Intelligent perception and active support system and method based on wind farm network source status

Abstract

The present invention relates to the field of new energy active support technology, and more specifically to an intelligent perception and active support system based on the grid source status of a wind farm, including a panoramic monitoring unit for wind farm grid source status perception, a dynamic evaluation unit for wind farm real-time adjustment capability, a grid source status perception unit, a wind farm frequency active support unit, and a wind farm frequency and voltage active support coordination unit. The present invention can improve the grid-connected stable operation and active support level of large-scale wind farms under resource fluctuations and grid operating condition changes.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy active support, and more specifically to an intelligent sensing and active support system and method based on wind farm network source status. Background Art

[0002] At present, the construction of a new power system with new energy as the main body is accelerating. Wind power is the main installed capacity, but wind power does not have the ability to actively support. After a high proportion of wind power is connected, the regulation capacity of the power system continues to decline. In order to meet the green transformation of new energy and the safe supply of electricity and avoid grid-off accidents, new energy needs to actively sense grid disturbances, change from "passive adaptation" to "active support", avoid safety risks, and improve the quality of power supply of the grid.

[0003] There are many wind turbines in a wind farm and they are widely distributed. Their active and reactive outputs are highly dependent on highly uncertain external environmental factors and the operating status of highly controllable energy converters. Active support for efficient coordination is difficult and the risk of safe and stable operation is high. It is necessary to perceive the operating conditions in the wind farm in real time to ensure that new energy effectively participates in the frequency stability support of the power grid.

[0004] The multiple types of equipment in wind farms increase the complexity of system operation, making it difficult for traditional measurement methods to meet multiple uncertainties (unit output, disturbance category, etc.). Therefore, achieving a balance between the two goals of "maintaining wind turbine stability" and "improving system frequency response" is a key difficulty.

[0005] In the new energy field with active control, a certain amount of energy storage is configured to assist wind power in participating in the frequency regulation of the power system, which can not only improve the utilization rate of natural resources but also reduce the fluctuation of system frequency. However, considering the cost of energy storage, it is necessary to reasonably utilize the relationship between energy storage and wind power, maximize the use of limited energy storage, and reduce the configuration of energy storage capacity. Summary of the invention

[0006] In view of the above requirements and technical defects, the present invention proposes an intelligent perception and active support system and method based on the grid source status of a wind farm, so as to improve the grid-connected stable operation and active support level of large-scale wind farms under resource fluctuations and changes in grid operating conditions.

[0007] In order to achieve the purpose of the present invention, the technical solution adopted is: an intelligent perception and active support system based on the state of the wind farm network source, including: a panoramic monitoring unit for sensing the state of the wind farm network source, used to realize the collection and fusion of multi-source data of the wind farm;

[0008] A dynamic evaluation unit for real-time regulation capability of a wind farm, which constructs a regulation capability mathematical model based on the multi-source data of the wind farm according to the multi-source data of the wind farm, determines an aggregation model and an evaluation method for the real-time regulation capability of the wind farm, and realizes a dynamic evaluation of the real-time regulation capability of the wind farm;

[0009] A grid source state perception unit, which evaluates the grid strength in real time based on the dynamic perception of the source grid state according to the multi-source data of the wind farm;

[0010] The wind farm frequency active support unit is used to determine the active power distribution method of the wind farm based on the dynamic evaluation of the real-time regulation capability of the wind farm, establish a fast active power control method of wind turbines based on the coordination of pitch and torque, create a dynamic active power distribution and parameter coordinated adjustment method that meets the active regulation amplitude and response delay time of the wind farm, and realize the decentralized coordinated frequency active support control of the wind farm;

[0011] The wind farm frequency and voltage active support collaborative unit is used to explore the dominant factors that affect the frequency / voltage active support performance under grid strength; establish a wind farm to achieve dynamic reactive power rapid distribution under voltage disturbance conditions; take active / reactive power margin and voltage / frequency qualification rate as optimization targets, create a multi-objective optimization method based on grid frequency / voltage stability, and derive a wind farm frequency rapid response and voltage active support collaborative optimization control method under weak grid conditions.

[0012] As an optimization scheme of the present invention, the panoramic monitoring unit for wind farm network source status perception is specifically used to realize synchronous processing and fusion of multi-source data based on the installation locations and data acquisition conditions of wind turbines, SVG and energy storage systems, communication network topology, data interaction interface, data volume, data type, data density and the presence or absence of data time stamps by adopting data processing methods such as format conversion, elimination, filling and interpolation.

[0013] As an optimization solution of the present invention, the dynamic evaluation unit of the real-time regulation capability of the wind farm is specifically used for:

[0014] Construct the regulation capability model of wind turbines, SVG and energy storage system in wind farms;

[0015] Combine numerical calculation and simulation analysis to quantify the regulation capability of wind turbines, SVG and energy storage system in wind farms under wind resource constraints, energy storage SOC constraints and equipment overcurrent constraints;

[0016] Extract key information that affects regulation capability, select time series operation data based on key information, and realize online dynamic measurement of regulation capability of wind turbines, SVG and energy storage systems in wind farms;

[0017] Analyze the historical operation data of the wind farm, mine the factors affecting the regulation ability of the wind farm, combine the distribution characteristics of the equipment in the wind farm, the real-time operation status of the equipment and the online dynamic measurement of the regulation ability, and use the capacity-weighted method to determine the real-time regulation ability aggregation model and evaluation method of the wind farm, so as to realize the dynamic evaluation of the real-time regulation ability of the wind farm.

[0018] As an optimized solution of the present invention, the network-source state perception unit is specifically used for:

[0019] Based on the influence of resource load fluctuations on the measurement data at the wind farm connection point under normal operating conditions, combined with the grid-connected operation data of the wind farm, select a suitable deep learning network model to realize the perception of the changes in the grid operation state of the wind farm; analyze the network-source characteristics closely related to the grid strength and the measurement data related to the characteristics, and through the dynamic measurement method of the source-side equipment state based on the equipment operation data, combined with the dynamic perception of the network-source state, evaluate the grid strength in real time.

[0020] As an optimized solution of the present invention, the wind farm frequency and voltage active support coordination unit is specifically used for:

[0021] Based on the spatio-temporal distribution characteristics of the power generation units in the wind farm, analyze the influence of resource fluctuations on the output characteristics of the wind farm, select the clustering index suitable for the wind farm, and use the k-mean clustering method to establish a dynamic mathematical model of the wind farm involving resource fluctuations, and obtain the source-network-load power flow distribution law during the frequency / voltage transient fault process;

[0022] Based on the topological structure of the complex power grid, distinguish the influence laws of grid strength, transmission lines, local load and reactive power compensation on the power change during the transient process of the complex power grid, establish a mathematical model that can characterize the power change characteristics during the transient process of the complex power grid, and obtain the power balance theory;

[0023] Analyze the source-network-load power flow distribution law during the frequency / voltage transient fault process, and based on the power balance theory, establish a power balance equation between the complex power grid and the wind farm, and reveal the coupling mechanism of the frequency / voltage active support of the wind farm under the complex power grid and resource fluctuations;

[0024] Distinguish the interaction between grid strength and the output characteristics of the wind farm, study the influence and change laws of the wind power control link and parameters on the support performance of power grids with different strengths, and mine the dominant factors affecting the frequency / voltage active support performance under voltage disturbances;

[0025] Under voltage disturbance, according to the voltage surge, voltage sag, three-phase balance status and voltage fluctuation duration at the wind farm grid connection point, a cluster analysis of the wind farm reactive voltage is conducted. Based on the reactive output capacity and dynamic response performance of the wind turbines, multiple sets of dynamic reactive compensation devices and energy storage devices in the wind farm, a parallel operation coordinated control method is proposed that takes into account the compensation consistency, reactive control response time and voltage control accuracy.

[0026] According to the reactive margin of wind turbines, dynamic reactive compensation devices, and energy storage devices, and based on the reactive power replacement strategy between different reactive devices, the dynamic reactive reserve of the wind farm is optimized;

[0027] Based on the short-circuit ratio of the wind farm grid connection point, with the minimum reactive power loss and the maximum reactive power margin as the optimization objectives, and with the reactive power control response time and voltage qualification rate of the wind farm as the constraints, a dynamic reactive power optimization control strategy and parameter coordination method for the wind farm under different voltage disturbance conditions are proposed. Based on the dynamic estimation results of the real-time reactive power regulation capability of the wind farm, a dynamic reactive power rapid allocation method for the wind farm is proposed.

[0028] In order to optimize the frequency / voltage active support performance, under weak grid conditions, based on the basic information of the power generation units in the station and the station structure, the influence and change law of the regional distribution, unit type, control structure and parameters of the station power generation units on the support performance of grids of different strengths are analyzed, and the frequency / voltage support performance is considered to establish a multi-objective unified optimization model for wind farm active support.

[0029] Taking power loss, active / reactive power margin, operation economy and voltage / frequency qualification rate as optimization targets, a frequency / voltage active support capability evaluation method under weak power grid conditions is proposed by using multi-objective optimization theory, and a multi-objective optimization method for power grid frequency / voltage stability is obtained.

[0030] Taking the multi-objective optimization results of wind farm active support as the design criteria, the station-level and equipment-level control strategies are designed and studied. Based on the basic theory of collaborative control, a wind farm frequency rapid response and voltage active support control system architecture with frequency / voltage support collaboration is constructed, and collaborative optimization control of wind farm frequency rapid response and voltage active support under weak grid conditions is carried out.

[0031] As an optimization solution of the present invention, the panoramic monitoring unit of the wind farm network source state perception is also used to reasonably configure the capacity of the energy storage system participating in wind power frequency regulation. es for:

[0032]

[0033] Where △P is the maximum missing or excess power within △T, η DC / DC , η DC / ACand η C , η D are the charging and discharging efficiencies of the converter and the energy storage system, respectively;

[0034] The state of charge of the energy storage system is related to the rated capacity of the energy storage Es, and combined with the state of charge at the kth moment, we get:

[0035]

[0036] In the formula, is the missing or excess power at the i-th moment within △T, S SOC,ref is the initial value of the state of charge, S SOC,max and S SOC,min are the upper and lower limits of the state of charge of the energy storage system, and the cost of the energy storage system F total The expression is:

[0037] F total =F bat +F pcs +F bop

[0038] Among them, F bat is the cost of energy storage device, F pcs is the power conversion system cost, F bop Cost of auxiliary equipment;

[0039] The cost of energy storage system due to replacement of SOC body F rep for:

[0040] In the formula, α is the average annual reduction rate of the energy storage device cost, k is the number of SOC replacements, k = N / n-1, n is the SOC life span, C E is the unit energy price of the energy storage system, E rated

[0041] is the total energy of the energy storage system, η is the conversion efficiency of the energy storage system,

[0042] Fixed operation and maintenance cost of energy storage system F POM for:

[0043] F POM =C f ·P es

[0044] In the formula, C f is the operation and maintenance cost per unit power, and the life cycle cost F LCC for:

[0045] F LCC =F total +F rep +FPOM +F VOM

[0046] In the formula, F POM The operation and maintenance cost F determined by the charging and discharging of the energy storage system VOM .

[0047] As an optimization scheme of the present invention, under the condition of satisfying the energy storage system operation control performance constraint, when the energy storage capacity is configured with the optimal frequency regulation effect as the objective function, the sample standard deviation is used as an indicator for evaluating the primary frequency regulation effect. The objective function is as follows:

[0048]

[0049] In the formula, △f i is the frequency deviation of the energy storage system, and λ is the weight coefficient of the objective function D.

[0050] In order to achieve the purpose of the present invention, the technical solution adopted is: an intelligent perception and active support method based on the wind farm network source state, comprising:

[0051] Collect multi-source data of wind farms;

[0052] According to the wind farm multi-source data, a regulation capability mathematical model based on the wind farm multi-source data is constructed, a wind farm real-time regulation capability aggregation model and an evaluation method are determined, and a dynamic evaluation of the wind farm real-time regulation capability is realized;

[0053] According to the multi-source data of the wind farm, based on the dynamic perception of the source network status, the strength of the power grid is evaluated in real time;

[0054] Based on the dynamic evaluation of the real-time regulation capability of the wind farm, determine the active power distribution method of the wind farm, establish a fast active power control method of wind turbines based on the coordination of pitch and torque, create a dynamic active power distribution and parameter coordination adjustment method that meets the active regulation amplitude and response delay time of the farm, and realize the decentralized coordinated frequency active support control of the wind farm;

[0055] Explore the dominant factors that affect the performance of active frequency / voltage support under grid strength; establish a wind farm under voltage disturbance conditions to achieve rapid dynamic reactive power distribution; take active / reactive power margin and voltage / frequency qualification rate as optimization targets, create a multi-objective optimization method based on grid frequency / voltage stability, and derive a coordinated optimization control method for rapid frequency response and active voltage support of wind farms under weak grid conditions.

[0056] As an optimization solution of the present invention, a method for allocating active power of a wind farm station specifically includes:

[0057] Construct a fast frequency regulation control model for a station containing energy storage equipment and wind turbines, and analyze the dynamic frequency regulation response of the station at multiple time scales through eigenvalue modal analysis;

[0058] Based on the dynamic frequency modulation response speed, frequency modulation amplitude and support duration of energy storage equipment and wind turbines at different time scales, the frequency modulation control parameters of wind turbines and energy storage equipment that meet the requirements of different time scales and frequency modulation responses are obtained;

[0059] Taking into account the station collection topology, electrical distance and communication delay, a system frequency stability, rapidity and economy are established. Based on the dynamic estimation of the real-time active power regulation capability of the wind farm, the rapid frequency regulation control parameters of wind turbines and energy storage equipment that meet multiple time scales are obtained, and the active coordinated allocation principle of wind turbine active standby and energy storage capacity that takes into account both economy and fast frequency response performance is proposed.

[0060] As an optimization scheme of the present invention, a fast active power control method of a wind turbine generator system based on the coordinated cooperation of pitch control and torque specifically includes:

[0061] Establish a fast active power control model for wind turbines that includes the dynamic characteristics of transmission chain torsion;

[0062] Analyze the electromagnetic torque response characteristics of the fast active power control model and the load response change characteristics of the mechanical components under different working conditions. By changing the torque change rate and pitch rate, extract the influencing factors and boundary conditions that restrict the fast active power control. Based on the data addressing method, obtain the active power change rate and active power change amplitude under different operating conditions of the wind turbine under the unit load and electrical constraints.

[0063] Based on the fast active power control boundary of wind turbines, the mapping relationship between the unit operating status and the fast active power control capability is established. With the optimal tracking of active command deviation and fast dynamic response performance as the control target, an active power control method that meets the fast active power control requirements and coordinates the optimization of pitch and torque is proposed. The influence of fast active power control on the stability and dynamic performance of various equipment in the wind farm is obtained.

[0064] Based on the spatial distribution of each device in the wind farm, resource characteristics and station frequency control mode, the influence of frequency control parameters on frequency response performance is obtained;

[0065] Through the participating factor analysis method, the influence of frequency regulation control parameters on system frequency changes is quantified, and the key control parameters affecting the rapid frequency regulation characteristics of wind farms are extracted; and combined with the dynamic estimation of the real-time active power regulation capability of wind farms and the frequency regulation requirements, a control strategy combining single-machine autonomous response and station centralized response is created, and a dynamic active power allocation and parameter coordinated adjustment method that meets the different active power regulation amplitudes and response times of the stations is obtained.

[0066] The present invention has positive effects: 1) The present invention realizes high-frequency, high-precision real-time evaluation of wind farm regulation capability and grid strength through the cooperation of the panoramic monitoring unit for wind farm grid source state perception, the dynamic evaluation unit for wind farm real-time regulation capability and the grid source state perception unit; the wind farm frequency active support unit breaks through the active frequency support of the grid source coordinated frequency / voltage active support in the wind farm station from the aspect of active frequency support, and the wind farm frequency and voltage active support coordination unit breaks through the active frequency and voltage support of the grid source coordinated frequency / voltage active support in the wind farm station from the aspect of active frequency support, improves the active support performance of the station, and fully adapts to the wind resource characteristics and complex grid conditions. Wind farm frequency / voltage multi-source coordinated control;

[0067] 2) The present invention obtains an intelligent perception method of wind farm network source status based on multi-source data drive, which can dynamically evaluate the real-time regulation capability of the wind farm and at the same time, can perform real-time evaluation of the strength of the power grid;

[0068] 3) The present invention proposes a wind farm multi-mode rapid frequency support technology that adapts to resource characteristics and a wind farm frequency rapid response and voltage active support coordinated optimization control strategy;

[0069] 4) The present invention comprehensively considers the energy storage cost and reasonably configures the capacity of the energy storage system, which not only improves the utilization rate of resources but also reduces the overall frequency fluctuation, maximizes the utilization of limited energy storage, and reduces the configuration of energy storage capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0071] Figure 1 It is a functional block diagram of the system of the present invention. DETAILED DESCRIPTION

[0072] like Figure 1 As shown, the present invention discloses an intelligent perception and active support system based on the grid source status of a wind farm, including a panoramic monitoring unit for sensing the grid source status of a wind farm, a dynamic evaluation unit for real-time regulation capability of a wind farm, a grid source status sensing unit, a wind farm frequency active support unit, and a wind farm frequency and voltage active support coordination unit:

[0073] Panoramic monitoring unit for wind farm network source status perception: realizes the collection and integration of multi-source data of wind farms, and provides data support for the dynamic evaluation unit of wind farm real-time regulation capability;

[0074] Taking into account factors such as the installation locations of wind turbines, SVG and energy storage systems in the wind farm, data collection conditions, communication network topology, supported communication protocols, etc., a panoramic monitoring unit architecture for wind farm network source status perception is designed based on the principles of modular and layered design; based on the communication access method of wind turbines, SVG and energy storage systems in the wind farm and data characteristics such as data volume, data type, data density, and presence or absence of data time stamps of multi-source data collection, a multi-source data fusion method (based on weighted average method, feature extraction method, model-based method and decision rule-based method) of flexible adjustment resources is used to select an appropriate data fusion level to achieve multi-source data fusion, so as to provide data support for the dynamic evaluation of flexible adjustment resources.

[0075] The dynamic evaluation unit of the real-time regulation capability of the wind farm constructs a regulation capability mathematical model based on the multi-source data of the wind farm according to the multi-source data of the wind farm, determines the aggregation model and evaluation method of the real-time regulation capability of the wind farm, and realizes the dynamic evaluation of the real-time regulation capability of the wind farm.

[0076] The dynamic evaluation unit of the real-time regulation capability of the wind farm includes:

[0077] S2-1. Construct the regulation capability model of wind farm wind turbines, SVG (high voltage static VAR generator) and energy storage system;

[0078] The regulation capability model of wind turbines is:

[0079] The regulation capability model of SVG is as follows: the maximum adjustable reactive power of SVG is determined according to the active output value and the rated capacity of SVG.

[0080] Regulation capability model of energy storage system:

[0081]

[0082] Where, X capr , X capd are the upward adjustment capacity and downward adjustment capacity of the energy storage device in period t respectively; P f (t), P c (t) is the discharge power and charging power of the energy storage system during period t; SOC min , SOC max are the lower and upper limits of the energy storage capacity of the energy storage system respectively; τ is the scheduling duration, and SOC(t) is the storage capacity of the energy storage system at time t.

[0083] S2-2. Combine numerical calculation and simulation analysis to quantify the regulation capability of wind turbines, SVG and energy storage system in wind farms under wind resource constraints, energy storage SOC constraints and equipment overcurrent constraints; energy storage constrains the SOC value. In the absence of disturbances, the energy storage system is charged at a constant power to keep the SOC in a reasonable range.

[0084] Energy storage SOC constraints:

[0085] G(t)=G(t-1)+sgn(q SOC (t)·η ba )∫q SOC (t)dt

[0086] G min (t)≤G(t)≤G max (t)

[0087]

[0088] Where: G(i,t) is the capacity of energy storage at time t with SOC constraint, G(i,t-1) is the capacity of energy storage at time t-1 with SOC constraint, η ba The energy storage SOC constrains the charging and discharging efficiency, G min (t) is the minimum capacity constrained by the energy storage SOC, G max (t) is the maximum capacity of energy storage SOC constraint, The minimum charge and discharge power at time t is constrained by the energy storage SOC, is the maximum charge and discharge power at time t constrained by the energy storage SOC, q soc (t) is the charge and discharge power at time t constrained by the energy storage SOC;

[0089] S2-3, extract key information that affects the regulation capability, select time series operation data based on the key information, and realize online dynamic measurement of the regulation capability of wind turbines, SVG and energy storage systems in wind farms;

[0090] S2-4. Analyze the historical operation data of the wind farm, explore the factors that affect the regulation capacity of the wind farm, combine the distribution characteristics of the equipment in the wind farm, the real-time operation status of the equipment and the online dynamic measurement of the regulation capacity, and use the capacity weighted method to determine the wind farm real-time regulation capacity aggregation model and evaluation method, so as to realize the dynamic evaluation of the wind farm real-time regulation capacity.

[0091] By constructing a mathematical model of the regulation capacity including wind turbines, SVG and energy storage systems, and combining numerical calculation and simulation analysis to quantify the regulation and control capabilities of various types of power equipment in wind farms (wind turbines, SVG and energy storage systems) under wind resource constraints, energy storage SOC constraints and equipment overcurrent constraints, the key information that affects the dynamic regulation capability of the equipment is extracted, and the time series operation data is selected based on the key information. A data mining model based on time series measurement data is established to realize online dynamic measurement of the regulation capabilities of various types of equipment based on machine learning.

[0092] Analyze the historical operation data of the wind farm, explore the degree and law of the influence of each influencing factor individually / collectively on the regulation capacity of the wind farm, combine the distribution characteristics of the equipment in the wind farm, the real-time operation status of the equipment and the real-time regulation capacity of the equipment, and use the capacity weighted method to determine the aggregation model and evaluation method of the real-time regulation capacity of the wind farm, so as to realize the dynamic evaluation of the real-time regulation capacity of the wind farm.

[0093] The grid source status perception unit uses the data collected by the panoramic monitoring unit of the wind farm grid source status perception to evaluate the grid strength in real time based on the dynamic perception of the source network status; the grid source status perception unit specifically includes: based on the impact of resource load fluctuations on the measurement data of the wind farm access point under normal operating conditions, combined with the wind farm grid-connected operation data, selecting a suitable deep learning network model to realize the perception of changes in the wind farm grid operation status; analyzing the grid source characteristics closely related to the grid strength and the measurement data related to the characteristics, and through the source side equipment status dynamic measurement method based on the equipment operation data, combined with the dynamic perception of the source network status, to evaluate the grid strength in real time.

[0094] The weighted clustering algorithm in the machine learning algorithm is used to calculate the distance from the indicator to the cluster center. The evaluation indicator is selected according to the distance. The selected indicator is used as the input variable of the deep learning network model. The integral value of the indicator and the weight is calculated in the hidden layer. The wind farm power grid operation status classification table is compared according to the score comparison, and the wind farm power grid operation status is obtained in the output layer.

[0095] The wind farm frequency active support unit includes a station active power distribution module, a wind turbine fast active power regulation module and a station decentralized coordinated frequency active support module; the station active power distribution module proposes a wind farm station active power distribution method based on a dynamic evaluation of the wind farm's real-time regulation capability; the wind turbine fast active power regulation module establishes a wind turbine fast active power control method based on pitch and torque coordination; the station decentralized coordinated frequency active support module creates a dynamic active power distribution and parameter coordinated adjustment method that meets the station active power regulation amplitude and response delay time, and realizes decentralized coordinated frequency active support control of wind farms;

[0096] The wind farm frequency and voltage active support collaborative unit explores the dominant factors that affect the frequency / voltage active support performance under grid strength; establishes a wind farm under voltage disturbance conditions to achieve dynamic reactive power rapid distribution; takes active / reactive power margin and voltage / frequency qualification rate as optimization targets, creates a multi-objective optimization method based on grid frequency / voltage stability, and derives a collaborative optimization control method for wind farm frequency rapid response and voltage active support under weak grid conditions.

[0097] The active power distribution method of a wind farm station specifically includes:

[0098] S4-1-1. Construct a fast frequency regulation control model for a station containing energy storage equipment and wind turbines, and analyze the dynamic frequency regulation response of the station at multiple time scales through eigenvalue modal analysis;

[0099] S4-1-2. Based on the frequency modulation response speed, frequency modulation amplitude and support duration of energy storage equipment and wind turbines at different time scales, obtain the frequency modulation parameters of wind turbines and energy storage equipment that meet the requirements of different time scales and frequency modulation responses;

[0100] S4-1-3. Considering the site collection topology, electrical distance and communication delay, establish a system frequency stability, rapidity and economy, based on the dynamic estimation of the real-time regulation capability of the wind farm active power, obtain the rapid frequency regulation control parameters of wind turbines and energy storage equipment that meet multiple time scales, and propose the active coordination allocation principle of wind turbine active reserve and energy storage capacity that takes into account both economy and rapid frequency response performance;

[0101] The fast active power control method of wind turbines based on the coordinated coordination of pitch control and torque includes:

[0102] S4-2-1. Establish a fast active power control model for wind turbines that includes the dynamic characteristics of transmission chain torsion;

[0103] S4-2-2. Analyze the electromagnetic torque response characteristics of the model and the load response change characteristics of the mechanical components during rapid active control under different working conditions. By changing the torque change rate and pitch rate, extract the influencing factors and boundary conditions that restrict rapid active control. Based on the data addressing method, obtain the active change rate and active change amplitude of the wind turbine under different operating conditions under the unit load and electrical constraints;

[0104] S4-2-3. Based on the fast active power control boundary of wind turbines, the mapping relationship between the unit operating status and the fast active power control capability is established. With the optimal tracking of active power command deviation and fast dynamic response performance as the control target, an active power control method that satisfies the fast active power control and coordinated optimization of pitch and torque is proposed, and the influence of fast active power control on the stability and dynamic performance of various equipment in the wind farm is obtained.

[0105] The station decentralized coordinated frequency active support module has the following specific implementation steps:

[0106] S4-3-1. Based on the spatial distribution of each device in the wind farm, resource characteristics and station frequency control mode, the influence of frequency control parameters on frequency response performance is obtained;

[0107] S4-3-2. Through the participating factor analysis method, the influence of frequency regulation control parameters on system frequency changes is quantified, the key control parameters that affect the rapid frequency regulation characteristics of the wind farm are extracted, and combined with the dynamic estimation of the real-time active power regulation capability of the wind farm and the frequency regulation requirements, a control strategy that combines single-machine autonomous response and station centralized response is created, and a dynamic active power allocation and parameter coordinated adjustment method that meets the different active power regulation amplitudes and response times of the stations is obtained.

[0108] Wind farm frequency and voltage active support coordination unit, the specific implementation steps are:

[0109] S5-1. Based on the spatiotemporal distribution characteristics of the power generation units in the wind farm, analyze the impact of resource fluctuations on the output characteristics of the wind farm, select clustering indicators suitable for the wind farm, and use the k-mean clustering method to establish a dynamic mathematical model of the wind farm involving resource fluctuations;

[0110] S5-2. Based on the topological structure of complex power grids, analyze the influence of power grid strength, transmission lines, local loads and reactive power compensation on power changes in transient processes of complex power grids, establish a mathematical model that can characterize the power change characteristics of transient processes of complex power grids, and obtain the power balance theory;

[0111] S5-3. Analyze the source-grid-load flow distribution law during frequency / voltage transient fault process, establish the power balance equation of complex power grid and wind farm based on power balance theory, and reveal the frequency / voltage active support coupling mechanism of wind farm under complex power grid and resource fluctuation;

[0112] S5-4. Analyze the interaction between grid strength and wind farm output characteristics, study the influence and change rules of wind power control links and parameters on grid support performance of different strengths, and explore the dominant factors affecting frequency / voltage active support performance under different grid strengths;

[0113] S5-5. Perform cluster analysis on the reactive voltage of the wind farm according to the voltage temporary rise, voltage temporary drop, three-phase balance status and voltage fluctuation duration at the wind farm grid connection point;

[0114] S5-6. Based on the reactive output capacity and dynamic response performance of wind turbines, multiple sets of dynamic reactive compensation devices and energy storage devices in the wind farm, a parallel operation coordinated control method is proposed to take into account the compensation consistency, reactive control response time and voltage control accuracy requirements;

[0115] S5-7. According to the reactive margin of wind turbines, dynamic reactive compensation devices and energy storage devices, and based on the reactive power replacement strategy between different reactive devices, the dynamic reactive reserve of the wind farm is optimized;

[0116] S5-8, short-circuit ratio of wind farm grid connection point, with minimum reactive power loss and maximum reactive power margin as optimization objectives, and reactive power control response time and voltage qualification rate of wind farm as constraints, a dynamic reactive power optimization control strategy and parameter coordination method for wind farm under different voltage disturbance conditions are proposed, and a dynamic reactive power rapid allocation method for wind farm is proposed based on the dynamic estimation results of the real-time reactive power regulation capability of wind farm;

[0117] S5-9. To optimize the frequency / voltage active support performance, based on the basic information of the power generation units in the station and the station structure, the influence and change rules of the regional distribution, unit type, control structure and parameters of the power generation units in the station on the support performance of power grids of different strengths are analyzed, and the frequency / voltage support performance is considered to establish a multi-objective unified optimization model for active support of wind farms;

[0118] S5-10. Taking power loss, active / reactive margin, operation economy and voltage / frequency qualification rate as optimization targets, and using multi-objective optimization theory, a frequency / voltage active support capability evaluation method under weak grid conditions is proposed, and a multi-objective optimization method for grid frequency / voltage stability is obtained;

[0119] S5-11. Taking the multi-objective optimization results of wind farm active support as the design criteria, design and study the station-level and equipment-level control strategies. Based on the basic theory of collaborative control, construct a wind farm frequency rapid response and voltage active support control system architecture that takes into account dispatch command / station control collaboration and frequency / voltage support collaboration, and perform collaborative optimization control of wind farm frequency rapid response and voltage active support under weak grid conditions.

[0120] The wind turbine's variable pitch and speed controller is used to adjust the standby power, so that the active power output can be quickly released or reduced to respond to the fluctuation of system frequency. The wind farm frequency and voltage active support collaborative unit proposes a dynamic reactive power optimization control strategy and parameter collaborative setting method for wind farms under different voltage disturbances.

[0121] Configuring a certain amount of energy storage system in the wind farm to assist wind power in participating in the frequency regulation of the power system can not only improve the utilization rate of natural resources but also reduce the fluctuation of the overall frequency. However, considering the cost of energy storage, it is necessary to rationally utilize the relationship between energy storage and wind power to maximize the use of limited energy storage and reduce the configuration of energy storage capacity.

[0122] It is necessary to reasonably configure the capacity of the energy storage system participating in wind power frequency regulation. es for:

[0123]

[0124] Where △P is the maximum missing or excess power within △T, η DC / DC , η DC / AC and η C , η D are the charging and discharging efficiencies of the converter and the energy storage system, respectively; the frequency modulation duration is △T, the charging of the energy storage system is positive, and the discharging is negative.

[0125] The energy storage system charge state is introduced to design the energy storage rated capacity Es. Combined with the charge state at the kth moment, we can get:

[0126]

[0127] In the formula, is the missing or excess power at the i-th moment within △T, S SOC,ref is the initial value of the state of charge, S SOC,max and S SOC,min are the upper and lower limits of the state of charge of the energy storage system, and the cost of the energy storage system F total The expression is:

[0128] F total =F bat +F pcs +F bop

[0129] Among them, F bat is the cost of energy storage device, F pcs is the power conversion system cost, F bop The cost of auxiliary equipment; the energy storage system is mainly composed of energy storage, power conversion system and some auxiliary equipment.

[0130] When the life cycle of the energy storage system is greater than that of the energy storage system, the energy storage system needs to replace the equipment, and the cost of replacing the SOC body is F rep for:

[0131] In the formula, α is the average annual reduction rate of the energy storage device cost, k is the number of SOC replacements, k = N / n-1, n is the SOC life span, C E is the unit energy price of the energy storage system, E rated is the total energy of the energy storage system, and η is the conversion efficiency of the energy storage system.

[0132] Fixed operation and maintenance cost of energy storage system F POM for:

[0133] F POM =C f·P es

[0134] In the formula, C f is the operation and maintenance cost per unit power, and the life cycle cost F LCC for:

[0135] F LCC =F total +F rep +F POM +F VOM

[0136] In the formula, F POM The variable operation and maintenance cost F determined by the charging and discharging of the energy storage system VOM .

[0137] Under the condition of satisfying the operation control performance constraints of the energy storage system, when the energy storage capacity is configured with the optimal frequency regulation effect as the objective function, the sample standard deviation is used as an indicator to evaluate the primary frequency regulation effect. The objective function is as follows:

[0138]

[0139] In the formula, △f i is the deviation of the energy storage system frequency, and λ is the weight coefficient of the objective function D. The smaller D is, the better the comprehensive effect of frequency regulation and economy is. In addition, in order to prevent overcharging and overdischarging of the battery and extend the battery life, the state of charge is used as a constraint.

[0140] The capacity optimization configuration strategy with the goal of optimizing frequency regulation effect and economic efficiency is to use energy storage systems D and F. LCC The objective function is to minimize the sum of the two, and the state of charge of the energy storage system is taken as the constraint condition. The optimal combination solution of the inertia constant and the primary frequency modulation coefficient of the energy storage system is determined by the exhaustive method, and the corresponding Pes and Es under the combination solution are calculated.

[0141] The present invention also discloses a method for performing intelligent sensing and active support using the above-mentioned intelligent sensing and active support system based on the wind farm network source state, comprising the following steps:

[0142] Collect multi-source data of wind farms;

[0143] According to the wind farm multi-source data, a regulation capability mathematical model based on the wind farm multi-source data is constructed, a wind farm real-time regulation capability aggregation model and an evaluation method are determined, and a dynamic evaluation of the wind farm real-time regulation capability is realized;

[0144] According to the multi-source data of the wind farm, based on the dynamic perception of the source network status, the strength of the power grid is evaluated in real time;

[0145] Based on the dynamic evaluation of the real-time regulation capability of the wind farm, determine the active power distribution method of the wind farm, establish a fast active power control method of wind turbines based on the coordination of pitch and torque, create a dynamic active power distribution and parameter coordination adjustment method that meets the active regulation amplitude and response delay time of the farm, and realize the decentralized coordinated frequency active support control of the wind farm;

[0146] Explore the dominant factors that affect the performance of active frequency / voltage support under grid strength; establish a wind farm under voltage disturbance conditions to achieve rapid dynamic reactive power distribution; take active / reactive power margin and voltage / frequency qualification rate as optimization targets, create a multi-objective optimization method based on grid frequency / voltage stability, and derive a coordinated optimization control method for rapid frequency response and active voltage support of wind farms under weak grid conditions.

[0147] The active power distribution method of a wind farm station specifically includes:

[0148] Construct a fast frequency regulation control model for a station containing energy storage equipment and wind turbines, and analyze the dynamic frequency regulation response of the station at multiple time scales through eigenvalue modal analysis;

[0149] Based on the dynamic frequency modulation response speed, frequency modulation amplitude and support duration of energy storage equipment and wind turbines at different time scales, the frequency modulation control parameters of wind turbines and energy storage equipment that meet the requirements of different time scales and frequency modulation responses are obtained;

[0150] Taking into account the station collection topology, electrical distance and communication delay, a system frequency stability, rapidity and economy are established. Based on the dynamic estimation of the real-time active power regulation capability of the wind farm, the rapid frequency regulation control parameters of wind turbines and energy storage equipment that meet multiple time scales are obtained, and the active coordinated allocation principle of wind turbine active standby and energy storage capacity that takes into account both economy and fast frequency response performance is proposed.

[0151] A fast active power control method for wind turbines based on the coordinated coordination of pitch control and torque includes:

[0152] Establish a fast active power control model for wind turbines that includes the dynamic characteristics of transmission chain torsion;

[0153] Analyze the electromagnetic torque response characteristics of the fast active power control model and the load response change characteristics of the mechanical components under different working conditions. By changing the torque change rate and pitch rate, extract the influencing factors and boundary conditions that restrict the fast active power control. Based on the data addressing method, obtain the active power change rate and active power change amplitude under different operating conditions of the wind turbine under the unit load and electrical constraints.

[0154] Based on the fast active power control boundary of wind turbines, the mapping relationship between the unit operating status and the fast active power control capability is established. With the optimal tracking of active command deviation and fast dynamic response performance as the control target, an active power control method that meets the fast active power control requirements and coordinates the optimization of pitch and torque is proposed. The influence of fast active power control on the stability and dynamic performance of various equipment in the wind farm is obtained.

[0155] Based on the spatial distribution of each device in the wind farm, resource characteristics and station frequency control mode, the influence of frequency control parameters on frequency response performance is obtained;

[0156] Through the participating factor analysis method, the influence of frequency regulation control parameters on system frequency changes is quantified, and the key control parameters affecting the rapid frequency regulation characteristics of wind farms are extracted; and combined with the dynamic estimation of the real-time active power regulation capability of wind farms and the frequency regulation requirements, a control strategy combining single-machine autonomous response and station centralized response is created, and a dynamic active power allocation and parameter coordinated adjustment method that meets the different active power regulation amplitudes and response times of the stations is obtained.

[0157] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific 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 in the protection scope of the present invention.

Claims

1. Intelligent perception and active support system based on wind farm grid source status, characterized by: include: A panoramic monitoring unit for wind farm network source status perception, used to collect and integrate multi-source data from wind farms; A dynamic evaluation unit for real-time regulation capability of a wind farm, which constructs a regulation capability mathematical model based on the multi-source data of the wind farm according to the multi-source data of the wind farm, determines an aggregation model and an evaluation method for the real-time regulation capability of the wind farm, and realizes a dynamic evaluation of the real-time regulation capability of the wind farm; A grid source state perception unit, which evaluates the grid strength in real time based on the dynamic perception of the source grid state according to the multi-source data of the wind farm; The wind farm frequency active support unit is used to determine the active power distribution method of the wind farm based on the dynamic evaluation of the real-time regulation capability of the wind farm, establish a fast active power control method of wind turbines based on the coordination of pitch and torque, create a dynamic active power distribution and parameter coordinated adjustment method that meets the active regulation amplitude and response delay time of the wind farm, and realize the decentralized coordinated frequency active support control of the wind farm; Wind farm frequency and voltage active support coordination unit, used to explore the dominant factors affecting the performance of frequency / voltage active support under grid strength; Establish a wind farm to achieve rapid dynamic reactive power distribution in the event of voltage disturbance; Taking active / reactive power margin and voltage / frequency qualification rate as optimization objectives, a multi-objective optimization method based on grid frequency / voltage stability is created, and a coordinated optimization control method for wind farm frequency rapid response and voltage active support under weak grid conditions is obtained.

2. The intelligent sensing and active support system based on wind farm network source status according to claim 1 is characterized in that: The panoramic monitoring unit for wind farm network source status perception is specifically used to achieve synchronous processing and fusion of multi-source data based on the installation locations and data acquisition conditions of wind turbines, SVG and energy storage systems, communication network topology, data interaction interface, data volume, data type, data density and the presence or absence of data time stamps by adopting data processing methods such as format conversion, elimination, filling and interpolation.

3. The intelligent sensing and active support system based on wind farm network source status according to claim 1 is characterized in that: The dynamic evaluation unit for the real-time regulation capability of a wind farm is specifically used for: Construct the regulation capability model of wind turbines, SVG and energy storage system in wind farms; Combine numerical calculation and simulation analysis to quantify the regulation capability of wind turbines, SVG and energy storage system in wind farms under wind resource constraints, energy storage SOC constraints and equipment overcurrent constraints; Extract key information that affects regulation capability, select time series operation data based on key information, and realize online dynamic measurement of regulation capability of wind turbines, SVG and energy storage systems in wind farms; Analyze the historical operation data of the wind farm, explore the factors that affect the regulation capacity of the wind farm, combine the distribution characteristics of the equipment in the wind farm, the real-time operation status of the equipment and the online dynamic measurement of the regulation capacity, and use the capacity weighted method to determine the wind farm real-time regulation capacity aggregation model and evaluation method, so as to realize the dynamic evaluation of the wind farm real-time regulation capacity.

4. The intelligent sensing and active support system based on wind farm network source status according to claim 1 is characterized in that: The network source status perception unit is specifically used for: Based on the impact of resource load fluctuations on the measurement data of wind farm access points under normal operating conditions, combined with the wind farm grid-connected operation data, a suitable deep learning network model is selected to realize the perception of changes in the wind farm grid operation status; Analyze the network source characteristics and measurement data related to the characteristics that are closely related to the strength of the power grid, and evaluate the strength of the power grid in real time through a dynamic measurement method of the source-side equipment status based on the equipment operation data, combined with the dynamic perception of the source network status.

5. The intelligent sensing and active support system based on wind farm network source status according to claim 1 is characterized in that: Wind farm frequency and voltage active support coordination unit, specifically used for: Based on the spatiotemporal distribution characteristics of the power generation units in the wind farm, the impact of resource fluctuations on the output characteristics of the wind farm is analyzed, and the clustering index suitable for the wind farm is selected. The k-mean clustering method is used to establish a dynamic mathematical model of the wind farm involving resource fluctuations, and the source-grid-load flow distribution law of the frequency / voltage transient fault process is obtained; Based on the topological structure of complex power grids, the influence of power grid strength, transmission lines, local loads and reactive power compensation on power changes in transient processes of complex power grids is analyzed, and a mathematical model that can characterize the power change characteristics of transient processes of complex power grids is established to obtain the power balance theory. Analyze the source-grid-load flow distribution law of frequency / voltage transient fault process, establish the power balance equation of complex power grid and wind farm based on power balance theory, and reveal the frequency / voltage active support coupling mechanism of wind farm under complex power grid and resource fluctuation; Analyze the interaction between grid strength and wind farm output characteristics, study the influence and change rules of wind power control links and parameters on grid support performance of different strengths, and explore the dominant factors affecting frequency / voltage active support performance under voltage disturbance; Under voltage disturbance, according to the voltage surge, voltage sag, three-phase balance status and voltage fluctuation duration at the wind farm grid connection point, a cluster analysis of the wind farm reactive voltage is conducted. Based on the reactive output capacity and dynamic response performance of the wind turbines, multiple sets of dynamic reactive compensation devices and energy storage devices in the wind farm, a parallel operation coordinated control method is proposed that takes into account the compensation consistency, reactive control response time and voltage control accuracy. According to the reactive margin of wind turbines, dynamic reactive compensation devices, and energy storage devices, and based on the reactive power replacement strategy between different reactive devices, the dynamic reactive reserve of the wind farm is optimized; Based on the short-circuit ratio of the wind farm grid connection point, with the minimum reactive power loss and the maximum reactive power margin as the optimization objectives, and with the reactive power control response time and voltage qualification rate of the wind farm as the constraints, a dynamic reactive power optimization control strategy and parameter coordination method for the wind farm under different voltage disturbance conditions are proposed. Based on the dynamic estimation results of the real-time reactive power regulation capability of the wind farm, a dynamic reactive power rapid allocation method for the wind farm is proposed. In order to optimize the frequency / voltage active support performance, under weak grid conditions, based on the basic information of the power generation units in the station and the station structure, the influence and change law of the regional distribution, unit type, control structure and parameters of the station power generation units on the support performance of grids of different strengths are analyzed, and the frequency / voltage support performance is considered to establish a multi-objective unified optimization model for wind farm active support. Taking power loss, active / reactive power margin, operation economy and voltage / frequency qualification rate as optimization targets, a frequency / voltage active support capability evaluation method under weak power grid conditions is proposed by using multi-objective optimization theory, and a multi-objective optimization method for power grid frequency / voltage stability is obtained. Taking the multi-objective optimization results of wind farm active support as the design criteria, the station-level and equipment-level control strategies are designed and studied. Based on the basic theory of collaborative control, a wind farm frequency rapid response and voltage active support control system architecture with frequency / voltage support collaboration is constructed, and collaborative optimization control of wind farm frequency rapid response and voltage active support under weak grid conditions is carried out.

6. The intelligent sensing and active support system based on wind farm network source status according to claim 1 is characterized in that: The panoramic monitoring unit of the wind farm grid source status perception is also used to reasonably configure the capacity of the energy storage system participating in wind power frequency regulation. es for: Where △P is the maximum missing or excess power within △T, η DC / DC , η DC / AC and η C , η D are the charging and discharging efficiencies of the converter and the energy storage system, respectively; The state of charge of the energy storage system is related to the energy storage rated capacity Es, and combined with the state of charge at the kth moment, we get: In the formula, is the missing or excess power at the i-th moment within △T, S SOC,ref is the initial value of the state of charge, S SOC,max and S SOC,min are the upper and lower limits of the state of charge of the energy storage system, and the cost of the energy storage system F total The expression is: F total =F bat +F pcs +F bop Among them, F bat is the cost of energy storage device, F pcs is the power conversion system cost, F bop Cost of auxiliary equipment; The cost of energy storage system due to replacement of SOC body F rep for: In the formula, α is the average annual reduction rate of the energy storage device cost, k is the number of SOC replacements, k = N / n-1, n is the SOC life span, C E is the unit energy price of the energy storage system, E rated is the total energy of the energy storage system, η is the conversion efficiency of the energy storage system, and the fixed operation and maintenance cost F of the energy storage system POM for: F POM =C f ·P es In the formula, C f is the operation and maintenance cost per unit power, and the life cycle cost F LCC for: F LCC =F total +F rep +F POM +F VOM In the formula, F POM The operation and maintenance cost F determined by the charging and discharging of the energy storage system VOM .

7. The intelligent sensing and active support system based on wind farm network source status according to claim 6 is characterized in that: Under the condition of satisfying the operation control performance constraints of the energy storage system, when the energy storage capacity is configured with the optimal frequency regulation effect as the objective function, the sample standard deviation is used as an indicator to evaluate the primary frequency regulation effect. The objective function is as follows: In the formula, △f i is the frequency deviation of the energy storage system, and λ is the weight coefficient of the objective function D.

8. An intelligent perception and active support method based on wind farm network source status, characterized in that: include: Collect multi-source data of wind farms; According to the wind farm multi-source data, a regulation capability mathematical model based on the wind farm multi-source data is constructed, a wind farm real-time regulation capability aggregation model and an evaluation method are determined, and a dynamic evaluation of the wind farm real-time regulation capability is realized; According to the multi-source data of the wind farm, based on the dynamic perception of the source network status, the strength of the power grid is evaluated in real time; Based on the dynamic evaluation of the real-time regulation capability of the wind farm, determine the active power distribution method of the wind farm, establish a fast active power control method of wind turbines based on the coordination of pitch and torque, create a dynamic active power distribution and parameter coordination adjustment method that meets the active regulation amplitude and response delay time of the farm, and realize the decentralized coordinated frequency active support control of the wind farm; Explore the dominant factors that affect the performance of active frequency / voltage support under grid strength; Establish a wind farm to achieve rapid dynamic reactive power distribution in the event of voltage disturbance; Taking active / reactive power margin and voltage / frequency qualification rate as optimization objectives, a multi-objective optimization method based on grid frequency / voltage stability is created, and a coordinated optimization control method for wind farm frequency rapid response and voltage active support under weak grid conditions is obtained.

9. The method for intelligent perception and active support based on wind farm network source status according to claim 8 is characterized in that: The active power distribution method of a wind farm station specifically includes: Construct a fast frequency regulation control model for a station containing energy storage equipment and wind turbines, and analyze the dynamic frequency regulation response of the station at multiple time scales through eigenvalue modal analysis; Based on the dynamic frequency modulation response speed, frequency modulation amplitude and support duration of energy storage equipment and wind turbines at different time scales, the frequency modulation control parameters of wind turbines and energy storage equipment that meet the requirements of different time scales and frequency modulation responses are obtained; Taking into account the station collection topology, electrical distance and communication delay, a system frequency stability, rapidity and economy are established. Based on the dynamic estimation of the real-time active power regulation capability of the wind farm, the rapid frequency regulation control parameters of wind turbines and energy storage equipment that meet multiple time scales are obtained, and the active coordinated allocation principle of wind turbine active standby and energy storage capacity that takes into account both economy and fast frequency response performance is proposed.

10. The method for intelligent perception and active support based on wind farm network source status according to claim 8, characterized in that: A fast active power control method for wind turbines based on the coordinated coordination of pitch control and torque includes: Establish a fast active power control model for wind turbines that includes the dynamic characteristics of transmission chain torsion; Analyze the electromagnetic torque response characteristics of the fast active power control model and the load response change characteristics of the mechanical components under different working conditions. By changing the torque change rate and pitch rate, extract the influencing factors and boundary conditions that restrict the fast active power control. Based on the data addressing method, obtain the active power change rate and active power change amplitude under different operating conditions of the wind turbine under the unit load and electrical constraints. Based on the fast active power control boundary of wind turbines, the mapping relationship between the unit operating status and the fast active power control capability is established. With the optimal tracking of active command deviation and fast dynamic response performance as the control target, an active power control method that meets the fast active power control requirements and coordinates the optimization of pitch and torque is proposed. The influence of fast active power control on the stability and dynamic performance of various equipment in the wind farm is obtained. Based on the spatial distribution of each device in the wind farm, resource characteristics and station frequency control mode, the influence of frequency control parameters on frequency response performance is obtained; Through the participating factor analysis method, the influence of frequency regulation control parameters on system frequency changes is quantified, and the key control parameters affecting the rapid frequency regulation characteristics of wind farms are extracted; and combined with the dynamic estimation of the real-time active power regulation capability of wind farms and the frequency regulation requirements, a control strategy combining single-machine autonomous response and station centralized response is created, and a dynamic active power allocation and parameter coordinated adjustment method that meets the different active power regulation amplitudes and response times of the stations is obtained.

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