Forward design and device for preventing instability of constructed grid-connected inverter

Abstract

The invention discloses a forward design method for preventing instability of a grid-forming type grid-connected inverter. The method comprises the following steps: establishing an impedance model and each control loop model of the grid-forming type grid-connected inverter; based on the impedance model and the control loop model, defining a multi-target constraint condition, and confirming to-be-designed impedance; drawing an impedance curve Bode diagram of each sample, finding out a main difference frequency band, and sampling an impedance curve in the frequency band to obtain a labeled impedance frequency response data point set; performing data screening processing on the impedance frequency response points with the labels; training the processed label data set to obtain a feasible region and a forbidden region of an impedance plane and a decision boundary of the feasible region and the forbidden region; and performing search optimization on the surrounding neighborhood of the preliminary feasible sample based on the obtained impedance feasible region and forbidden region to form a parameter feasible region. The invention further provides a forward design device for preventing instability of the grid-forming type grid-connected inverter. According to the method provided by the invention, the forward design of instability prevention can be realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of grid-connected inverter design, and particularly relates to a grid-forming grid-connected inverter instability prevention and positive design and device. BACKGROUND

[0002] With the increasing penetration of new energy generation forms such as photovoltaic and wind power in the power grid, the dynamic characteristics of the power system have undergone profound changes. In the traditional power grid, the system inertia and voltage support are provided by synchronous generators, while the traditional grid-following (GFL) inverter mainly connects to the grid as a current source and lacks active support capability, resulting in a decrease in the stability of the power grid when facing disturbances, i.e., the problem of "weak power grid" is increasingly prominent.

[0003] To solve this problem, grid-forming (GFM) inverter technology has emerged. By simulating the operating mechanism of a synchronous generator, for example, by using virtual synchronous machine (VSG) control, the grid-forming inverter can actively establish the amplitude and frequency of its output voltage, providing key support such as virtual inertia and virtual damping for the power grid, significantly enhancing the stability of the power grid.

[0004] However, the VSG control introduces multiple coupled control parameters, such as virtual rotational inertia J, virtual damping coefficient D, active-frequency loop, reactive-voltage loop, voltage loop, and current loop PI controller parameters, which significantly increase the complexity of system design. These parameters jointly determine the steady-state and dynamic performance of the inverter when operating in parallel, such as cascade stability, loop stability, dynamic response speed, grid inertia and damping characteristics, etc.

[0005] Patent document CN116054233A discloses a grid-forming inverter switching control method with phase support capability under fault, the grid-forming inverter normally works in voltage source control mode, when detecting that the current limit under fault, the angular velocity compensation control link based on the grid phase change is put into operation, the phase difference between the inverter output inner electric potential phase and the actual grid phase is reduced, and the phase support capability is provided at the same time. A PI control link is introduced in the current limiting control module to improve the situation that the inner electric potential amplitude changes sharply due to current limiting under fault.

[0006] Patent document CN120150227A discloses a strong series compensation grid-connected stability regulation method and related device of grid-forming energy storage converter, by using harmonic linearization method to establish complete sequence impedance model of three-phase grid-connected inverter controlled by virtual synchronous generator, and analyzing the reason of sub-synchronous oscillation caused by double closed loop control strategy. SUMMARY

[0007] The application aims to provide a grid-forming grid-connected inverter instability prevention positive design method and device, which can optimize the prediction model to realize instability prevention positive design.

[0008] In order to realize the first object of the application, the following technical scheme is provided: a grid-forming grid-connected inverter instability prevention positive design method, comprising the following steps: Based on the grid-forming grid-connected inverter, a corresponding impedance model and a control loop model are constructed, the impedance model including the inverter output impedance and the grid impedance; Sample data is obtained, including the control parameters of the grid-forming grid-connected inverter and the corresponding target impedance; Multi-objective constraint conditions are constructed and brought into the control loop model and the impedance model, and the sample data is screened to divide the feasible sample data and the infeasible sample data that meet the multi-objective constraint conditions; Based on the feasible sample data and the infeasible sample data, a corresponding impedance curve Bode diagram is drawn, and the difference frequency bands in the impedance curve Bode diagram are collected and labeled with sample sources, and the collected difference frequency bands and labels constitute an impedance frequency response data point set; The impedance amplitude and phase response points of the difference frequency bands in the impedance frequency response data point set are normalized, and the normalized impedance amplitude and phase response points are respectively taken as dd the X-axis, dq the X-axis, qd the X-axis and qq the X-axis impedance for screening to construct the independent data set corresponding to each impedance; The independent data set corresponding to each impedance is trained by using a deep neural network algorithm to output the feasible region and the forbidden area of each impedance plane and the decision boundary thereof; Based on the feasible region and the forbidden area of all impedance planes and the decision boundary thereof, the neighborhood of the feasible sample data is searched and optimized to output the parameter combination that meets the multi-objective constraint conditions.

[0009] The application quickly trains and generates the impedance feasible region under a specific frequency band based on a small amount of parameter samples, and then quickly searches the parameter sample space by using the trained impedance feasible region to obtain the feasible region of each parameter.

[0010] Specifically, the control loop model includes the active-frequency loop, the reactive-voltage loop, the voltage control loop and the current control loop.

[0011] Specifically, the impedance frequency response data point set is expressed as follows: ; ;

[0012] Wherein, M the sampled amplitude response point data set represents the sampled amplitude response point data set,P a phase response point dataset of the sampling, i a data label indicating whether the sampling point belongs to the i impedance curve, flag a data label indicating whether the sampling point belongs to the

[0013] Specifically, the multi-objective constraint condition includes one or more of an amplitude and phase margin of a grid-connected inverter-weak grid level system, an active power-frequency loop amplitude and phase margin, a reactive power-voltage loop amplitude and phase margin, a voltage loop amplitude and phase margin, a current loop amplitude and phase margin, a voltage loop bandwidth, a current loop bandwidth, virtual inertia, and virtual damping.

[0014] Specifically, the construction process of the independent dataset is as follows: Determine the algorithm initialization parameters, and normalize the impedance amplitude and phase response points respectively to unify the scales of the coordinates of each dimension; Design the target axis impedance: take each frequency response point on the amplitude plane and the phase plane as a newly added data point, find K the nearest sample point and count the number of feasible labels as K 1, the number of non-feasible labels as K 2, and determine the label attribution of the newly added data point according to the following formula: ; in the formula, the labels 1, 0, and -1 represent feasible, non-feasible, and pending labels, respectively, K err an allowed error; count the number of amplitude response points and the number of frequency response points defined as non-feasible labels in each parameter sample curve, when the number of amplitude response points or the number of frequency response points meets the threshold, remove the points defined as pending labels in the corresponding parameter sample impedance curve, and update the total amplitude response point dataset and the phase dataset, reduce K and increase K err , until K is reduced to meet the iteration termination condition, to obtain the processed independent dataset.

[0015] Specifically, the deep neural network includes a support vector machine for generating a binary classification boundary and a Gaussian process regression joint model for predicting a feasibility probability distribution of an unlabeled region.

[0016] Specifically, the specific process of the neighborhood search optimization is as follows: Take a feasible parameter sample in the feasible sample data as the center, generate extended sample points for parameter feasible region expansion search at a fixed interval in each dimension of the control parameter; The sampling of the impedance frequency response in a specific frequency band is carried out for each extended sample point, and the obtained impedance plane feasible region / forbidden region is used to determine whether the extended sample point meets the multi-objective constraint condition; When the parameter sample point used for searching in a certain dimension direction is a non-feasible point, or there is a feasible or non-feasible parameter sample near the sample point used for searching, the search in the dimension direction is stopped, and the feasible search point extended to the farthest distance is taken as the boundary of the dimension; The farthest feasible sample points used for searching in each dimension are connected to each other to form a closed parameter feasible region; The above process is repeated until the field search of each feasible parameter sample in the feasible sample data is completed.

[0017] In order to realize the second object of the application, the technical scheme is provided as follows: a grid-connected inverter instability prevention and positive design device, comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, wherein the processor executes the computer program to execute the steps of the grid-connected inverter instability prevention and positive design method described above.

[0018] Compared with the prior art, the application has the following beneficial effects: (1) A grid-connected inverter parameter design method considering the cascade stability, the dynamic performance of multiple control loops, the inertia and damping requirements and other multi-objective constraints is proposed; (2) A small number of parameter sample points are used to quickly train an impedance-based visual feasible region and forbidden region, and then a data-driven method is used to guide the subsequent design; (3) The traditional theoretical analysis design is avoided, and the design process is more simple. DETAILED DESCRIPTION

[0019] Figure 1 A grid-connected inverter instability prevention and positive design method provided by the embodiment is shown in the schematic diagram; Figure 2 A control strategy schematic diagram of a virtual synchronous mechanism grid-connected inverter provided by the embodiment is shown in the schematic diagram; Figure 3 An improved K-neighbor algorithm flowchart provided by the embodiment is shown in the flowchart; Figure 4 A support vector machine and Gaussian regression process flowchart provided by the embodiment is shown in the flowchart. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] like Figure 1 The diagram shown is a schematic of a forward design method for preventing instability in a grid-connected inverter provided in this embodiment. The specific process is as follows: Establish the impedance model and control loop models of the grid-connected inverter; More specifically, the impedance model includes the inverter output impedance. Z o and grid impedance Z g ; like Figure 2 As shown, this embodiment uses a virtual synchronous grid-connected inverter control as an example. The control loop model includes an active-frequency loop, a reactive-voltage loop, a voltage control loop, and a current control loop. The active-frequency loop and the reactive-voltage loop generate reference values ​​for voltage and phase angle, and can also simulate the inertia characteristics of a synchronous generator. Then, the voltage, current, and power output by the grid-connected inverter are controlled by the voltage and current dual loops.

[0022] in v abc and i abc These are the three-phase grid-connected voltage and current, respectively. P 0 and Q 0 represents the active power and reactive power of the inverter, respectively. In the active-frequency control loop, P ref This is a reference value for active power. ω 0 represents the theoretical power grid angular frequency. J and D p These are the virtual inertia coefficient and the virtual damping coefficient, respectively. s For the Laplace operator, ω and θ These are the actual grid angular frequency and grid phase angle, respectively; in the reactive power-voltage control loop, Q refis a reactive power reference value, K q is a primary voltage regulation coefficient, E m is a grid rated voltage amplitude, E dref is a virtual synchronous machine inverter output open-circuit electromotive force amplitude; in the voltage and current double loop, v d and v q are grid point voltages in the dq coordinate system respectively, i Labc is a filter inductance current in the abc coordinate system, i Ld and i Lq are filter inductance currents in the dq coordinate system respectively, c d and c q are duty cycles in the dq coordinate system respectively, c abc is a modulation wave in the abc coordinate system, G v (1) and s (2) are voltage and current regulators respectively. G i (3) and s (4) are voltage and current regulators respectively.

[0023] Based on the impedance model and the control loop model, a multi-objective constraint condition is defined, and a to-be-designed impedance is confirmed. A small number of parameter sample points are generated by griding in the control parameter definition domain. Whether each sample meets the multi-objective constraint is determined based on theoretical model analysis, to obtain preliminary feasible / non-feasible parameter samples; More specifically, the to-be-designed impedance includes elements in a 2*2 matrix of the dq domain impedance model Z dd , Z dq , Z qd , Z qq .

[0024] An impedance curve Bode diagram of each sample is drawn, and a main difference frequency band is found. Impedance frequency response data points with labels are obtained by sampling the impedance curve in the frequency band. The obtained frequency response data point set is as follows: ; ; wherein, M and P represent an amplitude response point data set and a phase response point data set respectively, i indicates that the point belongs to the iImpedance curve, flag The feasible / non-feasible label is represented.

[0025] The improved K-NN algorithm is used to filter the impedance frequency response data points in the above impedance frequency response data set, which improves the problem of serious mixing of two types of label data. In this embodiment, the flowchart is shown as Figure 3 K The number of selected adjacent data is determined. Since the progressive data classification method with iterative decreasing K value is used, there is no need to strictly design K The initial value can be larger to ensure the feasibility of the algorithm. K K err The number of allowed errors is determined. The initial K err It should be as small as possible to achieve more stringent classification, but can be increased slightly as the iteration enters the later stage. K The iterative decreasing K value, K min The iterative final value, both of which can be designed according to the accuracy requirement and filtering effect. For example, if the accuracy needs to be improved, a smaller K (1~3) can be selected, otherwise K △ can be larger (4 or higher), if there are still significant data samples that cannot be classified after iteration, the value of K min Should be further reduced to further classify; n_judge The threshold for removing curve data is related to the data point sampling density of each curve. The specific steps are as follows: Determine the algorithm initialization parameters, and normalize the impedance amplitude and phase response points to unify the scale of each dimension coordinate. Design for dd axis, dq axis, qd axis and qq axis impedance, the amplitude plane and phase plane of each frequency response point are regarded as newly placed data points, find K The number of nearest sample points is 1, and the number of feasible labels is K 1, the number of non-feasible labels is K 2, and the label attribution of the newly placed data point is determined according to the following formula: ; In the formula, labels 1, 0 and -1 represent feasible, non-feasible and pending labels, K err ​​represents the allowable error, wherein if the point is defined as a feasible or infeasible label, the point will not be repeated in the subsequent iteration process; if the point is defined as a pending label, the point will continue to be classified in the subsequent iteration process; the number of amplitude response points defined as infeasible labels in each parameter sample curve is counted respectively n_mag j and the number of frequency response points n_ pha j When n_mag j or n_pha j at least one reaches the threshold n_judge , the points defined as pending labels in the parameter sample impedance curve are removed, and the total amplitude and phase data set is updated; decrease K and slightly increase K err Implement iterative classification; repeat the above operations to complete dd the selection of the axis, dq the axis, qd the axis and qq the axis impedance to construct an independent data set corresponding to each impedance.

[0026] Using a deep neural network (DNN) algorithm to train each independent data set to obtain the feasible region and forbidden zone of the impedance plane and its decision boundary; As shown in Figure 4 , the flow chart of the deep neural network (DNN) algorithm provided in this embodiment through the support vector machine (SVM) and Gaussian process regression (GPR) combined algorithm, the implementation steps are as follows: Map the amplitude and phase data of the impedance frequency response points to feature vectors x i , and label binary labels y i ∈{0,1} (0 represents infeasible, 1 represents feasible), and normalize; Using Z-score method for normalization processing, the normalization function is , wherein μ is the mean, σ is the standard deviation; Using a radial basis function kernel (RBF) to construct an SVM model, the kernel function is ; Optimize the hyperparameters γ and the penalty coefficient C by grid search and cross-validation to generate a preliminary decision boundary; Based on the SVM classification results, the sample points x kThe Gaussian process regression model is constructed, and the kernel function is selected as the Matern kernel, and the expression is as follows: ; wherein, K ( x i , x j ) is the covariance between the input points x i and x j , Γ( v ) is the gamma function, K v is the second kind of modified Bessel function of order v ,|| x i - x j ||is the Euclidean distance between x i and x j , l is a positive length scale parameter that controls the speed of decay of the correlation with distance, v is a positive smoothness parameter that controls the smoothness of the function.

[0027] The feasibility probability of the unlabeled region p ( y =1| x ) is calculated, and if p ( y =1| x ) is greater than or equal to 0.5, it is determined as a feasible region; According to the probability distribution output by the GPR, the decision boundary of the SVM is locally smoothed and corrected to generate the final impedance feasible region.

[0028] Based on the obtained impedance feasible region and the forbidden region, the surrounding neighborhood of the preliminary feasible sample is searched and optimized to expand the parameter combination that meets the multi-objective constraint, and finally form the parameter feasible region; More specifically, the neighborhood search step is as follows: A certain preliminary feasible sample point is taken as the center, and sample points are generated at regular intervals around it (in each control parameter dimension) for parameter feasible region expansion search; The impedance frequency response in a specific frequency band is sampled for each sample point, and the obtained impedance plane feasible region / forbidden region is used to determine whether the sample point meets the proposed steady-state and dynamic constraints; When a sample point for searching in a certain direction is a non-feasible point, or there is another preliminary feasible / non-feasible parameter sample near the sample point for searching, stop the expansion search in the direction, and take the feasible search point at the farthest distance as the boundary of the dimension; Connect the farthest feasible sample points for searching in each direction to each other to form a closed parameter feasible region; Repeat the above steps until the field of each preliminary feasible sample point is searched and the final parameter feasible region is formed.

[0029] The embodiment also provides a grid-connected inverter instability prevention and positive design device, which comprises a computer memory, a computer processor and a computer program stored in the computer memory and executable on the computer processor, and the processor executes the computer program to execute the steps of the grid-connected inverter instability prevention and positive design method provided by the above embodiment.

[0030] In addition, the terms "upper", "lower", "inner", "outer", "front", "back" are only used for description purposes, and cannot be understood as indicating or implying relative importance. Unless otherwise specified, the relative steps, numerical expressions and values of the components and steps set forth in the embodiments do not limit the scope of the present application.

[0031] Of course, the above only describes specific embodiments of the present application, and does not limit the scope of the present application. Equivalent changes or modifications made in accordance with the structure, features and principles described in the patent application of the present application shall be included in the patent application of the present application.

[0032] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit the scope of the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can make modifications or easily think of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed by the present application, or make equivalent substitutions for some technical features; and these modifications, changes or substitutions do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be limited by the protection scope of the claims.

[0033] The present application does not rely on expert experience, greatly saves manpower, and adopts the idea of machine learning and digital-analog hybrid driving to help simplify the process of grid-connected inverter system design, provide effective design ideas, and have great engineering application value and popularization prospect.

Claims

1. A forward design method for instability prevention of grid-connected inverters, characterized in that, Includes the following steps: Based on the grid-connected inverter, a corresponding impedance model and control loop model are constructed. The impedance model includes the inverter output impedance and the grid impedance. Acquire sample data, including the control parameters and corresponding target impedance of the grid-connected inverter; Multi-objective constraints are constructed and incorporated into the control loop model and impedance model. The sample data is then filtered to distinguish between feasible and infeasible sample data that satisfy the multi-objective constraints. Based on the feasible sample data and the infeasible sample data, the corresponding impedance curve Bode plot is drawn, and the difference frequency bands in the impedance curve Bode plot are collected and labeled with the sample source. The collected difference frequency bands and labels are combined to form an impedance frequency response data point set. The impedance amplitude and phase response points in the differential frequency bands of the impedance frequency response data set are normalized, and then respectively... dd axis, dq axis, qd shaft and qq The axial impedance is filtered to construct an independent dataset corresponding to each impedance; Deep neural network algorithms are used to train independent datasets corresponding to each impedance to output the feasible region, forbidden region and decision boundary of each impedance plane. Based on the feasible and forbidden regions and their decision boundaries of all impedance planes, a neighborhood search optimization is performed on the surrounding neighborhood of the feasible sample data to output a parameter combination that satisfies the multi-objective constraints.

2. The forward design method for instability prevention of grid-connected inverters according to claim 1, characterized in that, The control loop model includes an active-frequency loop, a reactive-voltage loop, a voltage control loop, and a current control loop.

3. The forward design method for instability prevention of grid-connected inverters according to claim 1, characterized in that, The impedance frequency response data point set is expressed as follows: ; ;in, M This represents a dataset of sampled amplitude response points. P This represents the sampled phase response point dataset. i Indicates that the collection point belongs to the first... i impedance curves, flag This indicates data labels, including feasible and infeasible.

4. The forward design method for instability prevention of grid-connected inverters according to claim 1, characterized in that, The multi-objective constraints include one or more of the following: amplitude and phase margin of the grid-connected inverter-weak grid cascade system; amplitude and phase margin of the active-frequency loop; amplitude and phase margin of the reactive-voltage loop; amplitude and phase margin of the voltage loop; amplitude and phase margin of the current loop; voltage loop bandwidth; current loop bandwidth; virtual inertia; and virtual damping.

5. The forward design method for instability prevention of grid-connected inverters according to claim 1, characterized in that, The process of constructing the independent dataset is as follows: Determine the algorithm initialization parameters and normalize the impedance amplitude and phase response points respectively to unify the scale of the coordinates in each dimension; Design for the target's axial impedance: Treat each frequency response point in the amplitude and phase planes as a newly added data point, and find... K Find the nearest sample points and count the number of feasible labels for each of them. K 1. The number of non-feasible tags is K 2. Determine the label assignment for newly added data points using the following formula: In the formula, labels 1, 0, and -1 represent feasible, infeasible, and pending labels, respectively. K err Indicates the allowable error; The number of amplitude response points and frequency response points defined as non-feasible labels in each parameter sample curve is counted. When the number of amplitude response points or frequency response points meets a threshold, the points defined as undetermined labels in the corresponding parameter sample impedance curve are removed, and the total amplitude response point dataset and phase dataset are updated to reduce the number of undetermined labels. K and increase K err until K Reduce the size until the iteration termination condition is met to obtain the processed independent dataset.

6. The forward design method for instability prevention of grid-connected inverters according to claim 1, characterized in that, The deep neural network includes a support vector machine for generating binary classification boundaries and a Gaussian process regression joint model for predicting the feasibility probability distribution of unlabeled regions.

7. The forward design method for instability prevention of grid-connected inverters according to claim 1, characterized in that, The specific process of neighborhood search optimization is as follows: Taking a feasible parameter sample in the feasible sample data as the center, expand sample points are generated at fixed intervals in each dimension of the control parameter for parameter feasible domain expansion search. For each extended sample point, the impedance frequency response within a specific frequency band is sampled, and the obtained feasible / no-go region of the impedance plane is used to determine whether the extended sample point satisfies the multi-objective constraint conditions. When the parameter sample point used for searching in a certain dimension is an infeasible point, or when there are feasible or infeasible parameter samples near the sample point used for searching, the expansion search in that dimension is stopped, and the feasible search point that has been expanded to the farthest distance is taken as the boundary of that dimension. Connect the furthest feasible sample points used for the search in each dimension to form a closed parameter feasible region; Repeat the above process until the domain search for each feasible parameter sample in the feasible sample data is completed.

8. A forward design device for instability prevention of a grid-connected inverter, comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, characterized in that, When the processor executes a computer program, it performs the steps of the forward design method for preventing instability in grid-connected inverters as described in any one of claims 1 to 7.

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