsynchronous oscillations, monitor the operation data of the grid-type PMSG wind power system after the additional damping controller executes the maximized system total damping coefficient, obtain the monitoring data to be identified, compare the monitoring data to be identified with the preset standard value range, obtain comparison error data, if the comparison error data exceeds the maximum value of the preset error range, optimize the maximized system total damping coefficient until the additional damping controller executes the optimized maximized system total damping coefficient and it meets the preset standard value range.

[0011] Furthermore, the oscillation suppression method for the grid-type PMSG wind power system connected to a weak power grid according to the present invention, step S101 includes:

[0012] The basic parameters of the grid-type PMSG wind power system include rated power, rated voltage, rated frequency, rotor inertia, stator resistance, inductance, converter type, switching frequency parameters and control parameters;

[0013] The parameter data of weak power grid include grid impedance, short-circuit capacity and line parameters;

[0014] The operational data of weak power grids include voltage, current, frequency and load changes;

[0015] The fault data of weak power grid includes historical fault records, fault types and fault duration.

[0016] Furthermore, the oscillation suppression method for the grid-type PMSG wind power system connected to a weak power grid according to the present invention, step S102 includes:

[0017] Using the basic parameters of the PMSG wind power system and the parameter data of the weak power grid, combined with the dynamic process of the PMSG wind power system, the second-order oscillation equation of the active power-frequency link is derived. The dynamic process of the PMSG wind power system includes the output power and speed changes.

[0018] In the Phillips-Heffron model, the stability of the system is usually judged by the discriminant Δ=D2-4J*K, where D, J, and K are the damping coefficient, inertia coefficient, and stiffness coefficient of the equation respectively. For the active-frequency link of the grid-type PMSG wind power system, its discriminant is calculated. When the total damping coefficient is less than 0, the grid-type PMSG wind power system is in an unstable state and subsynchronous oscillation occurs.

[0019] In the framework of the Phillips-Heffron model, the damping path between the PMSG wind power system and the generator and transmission line is analyzed, and the damping coefficients on the path are compared. When the total damping coefficient is less than 0, the system oscillates, and it can be considered that the wind power system dominates the subsynchronous oscillation mode.

[0020] Furthermore, the oscillation suppression method for the grid-type PMSG wind power system connected to a weak power grid according to the present invention, step S103 includes:

[0021] Real-time data of the wind power system from various sensors installed in the PMSG wind power system, including active power, reactive power, voltage and current of the PMSG wind power system;

[0022] The collected wind power system real-time data of the grid-type PMSG wind power system is stored in a database, and the wind power system real-time data is preprocessed to obtain the preprocessed wind power system real-time data;

[0023] The real-time data of the wind power system is compared with the set normal range, and the data points beyond the normal range are identified as abnormal data. The identified abnormal data are marked, and the mark includes timestamp, abnormal type and abnormal value.

[0024] Furthermore, the oscillation suppression method for the grid-type PMSG wind power system connected to a weak power grid according to the present invention, step S104 includes:

[0025] Extract the system frequency characteristics before the fault, the system frequency characteristics after the fault, the voltage characteristics at the time of the fault, the power fluctuation amplitude characteristics at the time of the fault, and the fault recovery time characteristics from the fault data

[0026] Receive and set the optimization goal, the value range of the control parameters and the constraints, and the optimization goal is to maximize the total damping coefficient of the system;

[0027] Initialize the parameters of the quantum particle swarm algorithm, randomly initialize a certain number of particles, namely the solution set, each particle represents a set of control parameter values, and set the parameters of the quantum particle swarm algorithm, which include the number of iterations, compression coefficient, expansion coefficient and quantum potential well parameters;

[0028] Fitness calculation: for each particle, the control parameters represented by each particle are substituted into the objective function, and the total damping coefficient of the corresponding grid-type PMSG wind power system is calculated. The total damping coefficient of the corresponding grid-type PMSG wind power system is used as the fitness value of the particle.

[0029] Compare the fitness value of each particle, update the individual optimal position and the global optimal position of the particle, and use the Monte Carlo algorithm to update the position of each particle according to the preset update rules of the quantum particle swarm algorithm to generate a new combination of control parameters.

[0030] Furthermore, the oscillation suppression method for the grid-type PMSG wind power system connected to a weak power grid according to the present invention, step S104 includes:

[0031] Check whether the quantum particle swarm algorithm meets the convergence conditions, such as reaching the preset number of iterations or the fitness value change is less than the preset threshold;

[0032] If the quantum particle swarm algorithm converges, the control parameter combination corresponding to the global optimal position is output, which is the optimized control parameter. The optimized control parameter is used to maximize the total damping coefficient of the system;

[0033] The optimized control parameters are applied to the simulation model of the preset wind power system and weak power grid to verify the effect of the optimized control parameters on the damping characteristics of the grid-connected PMSG wind power system.

[0034] Furthermore, the oscillation suppression method for the grid-type PMSG wind power system connected to a weak power grid according to the present invention, step S105 includes:

[0035] Compare the monitoring data to be identified with the preset standard value range and calculate the comparison error data;

[0036] Determine whether the comparison error data exceeds the maximum value of the preset error range. If so, enter the optimization process; if not, end the optimization process;

[0037] The quantum particle swarm algorithm is used for optimization, and the total damping coefficient of the maximum system is iteratively optimized. After each iteration, it is checked whether the quantum particle swarm algorithm converges, that is, whether a better control parameter combination is found;

[0038] Substitute the optimized maximum system total damping coefficient into the additional damping controller and update the parameter settings of the controller;

[0039] Start the additional damping controller to monitor the operating data of the grid-type PMSG wind power system in real time, compare the new monitoring data with the preset standard value range, and verify the optimization effect;

[0040] If the new comparison error data still exceeds the maximum value of the preset error range, the iterative optimization of the maximum system total damping coefficient will continue until a control parameter combination that meets the conditions is obtained. When the comparison error data is within the preset error range, the optimization process is terminated, and the control parameters executed by the additional damping controller at this time are the optimized maximum system total damping coefficient.

[0041] Beneficial effects of the present invention:

[0042] The present invention establishes a Phillips-Heffron-like model of the active power-frequency link of the PMSG wind power system in a grid-type and deeply analyzes the influence of key parameters in the closed-loop transfer function of the system on the subsynchronous oscillation mode. It can accurately identify and predict the oscillation behavior of the system. The additional damping controller designed in combination with the quantum particle swarm algorithm can adjust the system damping in real time, effectively suppress the subsynchronous oscillation that may be generated after the PMSG wind power system in a grid-type is connected to a weak power grid, and improve the stability and safety of the power grid.

[0043] The present invention optimizes the system control parameters through the quantum particle swarm algorithm, so as to maximize the total damping coefficient of the system and significantly enhance the damping characteristics of the system. The optimized control parameters can make the system more resistant to disturbances, reduce the oscillation amplitude and duration, and improve the dynamic response speed and stability of the system.

[0044] The present invention proposes an effective oscillation suppression method for the special scenario of the grid-type PMSG wind power system being connected to a weak power grid, so that the wind power system can operate more stably in a weak power grid environment. It not only improves the grid-connected capacity of the wind power system, but also promotes the large-scale development and utilization of new energy, and promotes the transformation and upgrading of the energy structure. By suppressing subsynchronous oscillations, the present invention reduces energy loss and waste caused by system instability. The wind power system can more efficiently convert wind energy into electrical energy, improving energy utilization efficiency and economic benefits.

[0045] In summary, the present invention proposes an effective oscillation suppression method by deeply analyzing and optimizing the dynamic behavior of the grid-connected PMSG wind power system, which significantly improves the stability and grid-connected capability of the system and has significant technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic diagram of the process steps of the oscillation suppression method for incorporating a grid-type PMSG wind power system into a weak power grid, as proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the structure and advantages of the present application clearer, the structure of the present application will be further described below in conjunction with the accompanying drawings.

[0049] The purpose of the present invention is to address the drawbacks of the prior art and provide an oscillation suppression method for a grid-connected PMSG wind power system connected to a weak power grid, with the aim of enhancing the ability of the grid-connected wind power system to suppress subsynchronous oscillations.

[0050] In step S101, obtaining the basic parameters of the PMSG (permanent magnet synchronous generator) wind power system and the basic data of the weak power grid is the basis for implementing the entire oscillation suppression method. This step ensures the accuracy of the subsequent analysis model and the effectiveness of the optimization control. Specifically, the following data needs to be obtained:

[0051] Basic parameters of grid-type PMSG wind power system:

[0052] Rated power: The rated output power of a wind power system, used to evaluate the power generation capacity of the system.

[0053] Rated voltage: The rated value of the wind power system output voltage is an important parameter for system design and operation.

[0054] Rated frequency: The rated frequency of the wind power system's output power, usually the standard frequency of the power grid (such as 50Hz or 60Hz).

[0055] Rotor inertia: A physical quantity that reflects the rotational inertia of the rotor of a wind power system and has an important influence on the dynamic response of the system.

[0056] Stator resistance and inductance: The resistance and inductance parameters of the stator circuit affect the electrical characteristics of the system.

[0057] Converter type and parameters: including the type and switching frequency of the full-power converter, which is the core component of wind power system control.

[0058] Control parameters: such as proportional coefficient, integral coefficient, etc., are used to adjust the control strategy of the wind power system.

[0059] Parameter data of weak power grid:

[0060] Grid impedance: A physical quantity that reflects the grid’s resistance to electric current and affects the grid-connected stability of wind power systems.

[0061] Short-circuit capacity: an important parameter that characterizes the strength of the power grid. The smaller the short-circuit capacity, the weaker the power grid.

[0062] Line parameters: including resistance, inductance, etc., describing the electrical characteristics of the power grid transmission lines.

[0063] Operation data of weak power grid:

[0064] Voltage and current: reflect the operating status of the power grid in real time and are important indicators for monitoring the stability of the power grid.

[0065] Frequency: The real-time frequency of the power grid, used to determine whether the power grid is in a stable state.

[0066] Load changes: The real-time changes in the grid load have an important impact on the stable operation of the grid.

[0067] Fault data of weak power grid:

[0068] Historical fault records: Records the type, time, location and other information of past faults in the power grid, which helps to analyze the fault patterns of the power grid and formulate response measures.

[0069] Fault type: Such as short circuit, line break, ground fault, etc. Different types of faults have different impacts on the power grid.

[0070] Fault duration: The time from fault occurrence to recovery, used to assess the impact of the fault on the power grid and the system's recovery capability.

[0071] In step S102, the present invention uses the basic parameters of the grid-type PMSG wind power system and the parameter data of the weak power grid to establish a Phillips-Heffron-like model, and deeply analyzes the properties of the model by deriving the second-order oscillation equation of the active power-frequency link, especially to determine whether the grid-type PMSG wind power system dominates the subsynchronous oscillation (SSO) mode. The following is a detailed explanation of this step:

[0072] The Phillips-Heffron model is a mathematical model commonly used to analyze the dynamic behavior of synchronous generators and their control systems. Based on the rotor motion equations and electrical equations of the generator, it can reveal the stability and dynamic response characteristics of the system. In the present invention, since the grid-type PMSG wind power system simulates the dynamic characteristics of the synchronous generator through virtual synchronous machine control, a model similar to Phillips-Heffron can be established to analyze its dynamic behavior.

[0073] The second-order oscillation equation of the active power-frequency link is derived. Based on the dynamic process of the grid-connected PMSG wind power system (including output power and speed changes, etc.), the second-order oscillation equation of the active power-frequency link can be derived. The form of this equation is similar to the second-order oscillation equation in the Phillips-Heffron model, so it can be regarded as a part of the Phillips-Heffron model. By deriving this equation, the dynamic response characteristics of the grid-connected PMSG wind power system during grid-connected operation can be further analyzed.

[0074] Using the derived second-order oscillation equation of the active-frequency link, we can deeply analyze the properties of the Phillips-Heffron model, mainly including the following aspects:

[0075] By calculating the discriminant of the equation (such as Δ=D²-4JK, where D, J, and K are the damping coefficient, inertia coefficient, and stiffness coefficient, respectively), the stability of the system can be determined. When the total damping coefficient is less than 0, the system is in an unstable state and subsynchronous oscillation may occur.

[0076] Damping path analysis: Within the framework of the Phillips-Heffron model, the damping path between the PMSG wind power system and the generator and transmission line can be analyzed. By comparing the damping coefficients on the path, it can be further determined whether the system dominates the subsynchronous oscillation mode. When the total damping coefficient of the wind power system plays a dominant role in the damping path and is less than 0, it can be considered that the wind power system dominates the subsynchronous oscillation mode.

[0077] Determine whether the grid-type PMSG wind power system is in the dominant subsynchronous oscillation mode;

[0078] Based on the above analysis, it can be determined whether the PMSG wind power system is in a dominant subsynchronous oscillation mode during grid-connected operation. This is crucial for developing an effective oscillation suppression strategy. If the system is judged to be in a dominant subsynchronous oscillation mode, further measures need to be taken to improve the damping characteristics of the system to suppress the occurrence of oscillation.

[0079] In step S103, the method of the present invention collects real-time data of the wind power system from various sensors installed in the grid-type PMSG (permanent magnet synchronous generator) wind power system, and performs abnormal identification processing on the real-time data of the wind power system to ensure the accuracy and reliability of subsequent analysis. The following is a detailed explanation of this step:

[0080] The PMSG wind power system is usually equipped with a variety of sensors to monitor the operating status of the system. These sensors can measure key parameters such as active power, reactive power, voltage and current of the wind power system. In step S103, it is first necessary to collect these data from these sensors in real time. These data reflect the current operating status and performance characteristics of the wind power system and are the basis for subsequent analysis.

[0081] Active power: The actual power output by a wind power system, used to evaluate the system's power generation capacity and efficiency.

[0082] Reactive power: The reactive power exchanged between the wind power system and the power grid has an important impact on the voltage stability and transmission efficiency of the power grid.

[0083] Voltage: The voltage value at the output end of the wind power system reflects the electrical characteristics and stability of the system.

[0084] Current: The current value at the output end of the wind power system is closely related to active power and reactive power, and is also an important indicator for evaluating the operating status of the system.

[0085] Identify abnormal data in real-time data of wind power systems;

[0086] After collecting the real-time data of the wind power system, it is necessary to identify and process these data for abnormalities. Due to the influence of various factors (such as sensor failure, data transmission errors, etc.), the collected data may contain abnormal values ​​or noise. If the abnormal data is not processed and directly used for subsequent analysis, it will lead to wrong results or reduce the accuracy of the analysis.

[0087] The method of anomaly identification usually includes the following steps:

[0088] Data preprocessing: Preprocess the collected raw data, such as denoising and filtering, to reduce the impact of noise and interference on data analysis.

[0089] Data comparison: Compare the pre-processed data with the set normal range or threshold value. This normal range or threshold value can be determined based on the operating experience and theoretical analysis of the wind power system.

[0090] Abnormal data marking: Identify data points that exceed the normal range or threshold as abnormal data and mark them. The marking information usually includes the timestamp of the abnormal data, the abnormal type, and the abnormal value.

[0091] After abnormality identification processing, a set of identified real-time data of the wind power system can be obtained. The identified real-time data of the wind power system has removed the interference of abnormal values ​​and noise, and more accurately reflects the actual operating status of the wind power system. The identified real-time data of the wind power system will be used in subsequent analysis and optimization steps to formulate effective oscillation suppression strategies.

[0092] In step S104, the method of the present invention aims to deeply understand the response characteristics of the PMSG (permanent magnet synchronous generator) wind power system to faults by analyzing the fault data of the weak power grid, and based on these analysis results, the control parameters of the wind power system and the weak power grid are optimized using the quantum particle swarm algorithm (QPSO) to maximize the total damping coefficient of the system. The following is a detailed explanation of this step:

[0093] The fault data of weak power grids record the behavior and response of the power grid under different fault conditions. The fault data of weak power grids include but are not limited to the fault type, fault duration, system frequency characteristics before and after the fault, voltage characteristics, and power fluctuation amplitude. By analyzing these fault data, the response characteristics of the PMSG wind power system in the face of power grid faults, the stability of the system, the recovery speed, and the damping capacity can be revealed.

[0094] Through in-depth analysis of weak grid fault data, the response characteristics analysis results of the grid-connected PMSG wind power system under fault conditions can be obtained. The response characteristics analysis results are helpful to identify the potential oscillation risks of the system and provide guidance for subsequent parameter optimization.

[0095] The quantum particle swarm algorithm (QPSO) is an optimization algorithm based on swarm intelligence. It simulates the motion law of particles in the quantum system and finds the global optimal solution through iterative search. In this step, the QPSO algorithm is used to optimize the control parameters of the grid-type PMSG wind power system and the weak power grid to maximize the total damping coefficient of the system.

[0096] Initialize algorithm parameters: Set the initial parameters of the QPSO algorithm, such as the number of particles, number of iterations, compression-expansion coefficient, etc.

[0097] Define fitness function: Based on the fault data analysis results, define a fitness function to measure the total damping coefficient of the system under different control parameter combinations. The larger the fitness function value, the higher the total damping coefficient of the system and the better the control parameter combination.

[0098] Randomly initialize particle positions: randomly generate a set of control parameter combinations in the solution space as the initial positions of the particles.

[0099] Iterative search: According to the update rules of the QPSO algorithm, the particle positions are continuously updated iteratively to find the global optimal solution. In each iteration, the fitness value of each particle is calculated, and the individual optimal position and global optimal position of the particle are updated according to the fitness value.

[0100] Convergence judgment: Check whether the algorithm meets the convergence conditions (such as reaching the preset number of iterations or the fitness value change is less than the preset threshold). If the convergence conditions are met, the control parameter combination corresponding to the global optimal position is output as the optimization result; otherwise, continue the iterative search.

[0101] After optimization by the QPSO algorithm, a set of optimal control parameter combinations can be obtained, so that the PMSG wind power system and the weak power grid can maximize the total damping coefficient of the system when connected to the grid. These optimal control parameters will be used to guide subsequent system design and operation control to improve the stability and anti-oscillation capability of the system.

[0102] In step S105, the method of the present invention applies the maximized total system damping coefficient obtained by quantum particle swarm optimization (QPSO) to the additional damping controller in the grid-type PMSG (permanent magnet synchronous generator) wind power system. The additional damping controller uses this maximized total system damping coefficient to execute the control strategy to effectively suppress subsynchronous oscillations. Subsequently, the control effect is evaluated by monitoring the operating data of the wind power system, and the monitoring data is compared and analyzed according to preset standards. The following is a detailed explanation of this step:

[0103] In step S104, a set of control parameters are obtained by optimizing the QPSO algorithm, and these parameters can maximize the total damping coefficient of the system. In step S105, these optimal control parameters are first substituted into the additional damping controller in the grid-type PMSG wind power system. The additional damping controller is a device specially designed to enhance system damping and suppress subsynchronous oscillations. By adjusting its parameters, the response characteristics of the system to oscillations can be changed.

[0104] When the maximum system total damping coefficient is substituted into the additional damping controller, the controller begins to execute its control strategy. Its goal is to offset the subsynchronous oscillations in the system by providing additional damping, thereby ensuring the stable operation of the wind power system. The working principle of the additional damping controller may involve real-time monitoring and adjustment of parameters such as the output power, voltage, and current of the wind power system.

[0105] While executing the control strategy, it is necessary to continuously monitor the operating data of the grid-type PMSG wind power system. These data include but are not limited to the active power, reactive power, voltage, current and frequency of the wind power system. The purpose of monitoring is to evaluate the control effect of the additional damping controller in real time and the suppression of subsynchronous oscillation by the system.

[0106] Compare and analyze the monitored wind power system operation data with the preset standard value range. These preset standard values ​​are formulated based on factors such as wind power system design requirements, operation experience, and safety regulations. Through comparison, the comparison error data can be calculated, that is, the difference between the monitored data and the standard value.

[0107] If the comparison error data exceeds the preset maximum error range, it means that the current maximum system total damping coefficient may not fully meet the system's stable operation requirements. In this case, it is necessary to return to step S104 to further optimize the maximum system total damping coefficient. The optimization process may involve measures such as adjusting the parameters of the QPSO algorithm, increasing the number of iterations, or changing the optimization objective function.

[0108] Repeat the optimization and monitoring process of steps S104 and S105 until the additional damping controller performs the optimization to maximize the total system damping coefficient, and the operation data of the wind power system meets the preset standard value range. At this point, it can be considered that the system has achieved effective suppression of subsynchronous oscillations and is stable and reliable in operation.

[0109] In step S101, the oscillation suppression method for the grid-type PMSG (permanent magnet synchronous generator) wind power system connected to the weak power grid described in the present invention elaborates on various basic data that need to be obtained. Various basic data are crucial for subsequent model building, oscillation characteristic analysis and suppression strategy design. The following is a further detailed explanation of this step:

[0110] Basic parameters of grid-type PMSG wind power system;

[0111] Rated power: the maximum power that a wind power system can continuously output under normal working conditions.

[0112] Rated voltage: The standard voltage value specified when designing a wind power system to ensure the stability and safety of power transmission.

[0113] Rated frequency: The standard frequency at which a wind power system outputs electrical energy, usually 50Hz or 60Hz.

[0114] Rotor inertia: A physical quantity that reflects the resistance of the wind power system rotor to speed changes during rotation, and has an important impact on the dynamic performance of the system.

[0115] Stator resistance and inductance: The stator is an important component of the wind power system, and its resistance and inductance parameters affect the power conversion efficiency and system stability.

[0116] Converter type: The converter type used in the grid-type PMSG wind power system, such as the voltage source converter (VSC). Different types of converters have different control characteristics and performance.

[0117] Switching frequency parameters: The operating frequency of the power electronic switches in the converter has an impact on power quality, system losses and electromagnetic interference.

[0118] Control parameters: including but not limited to the proportional, integral and differential coefficients of the PID controller, used to adjust the control strategy and performance of the wind power system.

[0119] Parameter data of weak power grid

[0120] Grid impedance: A physical quantity that reflects the grid's resistance to electric current, consisting of resistance and reactance.

[0121] Short-circuit capacity: It refers to the maximum current capacity that the power grid can provide under short-circuit fault conditions and is an important indicator for evaluating the strength of the power grid.

[0122] Line parameters: including line resistance, inductance and capacitance, etc., which affect the voltage drop, power loss and stability during power transmission.

[0123] Operation data of weak power grid

[0124] Voltage: The voltage value of each node in the power grid is an important parameter for evaluating the operating status and stability of the power grid.

[0125] Current: The value of the current flowing in the power grid, reflecting the transmission of electrical energy.

[0126] Frequency: The AC frequency of the power grid must be kept near the rated value to ensure the normal operation of power equipment.

[0127] Load variation: The change of load in the power grid over time has an important impact on the stability of the power grid and the quality of power supply.

[0128] Fault data of weak power grid

[0129] Historical fault records: including the time, location, type and handling method of the fault, providing important reference for fault analysis and prevention.

[0130] Fault type: such as short circuit fault, open circuit fault, ground fault, etc. Different types of faults have different impacts on the power grid and different handling methods.

[0131] Fault duration: The time from the occurrence of a fault to its clearing has an important impact on the stability of the power grid and the reliability of power supply.

[0132] By comprehensively acquiring these basic data, the method of the present invention can provide a solid data foundation for subsequently establishing an oscillation model for incorporating a PMSG wind power system into a weak power grid, analyzing oscillation characteristics, and designing an effective oscillation suppression strategy.

[0133] In step S102, the present invention further elaborates on how to use the basic parameters of the grid-type PMSG (permanent magnet synchronous generator) wind power system and the parameter data of the weak power grid, combined with the dynamic process of the wind power system, to derive the second-order oscillation equation of the active-frequency link, and analyze the stability and subsynchronous oscillation (SSO) mode of the system based on the Phillips-Heffron model. The following is a detailed explanation of this step:

[0134] Derive the second-order oscillation equation of the active-frequency link;

[0135] Data preparation: First, collect the basic parameters of the grid-type PMSG wind power system, such as rated power, rated voltage, rated frequency, rotor inertia, stator resistance, inductance, converter type, switching frequency parameters and control parameters, etc. At the same time, obtain the parameter data of the weak grid, including grid impedance, short-circuit capacity, line parameters, etc.

[0136] Dynamic process analysis: Consider the dynamic process of the grid-type PMSG wind power system, especially the changes in output power and speed. These dynamic processes reflect the behavioral characteristics of the wind power system in actual operation.

[0137] Derivation of the second-order oscillation equation: Based on the dynamic process of the wind power system and combined with the basic parameters of the system, the second-order oscillation equation of the active power-frequency link is derived. The form of this equation is similar to the second-order oscillation equation in the Phillips-Heffron model, so it can be considered as an instance of the Phillips-Heffron model.

[0138] Analyze system stability using the Phillips-Heffron model;

[0139] Discriminant calculation: In the Phillips-Heffron model, the stability of the system is usually judged by the discriminant Δ=D2-4J*K. Among them, D, J, and K are the damping coefficient, inertia coefficient, and stiffness coefficient of the equation respectively. For the active-frequency link of the grid-type PMSG wind power system, its discriminant is calculated to determine the stability of the system.

[0140] Stability judgment: When the result of the discriminant Δ is less than 0, it indicates that the total damping coefficient of the system is less than 0. At this time, the grid-type PMSG wind power system is in an unstable state and subsynchronous oscillation may occur.

[0141] Analyze damping paths and subsynchronous oscillation modes;

[0142] Damping path analysis: In the framework of the Phillips-Heffron model, the damping paths between the PMSG wind power system and the generator and transmission lines are further analyzed. These paths reflect the interaction and energy transfer between the various parts of the system.

[0143] Damping coefficient comparison: compare the damping coefficients on different damping paths. When the total damping coefficient on a path is less than 0, it can be considered that the wind power system has oscillated on this path and may have dominated the subsynchronous oscillation mode of the entire system.

[0144] Through the above steps, the present invention can deeply understand the dynamic behavior of the PMSG wind power system after being connected to the weak power grid, especially its stability and subsynchronous oscillation characteristics, which provides an important theoretical basis and analysis tool for the subsequent design of effective oscillation suppression strategies.

[0145] In step S103, the present invention describes in detail how to collect real-time data from a grid-type PMSG (permanent magnet synchronous generator) wind power system and perform preprocessing and abnormality detection on the data. The following is a detailed explanation of this step:

[0146] Real-time data collection collects real-time data from various sensors installed in the grid-type PMSG wind power system. These sensors can monitor key operating parameters of the wind power system, such as active power, reactive power, voltage and current. The collected data should contain sufficient timestamp information for subsequent time series analysis and anomaly detection.

[0147] The collected real-time data of the wind power system is stored in a dedicated database. The database should have efficient data storage and retrieval capabilities to support subsequent data analysis and processing.

[0148] Preprocess the real-time data stored in the database. Preprocessing steps may include data cleaning (removing noise and outliers), data transformation (converting data into a format suitable for analysis), and data normalization (scaling data to the same magnitude for comparison and analysis).

[0149] According to the operation experience and specifications of the wind power system, the normal range of each monitoring parameter should be set. These ranges should be determined based on the safe operation requirements and historical operation data of the wind power system.

[0150] Compare the pre-processed real-time data of the wind power system with the set normal range. By comparing whether each data point exceeds the normal range, potential abnormal data can be identified.

[0151] Mark the identified abnormal data for subsequent analysis and processing. The marking information should include the timestamp (recording the time when the abnormality occurred), the abnormality type (such as power abnormality, voltage abnormality, etc.) and the abnormal value (specific value outside the normal range).

[0152] Depending on the nature and impact of the abnormal data, appropriate processing measures are taken, which may include issuing an alarm to notify operation and maintenance personnel, automatically adjusting the operating parameters of the wind power system, or initiating an emergency shutdown procedure.

[0153] Through step S103, the present invention can realize effective monitoring and management of real-time data of the grid-type PMSG wind power system, timely discover and handle potential abnormal situations, thereby ensuring the safe and stable operation of the wind power system.

[0154] In step S104, the present invention elaborates on how to use fault data analysis and quantum particle swarm optimization (QPSO) to optimize the control parameters of the grid-type PMSG (permanent magnet synchronous generator) wind power system and the weak power grid to achieve the goal of maximizing the total damping coefficient of the system. The following is a detailed explanation of this step:

[0155] Key features are extracted from fault data, including system frequency features before and after the fault, voltage features at the time of the fault, power fluctuation amplitude features at the time of the fault, and fault recovery time features. These features can reflect the dynamic response and stability of the system under fault conditions.

[0156] Based on the extracted features, the response characteristics of the PMSG wind power system to faults are analyzed. This helps to understand the behavior mode of the system under fault conditions and provide guidance for subsequent optimization work.

[0157] The optimization goal is to maximize the total damping coefficient of the PMSG wind power system. By increasing the system damping, subsynchronous oscillations can be effectively suppressed and the stability and reliability of the system can be improved.

[0158] Control parameter range and constraints: Set the control parameter range and corresponding constraints. These constraints may include physical limitations of the parameters, system operation requirements, and safety considerations.

[0159] A certain number of particles are randomly initialized, each particle represents a set of control parameter values. These particles constitute the initial solution set of the algorithm.

[0160] Set the parameters of the quantum particle swarm algorithm, including the number of iterations, compression coefficient, expansion coefficient, and quantum potential well parameters. These parameters determine the algorithm's operating efficiency and convergence performance.

[0161] For each particle, the control parameter it represents is substituted into the objective function to calculate the total damping coefficient of the corresponding grid-type PMSG wind power system. The damping coefficient is used as the fitness value of the particle to evaluate the quality of the particle.

[0162] Particle position update, compare the fitness value of each particle, update the individual optimal position of the particle and the global optimal position. According to the preset update rules of the quantum particle swarm algorithm, the Monte Carlo algorithm is used to update the position of each particle and generate a new control parameter combination. This step is performed iteratively until the algorithm converges or reaches the preset number of iterations.

[0163] Through step S104, the present invention can use fault data analysis and quantum particle swarm algorithm to optimize the control parameters of the grid-type PMSG wind power system and the weak power grid, thereby achieving the goal of maximizing the total damping coefficient of the system. This helps to improve the stability and reliability of the system and suppress the occurrence of subsynchronous oscillations.

[0164] In step S104, the present invention further refines the convergence check of the quantum particle swarm algorithm (QPSO) and the verification process of the optimized control parameters. The following is a detailed explanation of this step:

[0165] Convergence check of quantum particle swarm algorithm: During the iteration process of quantum particle swarm algorithm, the algorithm is regularly checked to see if it meets the convergence conditions. Convergence conditions usually include reaching a preset number of iterations or the fitness value change is less than a preset threshold. When any of the conditions is met, the algorithm is considered to have converged, that is, a better solution has been found.

[0166] If the quantum particle swarm algorithm converges, the control parameter combination corresponding to the global optimal position is output. This set of control parameters is obtained after algorithm optimization and can maximize the total damping coefficient of the grid-type PMSG wind power system.

[0167] Verification of optimized control parameters: Apply the optimized control parameters to the preset wind power system and weak grid simulation model. The simulation model is a virtual power system environment that simulates the operation and response characteristics of the actual system.

[0168] In the simulation model, the damping characteristics of the grid-type PMSG wind power system after applying the optimized control parameters are observed and recorded. This includes the stability of the system, the suppression effect of subsynchronous oscillations, and various performance indicators. By comparing with the simulation results before optimization, the effectiveness and superiority of the optimized control parameters can be evaluated.

[0169] By refining step S104, the present invention realizes the convergence of the quantum particle swarm algorithm, and further proves the effectiveness of the method in improving the damping characteristics of the grid-type PMSG wind power system and suppressing subsynchronous oscillations through simulation verification of the optimized control parameters.

[0170] In step S105, the present invention elaborates on how to evaluate the optimization effect by comparing the monitoring data with the standard value range, and further uses the quantum particle swarm algorithm to iteratively optimize the maximum system total damping coefficient until the preset error range is met. The following is a detailed explanation of this step:

[0171] The monitoring data to be identified (i.e., the real-time operating data of the grid-type PMSG wind power system) is compared with the preset standard value range. The standard value range is set according to the system design requirements, operating experience or industry standards, and is used to evaluate whether the system's operating status is normal. Through the comparison, the comparison error data is calculated to quantify the deviation between the current state of the system and the standard state.

[0172] Determine whether the comparison error data exceeds the preset maximum error range. The preset maximum error range is set according to the tolerance, security and stability requirements of the system and is used to determine when the system needs to be adjusted or optimized.

[0173] If the comparison error data exceeds the maximum value of the preset error range, it means that the current system operation status does not meet the requirements and needs to enter the optimization process. At this time, the quantum particle swarm algorithm is used to iteratively optimize the maximum system total damping coefficient.

[0174] After each iteration, check whether the quantum particle swarm algorithm has converged, that is, whether a better combination of control parameters has been found. Convergence checks are usually based on the output results of the algorithm (such as changes in fitness values) and preset convergence conditions (such as the number of iterations, fitness value thresholds, etc.).

[0175] If the algorithm converges, the optimized maximum system total damping coefficient is substituted into the additional damping controller, and the controller parameter settings are updated. In this way, the additional damping controller can suppress the subsynchronous oscillation of the system with a better control strategy.

[0176] The additional damping controller is started, and after the controller parameters are updated, the additional damping controller is started to start real-time monitoring of the operating data of the grid-type PMSG wind power system.

[0177] Compare the new monitoring data with the preset standard value range to verify the optimization effect. If the new comparison error data is still within the preset error range, it means that the optimization is effective and the system operation status has been improved.

[0178] If the new comparison error data still exceeds the maximum value of the preset error range, it means that the current optimization result still does not meet the requirements, and it is necessary to continue to iteratively optimize the maximum system total damping coefficient. This process will be repeated until a control parameter combination that meets the conditions is found. When the comparison error data is within the preset error range, the optimization process is terminated, and the control parameters executed by the additional damping controller at this time are determined as the optimized maximum system total damping coefficient.

[0179] By refining step S105, the present invention ensures continuous monitoring and optimization of the oscillation suppression effect of the grid-type PMSG wind power system after it is connected to the weak power grid, thereby improving the stability and reliability of the system.

[0180] Based on an oscillation suppression method for PMSG wind power system connected to a weak power grid, specific implementation cases include:

[0181] Establish a Phillips-Heffron-like model of the active power-frequency link of the grid-connected PMSG wind power system;

[0182] Establish the closed-loop transfer function of the PMSG wind power system and analyze the influence of key parameters on the subsynchronous oscillation mode;

[0183] An additional damping controller is designed based on quantum particle swarm optimization (QPSO).

[0184] The Phillips-Heffron-like model of the active power-frequency link of the grid-connected PMSG wind power system is established, including:

[0185] Firstly, the second-order oscillation equation of the active-frequency link of the grid-type PMSG wind power system is derived according to the dynamic process of the active-frequency link. Its form is consistent with the Phillips-Heffron model. It can be considered that the second-order oscillation equation of the active-frequency link is a Phillips-Heffron model. According to the properties of the Phillips-Heffron model, the second-order oscillation equation of the active-frequency link is analyzed. It can be seen that the total damping coefficient of the wind power system has a great influence on the SSO mode. Therefore, the damping path method can be used to analyze the subsynchronous oscillation mode of the system.

[0186] The Phillips-Heffron-like model of the active power-frequency link of the grid-connected PMSG wind power system is established, including:

[0187] Firstly, the second-order oscillation equation of the active-frequency link of the grid-type PMSG wind power system is derived according to the dynamic process of the active-frequency link. Its form is consistent with the Phillips-Heffron model. It can be considered that the second-order oscillation equation of the active-frequency link is a Phillips-Heffron model. According to the properties of the Phillips-Heffron model, the second-order oscillation equation of the active-frequency link is analyzed. It can be seen that the total damping coefficient of the wind power system has a great influence on the SSO mode. Therefore, the damping path method can be used to analyze the subsynchronous oscillation mode of the system.

[0188] Establishing the closed-loop transfer function of the grid-connected PMSG wind power system and analyzing the impact of key parameters on the subsynchronous oscillation mode include:

[0189] Firstly, the closed-loop transfer function of the grid-type PMSG wind power system is established according to the expression of the output power of the PMSG grid-side converter. There are two damping paths in the grid-type PMSG wind power system. The interaction between the wind power system and the weak AC system acts on multiple coupling points through current disturbances and voltage disturbances. These coupling points have passed through the transfer function link in the current-voltage dual closed loop in the direct-drive wind farm. The weak AC system mainly provides an external research environment. Therefore, through the above analysis, it can be concluded that the two damping paths are mainly related to the parameters of the current-voltage dual closed loop; secondly, according to the system damping coefficient, the influence of the control parameters of the active-frequency link and the reactive-voltage link on the subsynchronous oscillation mode is analyzed respectively.

[0190] The design of an additional damping controller combined with the quantum particle swarm optimization (QPSO) includes:

[0191] After obtaining the influence of the total damping of the system on the subsynchronous oscillation mode, that is, when the total damping of the system is less than 0, the system will experience subsynchronous oscillation. An additional damping controller is designed using the influence of the total damping of the system. The purpose of the additional damping controller is to increase the total damping coefficient of the wind power system. Therefore, the maximum positive damping provided by the additional damping controller is taken as the optimal goal. Combined with the quantum particle swarm algorithm, an additional damping controller is designed.

[0192] According to the dynamic process of the active power-frequency link of the grid-type PMSG wind power system, its second-order oscillation equation on the complex plane is derived. The form of this second-order oscillation equation is consistent with the form of the second-order oscillation equation of the Phillips-Heffron model, and its properties can be considered to be similar to the Phillips-Heffron model. In the Phillips-Heffron model, the discriminant Δ=D2-4J*K can be used to determine whether the corresponding component dominates the subsynchronous oscillation mode. This property is also applicable to the grid-type PMSG wind power system. The discriminant of its second-order oscillation equation is related to the total damping coefficient of the system. Therefore, the damping path method can be used to analyze the subsynchronous oscillation mode of the grid-type PMSG wind power system.

[0193] The closed-loop transfer function of the grid-type PMSG wind power system is established with the output power of the PMSG grid-side converter. There are two damping paths in the grid-type PMSG wind power system. From the analysis of the paths, it can be seen that the interaction between the wind power system and the weak AC system acts on multiple coupling points through current disturbances and voltage disturbances. These coupling points have passed through the transfer function link in the current and voltage dual closed loop in the direct-drive wind farm. The weak AC system mainly provides an external research environment, so these two damping paths are mainly related to the parameters of the current and voltage dual closed loop; secondly, the influence of the active-frequency link and the reactive-voltage link control parameters on the subsynchronous oscillation is analyzed respectively. The analysis principle is that when the total damping coefficient is less than 0, the wind power system will experience subsynchronous oscillation, and when the total damping coefficient is greater than 0, the wind power system will not experience subsynchronous oscillation.

[0194] An additional damping controller is designed with the goal of improving the total damping coefficient of the system. The structure of the additional damping controller includes four links: a filtering link, a gain link, a lead-lag link and a limiting link. The invention is divided into five steps to design the additional damping controller. First, various parameters of the filter in the additional damping controller except the time constant are determined. Secondly, the input and output of the additional damping controller are determined. Then, the closed-loop transfer function of the system after the additional damping path is analyzed. Finally, the maximum positive damping coefficient provided by the additional damping controller is taken as the optimal objective function, and the quantum particle swarm algorithm is used to determine the time constant.

[0195] During implementation, for the system composed of permanent magnet direct-drive wind turbines connected to the weak power grid via back-to-back converters, virtual synchronous machine control technology is adopted to utilize the feedback of the frequency change rate to achieve flexible adjustment of the virtual inertia coefficient, thereby realizing active frequency control of the system.

[0196] By establishing a corresponding functional relationship between the virtual inertia coefficient J and the system frequency change rate |df / dt|, when the microgrid frequency changes relatively greatly, the system virtual inertia coefficient is increased through the functional relationship to achieve flexible adjustment of the system virtual inertia, thereby improving the frequency stability of the system and making the VSG control technology more practical.

[0197] The main structure of the grid-type PMSG wind power system is mainly composed of permanent magnet direct-drive wind turbines, full-power converters and power grids. The permanent magnet direct-drive wind turbines, full-power converters and power grids are connected in sequence. The machine-side converter adopts a fixed DC voltage control strategy to obtain the control signal required by the machine-side converter; the grid-side converter first collects wind speed, uses MPPT control to obtain the power required for grid-type control, and then adopts voltage and current dual closed-loop control to finally obtain the control signal required to control the grid-side converter.

[0198] The second-order oscillation equation of the grid-type PMSG wind power system is established through the dynamic process of the active-frequency link. The form of the second-order oscillation equation is similar to that of the Phillips-Heffron model, which can be called a quasi-Phillips-Heffron model. Therefore, the properties of the Phillips-Heffron model can be used. Through analysis, it can be seen that the main factor affecting the SSO mode is the total damping coefficient of the wind power system, so the damping path analysis method can be used; secondly, the transfer function of the grid-type PMSG wind power system is established, and two damping paths are obtained, and the influence of the control parameters of the active-frequency link and the reactive-voltage link on the SSO mode are analyzed respectively; after understanding the law affecting the SSO mode, a subsynchronous oscillation suppression strategy is proposed, namely, an additional damping controller, and the parameters of the additional damping controller are designed in five steps; finally, through the hardware-in-the-loop platform simulation, the waveform diagram of the PMSG output power with and without the additional damping controller is obtained, and the analysis shows that it is consistent with the theory.

[0199] Based on the output power expression of the PMSG grid-side converter, the closed-loop transfer function is obtained. There are two damping paths in the grid-type PMSG wind power system, which are marked with a red dotted ellipse and a purple solid ellipse respectively. The direction of the arrow represents the path direction of the subsynchronous component propagation. Analysis of the path shows that the interaction between the wind power system and the weak AC system acts on multiple coupling points through current disturbances and voltage disturbances. These coupling points have passed through the transfer function link in the current-voltage dual closed loop in the direct-drive wind farm. The present invention mainly studies the characteristics of the grid-type PMSG wind power system, and the weak AC system mainly provides an external research environment. Therefore, the two damping paths are mainly related to the parameters of the current-voltage dual closed loop.

[0200] The core task of the filtering link is to accurately extract the subsynchronous component from the input signal to ensure that the additional damping controller only effectively suppresses SSO; the gain link is to amplify or reduce the amplitude of the subsynchronous component in the input signal; the lead-lag link is to adjust the phase of the signal to achieve the lead or lag change of the phase, so that the output signal can effectively neutralize the subsynchronous frequency part of the attached signal. This adjustment process aims to achieve effective suppression of SSO; the main function of the limiting link is to control the output signal amplitude of the additional damping controller.

[0201] The quantum particle swarm algorithm (QPSO) introduces the concept of quantum dynamics kinematics into the particle swarm algorithm (PSO), cancels the original concept of particle velocity, and adopts the concept of potential well, that is, there is no relationship between the next movement of the particle and the previous movement of the particle, which increases the randomness of the particle movement.

[0202] First, randomly initialize the particle's position Xi and potential field Pi;

[0203] Determine whether the iteration termination condition is met, if yes, then end, otherwise calculate the average value Mbest of the best position of the particles in the current population;

[0204]

[0205] Calculate the potential field of the i-th particle;

[0206]

[0207] Update particle positions using the Monte Carlo algorithm;

[0208]

[0209] Calculate the particle position fitness. If it is better than the individual best position fitness, update the particle best position; if it is better than the population best position fitness, update the population best position; finally return to step 2.

[0210] Where n is the total number of particles; Mbest is the individual average optimal position of the current particle; pbesti is the individual optimal position of particle i; a is the compression-expansion coefficient, which is used to control the convergence speed of the algorithm; u is a random number between (0,1); and gbest is the global best position.

[0211] The damping path of the damping controller is added to the closed-loop transfer function of the original grid-type PMSG wind power system. The transfer function G(s) of the damping path is related to the parameter design of the additional damping controller itself, as well as the internal control structure of the weak grid and PMSG. This path will generate a new electromagnetic power to be added to the output power of the grid-side converter, which illustrates the effect of adding an additional damping controller on the damping of the system in the subsynchronous oscillation mode.

[0212] The present invention reflects the SSO mode by measuring the active power output by PMSG. By changing the proportional coefficient Ki at 2s, the waveform curve without the additional damping controller and the waveform curve with the additional damping controller can be obtained. The active power output by the PMSG without the additional damping controller begins to oscillate and fluctuate from 2s, with the maximum fluctuation ranging from -1.5pu to 1.8pu, and cannot be stable. After the additional damping controller is installed, the active power output by the PMSG always fluctuates slightly around 0.5pu. This phenomenon is consistent with the analysis of the present invention, indicating that the installation of the additional damping controller effectively suppresses the subsynchronous oscillation of the system, and also verifies the effectiveness of the strategy.

[0213] The present invention proposes an oscillation suppression method for a PMSG (permanent magnet synchronous generator) wind power system connected to a weak power grid, aiming to solve the subsynchronous oscillation problem that may occur in a PMSG wind turbine in a weak power grid environment. The specific technical solution is as follows:

[0214] The grid-type PMSG wind power system directly controls the output voltage amplitude and phase by simulating the rotor motion equation of the synchronous generator set, supports the frequency and voltage stability of the power grid, and provides inertia and damping support. However, with the development of new energy power systems, the proportion of power electronic systems has gradually increased, and the power grid has gradually changed from a strong power grid to a weak power grid, resulting in a decrease in the system inertia level, which may cause subsynchronous oscillation problems.

[0215] The present invention proposes a method for analyzing and suppressing subsynchronous oscillation of a PMSG wind power system connected to a weak power grid. Figure 1 , including establishing a Phillips-Heffron-like model of the active power-frequency link of the grid-type PMSG wind power system, analyzing the closed-loop transfer function of the system and the influence of key parameters, and designing an additional damping controller in combination with the quantum particle swarm algorithm (QPSO) to solve the subsynchronous oscillation problem.

[0216] A Phillips-Heffron-like model is established. According to the basic parameters of the grid-type PMSG wind power system and the parameter data of the weak power grid, combined with the dynamic process of the wind power system, the second-order oscillation equation of the active power-frequency link is derived. The form of this equation is consistent with the Phillips-Heffron model, so it can be called a Phillips-Heffron-like model.

[0217] The properties of the Phillips-Heffron model are analyzed using the second-order oscillation equation of the active-frequency link, especially to determine whether the wind power system is in a subsynchronous oscillation mode. When the total damping coefficient of the system is less than 0, the system is considered to be in a subsynchronous oscillation mode.

[0218] Based on the expression of the output power of the PMSG grid-side converter, the closed-loop transfer function of the grid-type PMSG wind power system is established to analyze the interaction between the wind power system and the weak AC system. These interactions affect multiple coupling points through current disturbances and voltage disturbances. These coupling points all pass through the transfer function link in the current-voltage double closed loop. Therefore, the damping path is mainly related to the parameters of the current-voltage double closed loop. The influence of the control parameters of the active-frequency link and the reactive-voltage link on the subsynchronous oscillation mode is further analyzed.

[0219] Initialize the parameters of the quantum particle swarm algorithm, including particle position, potential field, number of iterations, compression coefficient, expansion coefficient, and quantum potential well parameters. For each particle, calculate its fitness value, that is, the corresponding total damping coefficient of the PMSG wind power system, update the individual optimal position and global optimal position of the particle, use the Monte Carlo algorithm to update the particle position, and generate a new control parameter combination. Check whether the algorithm converges. If converged, output the control parameter combination corresponding to the global optimal position. Apply the optimized control parameters to the simulation model of the wind power system and the weak power grid to verify its effect on improving the damping characteristics of the system.

[0220] Substitute the maximum system total damping coefficient into the additional damping controller and start the controller. Monitor the operating data of the grid-type PMSG wind power system and compare it with the preset standard value range. If the comparison error data exceeds the maximum value of the preset error range, the quantum particle swarm algorithm is used to iteratively optimize the maximum system total damping coefficient. Repeat the optimization process until the comparison error data is within the preset error range.

[0221] The present invention effectively solves the subsynchronous oscillation problem of the grid-connected PMSG wind power system in a weak power grid environment by comprehensively using Phillips-Heffron model analysis, system closed-loop transfer function analysis, quantum particle swarm algorithm optimization and additional damping controller design, thereby improving the stability and grid-connected capability of the wind power system.

[0222] The above description is only an embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for suppressing oscillation of a PMSG wind power system connected to a weak power grid, characterized in that: include: Step S101, obtaining basic parameters of a grid-type PMSG wind power system and basic data of a weak power grid, wherein the basic data of the weak power grid includes parameter data of the weak power grid, operation data of the weak power grid and fault data of the weak power grid; Step S102, using the basic parameters of the grid-type PMSG wind power system and the parameter data of the weak power grid, a Phillips-Heffron-like model is established, and the second-order oscillation equation of the active power-frequency link is derived according to the basic parameters of the grid-type PMSG wind power system and the parameter data of the weak power grid and the dynamic process of the grid-type PMSG wind power system, and the properties of the Phillips-Heffron model are analyzed by using the second-order oscillation equation of the active power-frequency link. The properties of the Phillips-Heffron model analyzed by using the second-order oscillation equation of the active power-frequency link include determining whether the grid-type PMSG wind power system is in a dominant subsynchronous oscillation mode; Step S103, collecting real-time data of the wind power system from various sensors installed in the grid-type PMSG wind power system, the real-time data of the wind power system including active power of the wind power system, reactive power of the wind power system, voltage of the wind power system, and current of the wind power system, identifying abnormal data in the real-time data of the wind power system, and obtaining the identified real-time data of the wind power system; Step S104, using the fault data of the weak power grid to analyze the response characteristics of the PMSG wind power system to the fault, obtaining the fault data analysis results, and optimizing the control parameters of the PMSG wind power system and the weak power grid using the quantum particle swarm algorithm according to the fault data analysis results to obtain the maximum total damping coefficient of the system; Step S105, substitute the maximized system total damping coefficient into the additional damping controller in the grid-type PMSG wind power system, the additional damping controller executes the maximized system total damping coefficient to suppress subsynchronous oscillations, monitor the operation data of the grid-type PMSG wind power system after the additional damping controller executes the maximized system total damping coefficient, obtain the monitoring data to be identified, compare the monitoring data to be identified with the preset standard value range, obtain the comparison error data, if the comparison error data exceeds the maximum value of the preset error range, optimize the maximized system total damping coefficient until the additional damping controller executes the optimized maximized system total damping coefficient to meet the preset standard value range; The step S102 includes: Using the basic parameters of the PMSG wind power system and the parameter data of the weak power grid, combined with the dynamic process of the PMSG wind power system, the second-order oscillation equation of the active power-frequency link is derived. The dynamic process of the PMSG wind power system includes the output power and speed changes. In the Phillips-Heffron model, the stability of the system is usually judged by the discriminant Δ=D2-4J*K, where D, J, and K are the damping coefficient, inertia coefficient, and stiffness coefficient of the equation respectively. For the active-frequency link of the grid-type PMSG wind power system, its discriminant is calculated. When the total damping coefficient is less than 0, the grid-type PMSG wind power system is in an unstable state and subsynchronous oscillation occurs. In the framework of the Phillips-Heffron model, the damping path between the PMSG wind power system and the generator and transmission line is analyzed, and the damping coefficients on the path are compared. When the total damping coefficient is less than 0, the system oscillates, and it can be considered that the wind power system dominates the subsynchronous oscillation mode.

2. The oscillation suppression method for a PMSG wind power system connected to a weak power grid according to claim 1 is characterized in that: The step S101 includes: The basic parameters of the grid-type PMSG wind power system include rated power, rated voltage, rated frequency, rotor inertia, stator resistance, inductance, converter type, switching frequency parameters and control parameters; The parameter data of weak power grid include grid impedance, short-circuit capacity and line parameters; The operational data of weak power grids include voltage, current, frequency and load changes; The fault data of weak power grid includes historical fault records, fault types and fault duration.

3. The oscillation suppression method for a PMSG wind power system connected to a weak power grid according to claim 1 is characterized in that: The step S103 includes: Real-time data of the wind power system from various sensors installed in the PMSG wind power system, including active power, reactive power, voltage and current of the PMSG wind power system; The collected wind power system real-time data of the grid-type PMSG wind power system is stored in a database, and the wind power system real-time data is preprocessed to obtain the preprocessed wind power system real-time data; The real-time data of the wind power system is compared with the set normal range, and the data points beyond the normal range are identified as abnormal data. The identified abnormal data are marked, and the mark includes timestamp, abnormal type and abnormal value.

4. The oscillation suppression method for a PMSG wind power system connected to a weak power grid according to claim 1 is characterized in that: The step S104 includes: Extract the system frequency characteristics before the fault, the system frequency characteristics after the fault, the voltage characteristics at the time of the fault, the power fluctuation amplitude characteristics at the time of the fault, and the fault recovery time characteristics from the fault data Receive and set the optimization goal, the value range of the control parameters and the constraints, and the optimization goal is to maximize the total damping coefficient of the system; Initialize the parameters of the quantum particle swarm algorithm, randomly initialize a certain number of particles, namely the solution set, each particle represents a set of control parameter values, and set the parameters of the quantum particle swarm algorithm, which include the number of iterations, compression coefficient, expansion coefficient and quantum potential well parameters; Fitness calculation: for each particle, the control parameters represented by each particle are substituted into the objective function, and the total damping coefficient of the corresponding grid-type PMSG wind power system is calculated. The total damping coefficient of the corresponding grid-type PMSG wind power system is used as the fitness value of the particle. Compare the fitness value of each particle, update the individual optimal position and the global optimal position of the particle, and use the Monte Carlo algorithm to update the position of each particle according to the preset update rules of the quantum particle swarm algorithm to generate a new combination of control parameters.

5. The oscillation suppression method for a PMSG wind power system connected to a weak power grid according to claim 4 is characterized in that: The step S104 includes: Check whether the quantum particle swarm algorithm meets the convergence conditions, such as reaching the preset number of iterations or the fitness value change is less than the preset threshold; If the quantum particle swarm algorithm converges, the control parameter combination corresponding to the global optimal position is output, which is the optimized control parameter. The optimized control parameter is used to maximize the total damping coefficient of the system; The optimized control parameters are applied to the simulation model of the preset wind power system and weak power grid to verify the effect of the optimized control parameters on the damping characteristics of the grid-connected PMSG wind power system.

6. The oscillation suppression method for a PMSG wind power system connected to a weak power grid according to claim 5 is characterized in that: The step S105 includes: Compare the monitoring data to be identified with the preset standard value range and calculate the comparison error data; Determine whether the comparison error data exceeds the maximum value of the preset error range. If so, enter the optimization process; if not, end the optimization process; The quantum particle swarm algorithm is used for optimization, and the total damping coefficient of the maximum system is iteratively optimized. After each iteration, it is checked whether the quantum particle swarm algorithm converges, that is, whether a better control parameter combination is found; Substitute the optimized maximum system total damping coefficient into the additional damping controller and update the parameter settings of the controller; Start the additional damping controller to monitor the operating data of the grid-type PMSG wind power system in real time, compare the new monitoring data with the preset standard value range, and verify the optimization effect; If the new comparison error data still exceeds the maximum value of the preset error range, the iterative optimization of the maximum system total damping coefficient will continue until a control parameter combination that meets the conditions is obtained. When the comparison error data is within the preset error range, the optimization process is terminated, and the control parameters executed by the additional damping controller at this time are the optimized maximum system total damping coefficient.

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