Wind power inertia analysis method considering fan inertia support

Abstract

The invention discloses a wind power inertia analysis method considering fan inertia support, and relates to the technical field of wind power system control, and the method comprises the following steps: collecting dynamic response data of a target generator set under a preset disturbance condition, and building a double-loop dynamic model based on the dynamic response data; performing closed-loop coupling on the double-loop dynamic model and a preset power grid model, and constructing a unit-power grid joint dynamic simulation model; based on the joint dynamic simulation model, acquiring a multi-dimensional inertia response characteristic matrix; based on the inertia response characteristic matrix, obtaining a contribution value of a virtual inertia control layer to power grid frequency support; and dynamically adjusting control parameters of a virtual inertia controller in the target generator set according to the quantized contribution value. According to the method, the response characteristics of the wind turbine generator under different disturbances are simulated through the double-loop dynamic model and unit-power grid joint simulation, the inertia response characteristics are analyzed based on real-time data, the capability of efficiently and accurately adjusting the virtual inertia control layer is achieved, and the stability of the power grid is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power system control, and more particularly, to a wind power inertia analysis method considering wind turbine inertia support. BACKGROUND

[0002] With the increasing installed capacity of wind power in power systems, wind turbine inertia support has become an important means to ensure the stability of power grid frequency. The so-called wind turbine inertia support refers to the release or absorption of rotor kinetic energy by wind turbine under disturbance conditions to assist the power grid to maintain frequency stability. At present, wind turbines generally use double-fed induction generators or full-power converter structures, and the electrical isolation leads to the weakening of inertia characteristics, so it is urgent to realize virtual inertia response through control strategy.

[0003] In the prior art, wind turbine inertia support is usually achieved by fixed gain virtual inertia control or frequency deviation based feedforward / feedback regulation of MPPT layer output active power. This kind of method mainly evaluates the inertia contribution by overall power response curve, lacks phased quantitative analysis of inertia support stage, primary frequency modulation stage and recovery stage, and does not systematically evaluate and optimize different disturbance amplitudes and durations, resulting in the inability to accurately identify the actual contribution of each stage to the stability of power grid frequency, and the control parameters are difficult to dynamically adjust to adapt to complex disturbance conditions. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a wind power inertia analysis method considering wind turbine inertia support. By collecting wind turbine operation data and power grid frequency dynamic data, a double-loop dynamic model is constructed and coupled with the power grid model to form a simulation system. Under disturbance conditions, the inertia and frequency modulation response of each stage are calculated, the inertia response feature matrix is constructed, and the contribution value of the virtual inertia control layer is inversely calculated based on the matrix, so as to realize dynamic optimization adjustment, thereby solving the problems of phased quantitative analysis of inertia response and dynamic adjustment of control parameters in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: The wind power inertia analysis method considering wind turbine inertia support comprises the following steps: collecting dynamic response data of a target generator under a preset disturbance condition, and constructing a double-loop dynamic model based on the dynamic response data; coupling the double-loop dynamic model with a preset power grid model to construct a unit-grid joint dynamic simulation model for simulating the interaction between the unit and the grid; obtaining a multi-dimensional inertia response feature matrix based on the joint dynamic simulation model; obtaining the contribution value of the virtual inertia control layer to the frequency support of the power grid based on the inertia response feature matrix; and dynamically adjusting the control parameters of the virtual inertia controller in the target generator according to the quantitative contribution value.

[0006] In a preferred embodiment, the joint dynamic simulation model is used to obtain a multi-dimensional inertia response feature matrix, specifically: a disturbance event is set in the dynamic simulation model, dynamic simulation calculation is performed, inertia response values and frequency modulation response values at different time stages in the simulation data are extracted, and a multi-dimensional inertia response feature matrix is constructed based on the response values.

[0007] In a preferred embodiment, the double-loop dynamic model is constructed by: constructing a maximum power point tracking control layer dynamic model as an inner loop based on the rotor speed and the pitch angle through a nonlinear adaptive control algorithm; constructing a virtual inertia control layer dynamic model as an outer loop based on the grid frequency and its rate of change by using a feedforward-feedback composite control strategy to adjust the active power output of the converter; and coupling the inner loop and the outer loop to form the double-loop dynamic model.

[0008] In a preferred embodiment, the joint dynamic simulation model is used to obtain a multi-dimensional inertia response feature matrix through a linear adaptive control algorithm.

[0009] In a preferred embodiment, the feedforward-feedback composite control strategy is specifically: obtaining a grid frequency signal and its rate of change data; calculating a feedforward power increment through a feedforward control channel based on the rate of change of the grid frequency; calculating a feedback power increment through a feedback control channel based on the deviation of the grid frequency signal from the rated frequency; adding the feedforward power increment and the feedback power increment to obtain a comprehensive power adjustment amount; inputting the comprehensive power adjustment amount into the active power control of the converter to obtain an updated active power; The updated active power is used as the input for the next feedforward and feedback control calculation.

[0010] In a preferred embodiment, the double-loop dynamic model is coupled with a preset grid model in a closed loop to construct a unit-grid joint dynamic simulation model for simulating the interaction between the unit and the grid, specifically: a grid model is constructed with a frequency deviation as a state variable; an active power increment output by the double-loop dynamic model is decomposed into a virtual inertia control layer output and an MPPT layer output; a first coupling channel is established to input the active power increment output by the double-loop dynamic model into a power balance equation of the grid model for output coupling; a second coupling channel is established to feed back the frequency deviation output by the grid model to the virtual inertia control layer for input coupling; and the unit-grid joint dynamic simulation model is constructed through the input coupling and the output coupling.

[0011] In a preferred embodiment, the multi-dimensional inertia response feature matrix is obtained based on the joint dynamic simulation model, specifically: a disturbance sequence with different amplitudes and durations is constructed to form a disturbance signal; based on the disturbance signal, the instantaneous change rate and the recovery slope of the power grid frequency curve data of the simulation model output are calculated by using the variable step integral method; the instantaneous change rate and the recovery slope data are divided into inertia support stage data, primary frequency modulation stage data and recovery steady state stage data according to a preset time window, and the inertia response value and the frequency modulation response value of each stage are calculated; the inertia response value and the frequency modulation response value of each stage are normalized according to the disturbance amplitude and the duration to construct the inertia response feature matrix.

[0012] In a preferred embodiment, the control parameters of the virtual inertia controller in the target generator are dynamically adjusted, specifically: the inertia response feature matrix is calculated by stage inversion to obtain initial contribution values of each stage; the initial contribution values of each stage are optimized to generate optimized contribution values; the optimized contribution values are converted into parameter increments of the virtual inertia controller and updated to the virtual inertia control layer for dynamic adjustment.

[0013] In a preferred embodiment, the inertia response feature matrix is calculated by stage inversion to obtain initial contribution values of each stage, specifically: the stages include an inertia support stage, a primary frequency modulation stage and a recovery stage; the rotor kinetic energy change rate, the second derivative of the power grid frequency and the pitch angle change acceleration are collected to construct a three-dimensional phase space; the inertia support ring, the frequency modulation helix and the recovery steady state point are identified as geometric features through the three-dimensional phase space; and the initial contribution values of each stage are calculated according to the geometric features.

[0014] In a preferred embodiment, the initial contribution values of each stage are optimized to generate optimized contribution values, specifically: the stage performance evaluation vectors are calculated according to the initial contribution values of each stage and the corresponding power grid frequency response indicators; the optimization objective function is constructed based on the stage performance evaluation vectors to output comprehensive optimization indicators; the upper limit of the rotor kinetic energy reserve of the wind turbine, the power regulation range of the converter and the allowable fluctuation threshold of the power grid frequency are collected to construct a constraint condition set to output the allowed value range; and the optimized stage contribution values are obtained by using a constraint optimization algorithm based on the initial stage contribution values, the comprehensive optimization indicators and the allowed value range.

[0015] The wind turbine inertia support wind power inertia analysis method has the following technical effects and advantages: 1.The method for analyzing wind power inertia of the application, by constructing inertia response feature matrix and using three-dimensional phase space staging inversion algorithm, quantifies the frequency response contribution of wind turbine in inertia support stage, primary frequency modulation stage and recovery steady stage, realizes accurate evaluation of inertia output in each stage under different disturbance amplitude and duration, and solves the problem that the prior art only evaluates overall power curve and cannot analyze in stages.

[0016] 2.The method for analyzing wind power inertia of the application, by combining the inertia contribution value of each stage obtained by inversion with the upper limit of wind turbine rotor kinetic energy reserve, the power regulation range of the converter and the allowable fluctuation threshold of the grid frequency, and dynamically updating to the virtual inertia control layer, realizes online dynamic adjustment of virtual inertia output of the wind turbine under different disturbance conditions, and solves the problem that the control parameters are fixed and the response is not adaptive to various disturbances in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flowchart of the method for analyzing wind power inertia considering wind turbine inertia support of the application.

[0018] Figure 2 The structure diagram of the unit-grid combined dynamic simulation model of the application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0020] Embodiment 1, Figure 1 The method for analyzing wind power inertia considering wind turbine inertia support of the application is given, including: S1, collecting dynamic response data of the target generator set under a preset disturbance condition, and constructing a double-loop dynamic model based on the dynamic response data; In this embodiment, dynamic response data of the target generator set under a preset disturbance condition is collected, and a double-loop dynamic model is constructed based on the dynamic response data, specifically: The double-loop dynamic model includes a maximum power point tracking control layer and a virtual inertia control layer. Dynamic response data of the target generator set under a preset disturbance condition is collected through a rotor speed sensor, a pitch angle encoder and a power transmitter of the wind turbine, and the collected data includes rotor speed , pitch angle , actual output power of wind turbine operation data, based on the rotor speed and pitch angle, through the nonlinear adaptive control algorithm to build the maximum power point tracking control layer dynamic model, as the inner loop, through the grid synchronized phasor measurement unit to collect including the real-time frequency of the grid and its rate of change Based on the grid frequency dynamic data and its change rate, a feedforward-feedback composite control strategy is adopted to regulate the active power output of the converter. A dynamic model of the virtual inertia control layer is constructed as the outer loop, and a dual-loop dynamic model is formed by coupling the inner and outer loops.

[0021] By rotor speed and pitch angle Construct a nonlinear mapping model. The specific formula is:

[0022] in, is the air density, Fan blade radius, is the wind energy utilization coefficient, is the tip speed ratio and The nonlinear function of is the wind speed, measured by an anemometer.

[0023] Dynamic model function of the inner loop maximum power point tracking control layer of the dual-loop dynamic model By optimizing Get, specifically .

[0024] The nonlinear adaptive control algorithm is specifically: The power error is obtained by comparing the maximum power point function output power with the actual output power of the unit. , input the power error into the control gain update formula to obtain the updated control gain. The specific formula is:

[0025] in, for The control gain at time is the adaptive learning rate ( ), determines the convergence speed, is the nonlinear correction term ( When increasing, excessive gain growth is suppressed to avoid excessive pitch angle adjustment). Based on the updated control gain, the pitch angle adjustment amount is calculated using the nonlinear control law:

[0026] in, is a symbol function, which ensures the direction is correct. A new pitch angle is obtained, which is applied to the aerodynamic model of the wind turbine to recalculate , forming a closed loop.

[0027] The feedforward-feedback compound control strategy is specifically: The rate of change of the grid frequency is input into the feedforward control module to simulate the inertia characteristics of the synchronous machine and calculate the feedforward power increment:

[0028] wherein, is the feedforward gain, and the virtual inertia constant is related.

[0029] The deviation between the grid frequency signal and the rated frequency is calculated, and the grid frequency deviation is input into the feedback control module to calculate the feedback power increment:

[0030] wherein, is the proportional gain, which determines the frequency modulation accuracy, is the rated frequency.

[0031] The feedforward power increment and the feedback power increment are added to obtain the comprehensive power adjustment amount , which is input into the active power control of the converter to obtain the updated active power output , and the converter output power affects the grid frequency, and the PMU collects new frequency data and feeds back to the control layer.

[0032] S2, the double-loop dynamic model is coupled with the preset grid model to form a unit-grid joint dynamic simulation model for simulating the interaction between the unit and the grid. In this embodiment, the unit-grid joint dynamic simulation model is constructed, specifically: a grid model with frequency deviation as the state variable is constructed, specifically:

[0033] wherein, is the equivalent inertia constant of the grid, which reflects the system inertia, is the total active power increment of the unit, is the load disturbance power, is the grid damping coefficient.

[0034] The active power increment output by the double-loop dynamic model is decomposed into the virtual inertia control layer output and the MPPT layer output , the active power increment output by the double-loop dynamic model is input into the power balance equation of the grid model to realize output coupling, specifically as follows:

[0035] The unit power change directly affects the grid frequency dynamics.

[0036] The second coupling channel is established to feed back the frequency deviation output by the grid model to the virtual inertia control layer to realize input coupling, specifically as follows:

[0037] The frequency deviation acts on the unit power output through the outer loop control strategy.

[0038] Through the closed-loop connection of the output coupling and the input coupling, the double-loop dynamic model and the grid model are integrated, the unit output and the grid response interact, the unit-grid joint dynamic simulation model is formed, the joint dynamic simulation is realized, and the unit power response curve and the grid frequency change curve are output.

[0039] S3, based on the joint dynamic simulation model, a multi-dimensional inertia response feature matrix is obtained; In this embodiment, based on the joint dynamic simulation model, a multi-dimensional inertia response feature matrix is obtained, specifically as follows: In the dynamic simulation model, a disturbance event is set, and under the disturbance condition, the inertia response value and the frequency modulation response value of each stage are calculated through the simulation model, and an inertia response feature matrix is constructed, specifically as follows: In the dynamic simulation model, a disturbance event is set, and a power disturbance sequence with different amplitudes and durations is constructed According to the amplitude and duration combination, a plurality of disturbance sequences are generated to form a disturbance signal:

[0040] According to the grid load fluctuation range, the minimum amplitude and the maximum amplitude are set, and the amplitude step is determined, wherein is the number of amplitudes to be generated, and the amplitude sequence is obtained, and according to the grid frequency modulation characteristics, the shortest duration and the longest duration are set, and the time step is determined, and the duration sequence is obtained. The amplitude sequence and the duration sequence are used to form a disturbance parameter pair, and each pair Corresponding to a disturbance signal.

[0041] The multi-dimensional inertia response characteristic matrix is ​​obtained by the linear adaptive control algorithm, specifically: The generated disturbance signal is input into the simulation model, and the dynamic curve of the power grid frequency over time is solved by the variable step-size integration method, and the instantaneous rate of change and recovery slope are recorded. The step size can be automatically adjusted according to the rate of change of the system frequency. When the frequency changes rapidly, a small step size is used to ensure the calculation accuracy. When the frequency changes slowly, a large step size is used to improve the calculation efficiency and ensure the accuracy of the rapid change stage. Instantaneous rate of change:

[0042] in, is the current integration step size.

[0043] Recovery slope:

[0044] in, 、 The start and end time of each stage respectively.

[0045] The instantaneous rate of change and recovery slope data are divided into the inertia support stage, the primary frequency modulation stage and the recovery steady state stage according to the preset time window, and the data of the corresponding stages. Indicates the time period from the occurrence of disturbance to the completion of inertia response, a frequency modulation stage Indicates the time period from the minimum frequency recovery to the steady state, the steady state recovery stage Indicates that the frequency is basically stable and the system tends to a new steady state, where is the time when the disturbance occurs, is the end time of inertia response, is the end time of a frequency modulation. The inertia response value and frequency modulation response value of each stage are used to quantify the instantaneous suppression capability of the wind turbine to the grid frequency disturbance and the frequency recovery capability of the system at each stage. The inertia response value of each stage takes the maximum absolute value of the instantaneous frequency change rate of each stage, which is ,in, for The instantaneous rate of change of the phase frequency, is the corresponding stage time period, , 1 corresponds to the inertia support stage, 2 corresponds to the primary frequency modulation stage, and 3 corresponds to the steady-state recovery stage. The inertia response value reflects the instantaneous power's ability to support the frequency.

[0046] The FM response value is solved by the recovery slope of the frequency curve at each stage, which is:

[0047] wherein, is the phase grid frequency curve.

[0048] The inertia response values and the frequency modulation response values in each phase under multiple disturbances are normalized according to the disturbance amplitude and the duration to construct an inertia response feature matrix, specifically: To eliminate the dimensional differences of different amplitude columns and different duration rows at the same time, bidirectional normalization (once for rows and once for columns) is adopted to pull the response values of different durations under the same amplitude to , facilitating the comparison of the relative strengths in the duration dimension, and pull the response values of different amplitudes under the same duration to , facilitating the comparison of the relative strengths in the amplitude dimension. The two kinds of normalization results are fused through a weight coefficient to obtain two groups of data, namely the normalized inertia response values and the frequency modulation response values , for each phase Two blocks of matrices, the inertia matrix block and the frequency modulation matrix block , are constructed for each phase , and the elements of the inertia matrix block are , and the elements of the frequency modulation matrix block are

[0049] wherein, and are the relative weights of the inertia and frequency modulation two types of indexes. S4, based on the inertia response feature matrix, obtains the contribution value of the virtual inertia control layer to the grid frequency support; In this embodiment, the inertia response feature matrix is phase-inverted to obtain the initial contribution value of each phase, specifically: Each phase includes an inertia support phase, a primary frequency modulation phase and a recovery phase, and the phase inversion calculation is realized through a three-dimensional phase space geometric feature recognition method. For each disturbance working condition and phase, the rotor kinetic energy change rate , the second derivative of the grid frequency and the pitch angle change acceleration The rate of change of rotor kinetic energy (i.e., the change in rotor speed) directly affects the wind turbine's ability to respond to grid frequency disturbances. The rate of change of rotor kinetic energy can provide real-time dynamic feedback for virtual inertia, which determines whether the wind turbine can provide sufficient inertia support when the grid frequency fluctuates. The change in grid frequency (i.e., frequency deviation) is the direct target of wind turbine frequency support. The second-order derivative of frequency, i.e., the acceleration of frequency change, reflects the rate of grid frequency change and the dynamic characteristics of frequency disturbances. The acceleration of pitch angle change can effectively reflect the dynamic ability of wind turbines to adjust power. When a frequency disturbance occurs, the wind turbine needs to quickly adjust its power output. The acceleration of pitch angle change reflects the sensitivity of the wind turbine to grid frequency disturbances.

[0050] The rotor kinetic energy change rate , the second derivative of the grid frequency and pitch angle change acceleration Forming a three-dimensional phase space , each time step Corresponding to a three-dimensional point, a trajectory is formed through the time series, and the trajectory is smoothed to reduce noise. The processed trajectory is .

[0051] For the inertia support stage, identify the inertia support ring characteristics: maximum radial deviation and the area of ​​the circular trajectory , the specific steps are: select the time period of the inertia support phase , extract the three-dimensional phase space trajectory points within the time period .

[0052] Calculate the centroid of the trajectory point:

[0053] in, is the number of time steps.

[0054] Calculate the radial distance from each trajectory point to the trajectory centroid:

[0055] Find the maximum radial distance, that is, the point farthest from the center of mass among all trajectory points:

[0056] The ring area is obtained by calculating the projection area of ​​the trajectory in the inertia support phase using the convex hull algorithm:

[0057] The contribution of the inertial support stage is mainly reflected in the ability of the system to stabilize frequency changes through inertial support, using the maximum radial deviation of the inertial support ring and the annular track area to measure the strength of the inertial support, the initial contribution value of the inertial support phase The calculation formula is:

[0058] Reflects the inhibitory effect of inertial support on frequency change, the larger the value, the greater the influence of inertial support on frequency.

[0059] For the primary frequency modulation stage, identify the characteristics of the frequency modulation spiral: the frequency modulation spiral radius and the pitch, specifically: Select the time period of the primary frequency modulation stage , extract the three-dimensional phase space trajectory points in the time period , fit the trajectory of the trajectory points to a spiral to form a frequency modulation spiral, and calculate the average radius of the frequency modulation spiral in the plane:

[0060] Among them, represents the projection of the trajectory point on the plane.

[0061] Calculate the pitch of the frequency modulation spiral:

[0062] Among them, , is the number of trajectory circles, which can be detected by Fourier analysis or trajectory projection.

[0063] The frequency modulation spiral radius and pitch are important characteristics of the frequency modulation stage, reflecting the system's ability to recover frequency, and the initial contribution value of the primary frequency modulation stage The calculation formula is:

[0064] Among them, and are the maximum frequency modulation spiral radius and pitch in all disturbances.

[0065] For the recovery steady state stage, identify the recovery steady state point, specifically: Select the time period of the recovery steady state stage , identify the recovery steady state point, which represents the average value of the trajectory in the recovery steady state stage, i.e. the average position of the steady state stage, the formula is:

[0066] Calculate the standard deviation of the data points in the recovery steady state stage to the steady state point, which represents the fluctuation of the steady state stage. The formula is:

[0067] The contribution value of the recovery steady state stage mainly depends on the speed and stability of the system recovering to the steady state, which is closely related to the steady state point and the standard deviation The initial contribution value of the recovery steady state stage The calculation formula is:

[0068] Reflects the stability of the recovery steady state stage, the smaller the standard deviation, the more stable the system recovers, and the greater the contribution value.

[0069] The initial contribution value of each stage is constrained and optimized to generate an optimized contribution value, specifically: According to the initial contribution value of each stage and the corresponding power grid frequency response index, the stage performance evaluation vector is calculated:

[0070] Among them, denotes element-wise multiplication, denote the power grid frequency response indexes (such as frequency deviation, recovery time, etc.) corresponding to the inertia support, primary frequency modulation and recovery steady state stage, respectively, which can be obtained through the simulation model.

[0071] According to the stage performance evaluation vector, an optimization objective function is constructed:

[0072] Among them, denote the performance evaluation components of the inertia support, frequency modulation and recovery steady state stage, are the weight coefficients of each stage, indicating the relative importance of each stage to the optimization objective function. The weight coefficients should satisfy: .

[0073] Through the optimization objective function, the contribution values of different stages can be balanced so that the wind turbine can maximize the contribution to the power grid frequency regulation and minimize the power grid frequency fluctuation.

[0074] The upper limit of the wind turbine rotor kinetic energy reserve, the power regulation range of the converter and the allowable fluctuation threshold of the power grid frequency are collected to construct a set of constraint conditions:

[0075] Rotor kinetic energy is the key to the inertia support that the wind turbine can provide. Its upper limit is determined by the design parameters of the wind turbine (such as rotor speed, rotor inertia moment, etc.). The manufacturer of the wind turbine usually provides the rotor inertia moment and the maximum speed With these data, the upper limit of rotor kinetic energy can be calculated, which is:

[0076] The converter regulation range of wind turbines is mainly determined by the design of the turbine and the requirements of the grid. The power regulation range of the converter is usually an important control parameter of the wind turbine, which directly affects the upper and lower limits of active power output. This data can be found in the controller settings of the turbine. It can be read through the remote monitoring system or PLC (Programmable Logic Controller) system of the turbine. It should meet:

[0077] where, P is the current active power output, Pmin is the minimum power that the converter can output (may be negative, indicating that it can absorb grid power), Pmax is the maximum power that the converter can output.

[0078] Similarly, the grid frequency fluctuation threshold can usually be found in the grid operation manual or power dispatching regulations, and should meet:

[0079] where, Δf is the difference between the grid frequency and the rated frequency, Δfmax is the maximum value of the grid frequency fluctuation.

[0080] Using a constraint optimization algorithm to solve the optimal phase contribution value, i.e. the optimized contribution value to maximize the frequency regulation performance of wind turbines and ensure the stability of the grid.

[0081] S5, according to the quantified contribution value, dynamically adjusting the control parameters of the virtual inertia controller in the target generator set; In this embodiment, the control parameters of the virtual inertia controller in the target generator set are dynamically adjusted according to the quantified contribution value, specifically: Performing phase inversion calculation on the inertia response characteristic matrix to obtain the initial contribution value of each phase; Conducting constraint optimization on the initial contribution value of each phase to generate the optimized contribution value; Converting the optimized contribution value into a virtual inertia controller parameter increment and updating it to the virtual inertia control layer for dynamic adjustment.

[0082] wherein the optimized contribution value is converted into a virtual inertia controller parameter increment and updated to the virtual inertia control layer for dynamic adjustment, specifically: The virtual inertia control layer is mainly used to adjust the active power output of the wind turbine to support the grid frequency stability. The controller parameters of the virtual inertia control layer include a virtual inertia coefficient , a virtual inertia control gain , and a power regulation gain . These parameters control the dynamic response speed and amplitude of the virtual inertia, which can affect the recovery process of the grid frequency. The formula is expressed as

[0083] wherein, is the virtual inertia output power, is the grid power error, is the grid frequency deviation.

[0084] By analyzing the contribution values at different stages , it can be deduced how to convert these contribution values into virtual inertia control parameters. In order to maintain the stability of dynamic adjustment, proportional mapping is needed, as follows:

[0085]

[0086] wherein, and are mapping functions used to convert the optimized contribution values into control parameter increments.

[0087] For example, the contribution values can be mapped to the control parameter increments by a linear relationship:

[0088]

[0089] wherein, and are the maximum possible values of the virtual inertia control layer.

[0090] The converted controller parameter increments and are added to the current control parameter values. If the parameter values exceed their maximum range, saturation limitation should be performed to ensure that the parameters do not exceed a reasonable range. These updated virtual inertia control parameters are fed back to the virtual inertia control layer model for real-time control to dynamically adjust the active power output of the wind turbine and help the grid maintain stability.

[0091] The above formulas are dimensionless values calculated. The formula is obtained by software simulation of a large amount of data to obtain a formula of the latest real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0092] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product.

[0093] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are performed by hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0094] In addition, each functional module in each embodiment of the present application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0095] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0096] Finally, the above is merely preferred embodiments of the present application, and is not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A wind power inertia analysis method considering wind turbine inertial support is characterized by: The following steps are involved: Collect dynamic response data of the target generator set under preset disturbance conditions and build a dual-loop dynamic model based on the dynamic response data; The dual-loop dynamic model is coupled with the preset power grid model in a closed loop to construct a unit-grid joint dynamic simulation model for simulating the interaction between the unit and the grid; Based on the joint dynamic simulation model, obtain the multi-dimensional inertia response characteristic matrix; Based on the inertia response characteristic matrix, the contribution value of the virtual inertia control layer to the grid frequency support is obtained; According to the quantitative contribution value, the control parameters of the virtual inertia controller in the target generator set are dynamically adjusted.

2. The wind power inertia analysis method considering wind turbine inertial support according to claim 1 is characterized in that: The multi-dimensional inertia response characteristic matrix is ​​obtained based on the joint dynamic simulation model, specifically: A disturbance event is set in the dynamic simulation model, and dynamic simulation calculations are performed. The inertia response values ​​and frequency modulation response values ​​at different time stages in the simulation data are extracted, and a multi-dimensional inertia response characteristic matrix is ​​constructed based on the response values.

3. The wind power inertia analysis method considering wind turbine inertial support according to claim 1 is characterized in that: The method for constructing the dual-loop dynamic model is specifically as follows: Based on the rotor speed and pitch angle, a nonlinear adaptive control algorithm is used to construct a maximum power point tracking control layer dynamic model as the inner loop; Based on the grid frequency and its rate of change, a feedforward-feedback composite control strategy is used to regulate the active power output of the converter, and a dynamic model of the virtual inertia control layer is constructed as the outer loop. A dual-loop dynamic model is formed by coupling the inner loop and the outer loop.

4. The wind power inertia analysis method considering wind turbine inertial support according to claim 3 is characterized in that: Based on the joint dynamic simulation model, a multi-dimensional inertia response characteristic matrix is ​​obtained through a linear adaptive control algorithm.

5. The wind power inertia analysis method considering wind turbine inertial support according to claim 4 is characterized in that: The feedforward-feedback composite control strategy is specifically as follows: Obtain grid frequency signal and its change rate data; Based on the grid frequency change rate, the feedforward power increment is calculated through the feedforward control channel; Based on the deviation between the grid frequency signal and the rated frequency, the feedback power increment is calculated through the feedback control channel; Add the feedforward power increment and the feedback power increment to obtain the comprehensive power adjustment; Inputting the integrated power adjustment amount into the converter active power control to obtain updated active power; The updated active power is used as the input for the next feedforward and feedback control calculations.

6. The wind power inertia analysis method considering wind turbine inertial support according to claim 5 is characterized in that: The dual-loop dynamic model is coupled with the preset power grid model in a closed loop to construct a unit-grid joint dynamic simulation model for simulating the interaction between the unit and the power grid, specifically: Construct a power grid model with frequency deviation as the state variable; Decompose the active power increment output by the dual-loop dynamic model into the virtual inertia control layer output and the MPPT layer output; Establishing a first coupling channel, inputting the active power increment output by the dual-loop dynamic model into the power balance equation of the power grid model for output coupling; Establish a second coupling channel to feed back the frequency deviation output by the power grid model to the virtual inertia control layer to achieve input coupling; Through input coupling and output coupling, a unit-grid joint dynamic simulation model is constructed.

7. The wind power inertia analysis method considering wind turbine inertial support according to claim 6 is characterized in that: The multi-dimensional inertia response characteristic matrix is ​​obtained based on the joint dynamic simulation model, specifically: Construct a disturbance sequence with different amplitudes and durations to form a disturbance signal; Based on the disturbance signal, the variable step-size integration method is used to calculate the instantaneous rate of change and recovery slope of the power grid frequency curve data output by the simulation model; The instantaneous rate of change and recovery slope data are divided into inertia support phase data, primary frequency modulation phase data, and steady-state recovery phase data according to the preset time window, and the inertia response value and frequency modulation response value of each phase are calculated; The inertia response values ​​and frequency modulation response values ​​at each stage are normalized according to the disturbance amplitude and duration, and the inertia response characteristic matrix is ​​constructed.

8. The wind power inertia analysis method considering wind turbine inertial support according to claim 7 is characterized in that: The control parameters of the virtual inertia controller in the target generator set are dynamically adjusted as follows: Perform staged inversion calculation on the inertia response characteristic matrix to obtain the initial contribution value of each stage; Perform constraint optimization on the initial contribution values ​​of each stage to generate optimized contribution values; The optimized contribution value is converted into a virtual inertia controller parameter increment and updated to the virtual inertia control layer for dynamic adjustment.

9. The wind power inertia analysis method considering wind turbine inertial support according to claim 8, characterized in that: The inertia response characteristic matrix is ​​subjected to a staged inversion calculation to obtain the initial contribution value of each stage, specifically: The stages include an inertia support stage, a primary frequency modulation stage, and a recovery stage; Collect the rotor kinetic energy change rate, the second-order derivative of the grid frequency, and the pitch angle change acceleration to construct a three-dimensional phase space; Inertia support rings, frequency-modulated spirals, and points of stable recovery are identified as geometric features through three-dimensional phase space; The initial contribution value of each stage is calculated based on the geometric characteristics.

10. The wind power inertia analysis method considering wind turbine inertial support according to claim 9, characterized in that: The constraint optimization is performed on the initial contribution value of each stage to generate the optimized contribution value, specifically: According to the initial contribution value of each stage and the corresponding grid frequency response index, the stage performance evaluation vector is calculated; Construct an optimization objective function based on the stage performance evaluation vector and output a comprehensive optimization index; Collect the upper limit of the wind turbine rotor kinetic energy reserve, the converter power adjustment range, and the grid frequency allowable fluctuation threshold, build a set of constraint conditions, and output the allowable value range; Based on the initial staged contribution value, comprehensive optimization index and allowable value range, the constrained optimization algorithm is used to obtain the optimized staged contribution value.

Images