A stability control method and apparatus for power quality management systems

Abstract

This invention discloses a stability control method and apparatus for a power quality management system. Its features include: extracting characteristic parameters from collected three-phase power grid data to determine system state characteristic data; estimating system compensation parameters for the power quality compensation system based on the system state characteristic data and the three-phase power grid data to determine estimated system compensation parameters; determining optimal control parameters based on the system state characteristic data and the estimated system compensation parameters; performing system control prediction on the power quality compensation system based on the estimated system compensation parameters to determine the real-time control quantity of the power quality compensation system; performing control operations on the power quality compensation system based on the real-time control quantity to determine the control operation result; optimizing the optimal control parameters based on the control operation result to obtain target control parameters; and determining the target compensation current command based on the target control parameters. This achieves efficient control and satisfies economic, energy efficiency, and reliability requirements.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a stability control method and apparatus for power quality management systems. Background Technology

[0002] With the continuous development of society, economy, and power electronic devices, more and more non-resistive loads have appeared in the distribution network, posing a huge challenge to the safe and reliable operation of the power system and the power quality provided by the grid. Therefore, reactive power compensation has become increasingly important in the power system. At the same time, in order to better solve power quality problems such as low power factor, large harmonics, and large negative sequence components in the power supply system, power quality compensation systems must be adopted to effectively manage the power quality problems of the traction power supply network. Static Var Generators (SVG) are reactive power compensation devices with strong dynamic compensation performance; their AC side output current can quickly track the command current. Fixed Capacitors (FC) are passive filters that can effectively suppress integer harmonics in the system while compensating for fundamental reactive power. Using FC and SVG together for compensation can reduce the SVG capacity and improve system economy. However, when the two are used together, conflicts can arise in their control, leading to instability, requiring optimization of their control methods. Summary of the Invention

[0003] This invention provides a stability control method and apparatus for power quality management systems to solve the technical problem of system instability caused by joint compensation from static var generators and fixed capacitors.

[0004] According to one aspect of the present invention, a stability control method for a power quality management system is provided, comprising:

[0005] Collect three-phase power grid data for the power quality compensation system; extract characteristic parameters from the three-phase power grid data to determine system state characteristic data; wherein, the three-phase power grid data includes instantaneous three-phase voltage and instantaneous three-phase current.

[0006] Based on the system state characteristic data and the three-phase power grid data, the system compensation parameters of the power quality compensation system are estimated, and the estimated system compensation parameter data are determined.

[0007] The optimal control parameters of the power quality compensation system are determined based on the system state characteristic data and the system compensation parameter estimation data.

[0008] Based on the estimated data of the system compensation parameters, system control prediction is performed on the power quality compensation system to determine the real-time control quantity of the power quality compensation system.

[0009] The power quality compensation system is controlled according to the real-time control quantity, and the control operation result is determined.

[0010] Based on the control operation results, the optimal control parameters are optimized to obtain target control parameters, and the target compensation current command is determined according to the target control parameters.

[0011] In this embodiment of the invention, optimizing the optimal control parameters based on the control operation result to obtain target control parameters, and determining the target compensation current command based on the target control parameters, includes:

[0012] Based on the system state characteristic data and the control operation results, the comprehensive performance index of the power quality compensation system is calculated to determine the system performance evaluation data corresponding to the power quality compensation system.

[0013] A performance prediction model is constructed based on the system performance evaluation data, and a performance prediction function is obtained.

[0014] The optimal control parameters are optimized by applying a performance prediction function using a Bayesian optimization algorithm to obtain target control parameters, and the target compensation current command is determined based on the target control parameters.

[0015] In this embodiment of the invention, the step of optimizing the optimal control parameters using a performance prediction function through a Bayesian optimization algorithm to obtain target control parameters, and determining the target compensation current command based on the target control parameters, includes:

[0016] Define the upper and lower bounds of the control parameter search space, set a reasonable range of variation for each parameter in the control parameter search space, and normalize each parameter to obtain the standard control parameter search space;

[0017] A performance dataset is constructed based on the search space of the standard control parameters and the performance prediction function;

[0018] The first agent model is initialized based on the performance dataset to obtain the second agent model;

[0019] The target parameter point is determined by searching for the maximum value in the standard control parameter search space using a preset expected improvement function;

[0020] The target parameter point is predicted according to the performance prediction function to determine the target performance data, the target performance data is updated to the performance dataset, and the updated performance dataset is determined.

[0021] The second agent model is optimized and updated based on the updated performance dataset to determine the target agent model;

[0022] If the target proxy model satisfies the preset convergence condition, the target control parameters are obtained based on the performance dataset, and the target compensation current command is determined based on the target control parameters.

[0023] In this embodiment of the invention, the process of collecting three-phase power grid data from the power quality compensation system and extracting feature parameters based on the three-phase power grid data to determine system state feature data includes:

[0024] The three-phase power grid data of the power quality compensation system are collected to obtain the raw data.

[0025] The original data is subjected to wavelet threshold denoising processing to obtain the three-phase power grid data;

[0026] The three-phase data of the power grid is converted to a two-phase orthogonal coordinate system to obtain the converted three-phase data;

[0027] The converted three-phase data are used to perform parameter estimation to obtain signal parameter estimation results;

[0028] Based on the signal parameter estimation results, an analytical expression for the signal is constructed, thus obtaining the analytical expression for the signal;

[0029] The system power data is determined by calculating based on the signal analytical expression.

[0030] Time-frequency analysis is performed on the system power data to obtain power fluctuation characteristic data. Key feature parameters are extracted from the power fluctuation characteristic data to obtain system state characteristic data.

[0031] In this embodiment of the invention, the step of estimating system compensation parameters for the power quality compensation system based on the system state characteristic data and the three-phase data of the power grid, and determining the estimated system compensation parameters, includes:

[0032] A nonlinear state-space model of the power quality compensation system is constructed based on system state characteristic data, and the system dynamic equations are obtained.

[0033] The unscented Kalman filter algorithm is used to perform state estimation and parameter identification based on the system dynamic equation and the real-time measurement data of the power quality compensation system, thereby obtaining the estimated values ​​of the reactive power generator parameters.

[0034] The estimated values ​​of the fixed capacitor parameters are determined by estimating the converted three-phase data using an adaptive forgetting factor and the least squares method.

[0035] The estimated data of the system compensation parameters are determined based on the estimated values ​​of the reactive power generator parameters and the estimated values ​​of the fixed capacitor parameters.

[0036] In this embodiment of the invention, determining the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data includes:

[0037] Construct a multi-objective optimization function and the first constraint condition using system state characteristic data and system parameter estimation data;

[0038] The non-dominated solution is determined by solving the multi-objective optimization function and the constraints using a multi-objective differential evolution algorithm.

[0039] The optimal compensation strategy is selected by applying the fuzzy decision method to the non-dominated solution set, and the optimal control parameters are obtained.

[0040] In this embodiment of the invention, the step of performing system control prediction on the power quality compensation system based on the estimated system compensation parameters, and determining the real-time control quantities of the power quality compensation system, includes:

[0041] Based on the system parameter estimation data, a discrete-time prediction model is constructed, and the system state prediction equation is determined.

[0042] Based on the system state prediction equation, a rolling time-domain optimization problem is constructed to obtain the objective optimization function and the second constraint condition.

[0043] The optimal control sequence is determined by solving the objective optimization function and the second constraint condition using a sequential quadratic programming algorithm.

[0044] The optimal control sequence is subjected to feedforward control and feedback correction to determine the real-time control quantity.

[0045] According to another aspect of the present invention, a stability control device for a power quality management system is provided, comprising:

[0046] The system data acquisition module is used to collect three-phase power grid data for the power quality compensation system; extract characteristic parameters based on the three-phase power grid data to determine system state characteristic data; wherein, the three-phase power grid data includes instantaneous three-phase voltage and instantaneous three-phase current.

[0047] The system parameter estimation module is used to estimate the system compensation parameters of the power quality compensation system based on the system state characteristic data and the three-phase data of the power grid, and to determine the system compensation parameter estimation data.

[0048] The optimal operating parameter calculation module is used to determine the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data.

[0049] The system control calculation module is used to perform system control prediction on the power quality compensation system based on the estimated data of the system compensation parameters, and to determine the real-time control quantity of the power quality compensation system.

[0050] The system control module is used to perform control operations on the power quality compensation system according to the real-time control quantity and determine the control operation result;

[0051] The compensation current calculation module is used to optimize the optimal control parameters based on the control operation results to obtain target control parameters, and determine the target compensation current command based on the target control parameters.

[0052] In this embodiment of the invention, the compensation current calculation module includes:

[0053] The system performance evaluation unit is used to calculate the comprehensive performance index of the power quality compensation system based on the system state characteristic data and the control operation results, and to determine the system performance evaluation data corresponding to the power quality compensation system.

[0054] The performance prediction function construction unit is used to construct a performance prediction model based on the system performance evaluation data and obtain a performance prediction function.

[0055] The instruction calculation unit is used to optimize the optimal control parameters by applying a performance prediction function through a Bayesian optimization algorithm to obtain target control parameters, and to determine the target compensation current instruction based on the target control parameters.

[0056] In this embodiment of the invention, the instruction calculation unit includes:

[0057] The parameter calculation subunit is used to define the upper and lower bounds of the control parameter search space, set the reasonable variation range of each parameter in the control parameter search space, and normalize each parameter to obtain the standard control parameter search space.

[0058] The dataset computation subunit is used to construct a performance dataset based on the search space of the standard control parameters and the performance prediction function.

[0059] The proxy model update subunit is used to initialize a pre-set first proxy model based on the performance dataset to obtain a second proxy model;

[0060] The function computation subunit is used to find the maximum value in the standard control parameter search space through a preset expected improvement function to determine the target parameter point;

[0061] The performance prediction subunit is used to predict the performance of the target parameter point according to the performance prediction function, determine the target performance data, update the target performance data to the performance dataset, and determine the updated performance dataset.

[0062] The proxy model optimization subunit is used to optimize and update the second proxy model based on the updated performance dataset to determine the target proxy model.

[0063] The compensation current command calculation subunit is used to obtain the target control parameters based on the performance dataset and determine the target compensation current command based on the target control parameters, provided that the target proxy model meets the preset convergence conditions.

[0064] In this embodiment of the invention, the system data acquisition module includes:

[0065] The data acquisition unit is used to acquire the three-phase power grid data of the power quality compensation system to obtain the raw data.

[0066] A denoising unit is used to perform wavelet threshold denoising processing on the original acquired data to obtain three-phase power grid data.

[0067] The three-phase power grid calculation unit is used to convert the three-phase power grid data to a two-phase orthogonal coordinate system to obtain the converted three-phase data.

[0068] A signal parameter estimation unit is used to perform parameter estimation on the converted three-phase data to obtain signal parameter estimation results;

[0069] The signal analysis unit is used to construct an analytical expression for the signal based on the signal parameter estimation results, thereby obtaining the analytical expression for the signal.

[0070] The system power calculation unit is used to calculate and determine the system power data based on the signal analytical expression;

[0071] The feature data extraction unit is used to perform time-frequency analysis on the system power data to obtain power fluctuation characteristic data, and to extract key feature parameters from the power fluctuation characteristic data to obtain system state feature data.

[0072] In this embodiment of the invention, the system parameter estimation module includes:

[0073] The system equation construction unit is used to construct a nonlinear state-space model of the power quality compensation system based on system state characteristic data, and obtain the system dynamic equations.

[0074] The unscented Kalman filter calculation unit is used to perform state estimation and parameter identification based on the system dynamic equation and the real-time measurement data of the power quality compensation system through the unscented Kalman filter algorithm, and obtain the estimated value of the reactive power generator parameters.

[0075] The least squares recursive unit is used to estimate the parameters of the fixed capacitor by using an adaptive forgetting factor and the least squares method based on the converted three-phase data.

[0076] The data calculation unit is used to determine the estimated data of the system compensation parameters based on the estimated values ​​of the reactive power generator parameters and the estimated values ​​of the fixed capacitor parameters.

[0077] In this embodiment of the invention, the optimal operating parameter calculation module includes:

[0078] The optimization function construction unit is used to construct a multi-objective optimization function and the first constraint condition from the system state characteristic data and system parameter estimation data;

[0079] The solving unit is used to solve the multi-objective optimization function and the constraints using a multi-objective differential evolution algorithm to determine the non-dominated solution;

[0080] The control parameter calculation unit is used to apply a fuzzy decision method to the non-dominated solution set to select the best compensation strategy and obtain the optimal control parameters.

[0081] In this embodiment of the invention, the system control module includes:

[0082] The system state prediction unit is used to construct a discrete-time prediction model based on the system parameter estimation data and determine the system state prediction equation.

[0083] The system optimization unit is used to construct a rolling time-domain optimization problem based on the system state prediction equation, and obtain the objective optimization function and the second constraint condition.

[0084] The optimal control sequence solving unit is used to solve the objective optimization function and the second constraint condition through a sequence quadratic programming algorithm to determine the optimal control sequence.

[0085] A real-time control unit is used to perform feedforward control and feedback correction on the optimal control sequence and to determine the real-time control quantity.

[0086] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0087] At least one processor; and

[0088] A memory communicatively connected to the at least one processor; wherein,

[0089] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the stability control method for a power quality management system according to any embodiment of the present invention.

[0090] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the stability control method for a power quality management system according to any embodiment of the present invention.

[0091] The technical solution of this invention collects three-phase power grid data for a power quality compensation system; extracts feature parameters from the three-phase power grid data to determine system state feature data, which can serve as the basis for optimized power system scheduling and improve the economic efficiency of the power system; estimates system compensation parameters for the power quality compensation system based on the system state feature data and the three-phase power grid data to determine estimated system compensation parameters; and effectively controls the reactive power of the power system based on the estimated system compensation parameters, thereby improving the stability of the power system; finally, it determines the optimal control parameters for the power quality compensation system based on the system state feature data and the estimated system compensation parameters, which further improves the stability and operational efficiency of the power system. This method improves the efficiency and effectively controls the cost of the power system, enhancing its economic benefits. Based on the estimated system compensation parameters, it predicts system control for the power quality compensation system, determines the real-time control quantity, performs control operations on the system based on the real-time control quantity, determines the control operation result, and achieves rapid control response of the power system, improving power quality. Based on the control operation result, it optimizes the optimal control parameters to obtain target control parameters, determines the target compensation current command based on the target control parameters, and through precise control of the target compensation current, effectively improves the response efficiency of the power system, enhances power quality, and strengthens stability and economic benefits. It solves the technical problem of system instability caused by the joint compensation of static var generators and fixed capacitors, achieving efficient control and meeting the requirements of economy, energy efficiency, and reliability.

[0092] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0093] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0094] Figure 1 A flowchart of a stability control method for a power quality management system is provided as an embodiment of the present invention;

[0095] Figure 2 A flowchart of another stability control method for a power quality management system provided in an embodiment of the present invention;

[0096] Figure 3 A schematic diagram of the process control of a power quality management system provided in an embodiment of the present invention;

[0097] Figure 4 This is a schematic diagram of the structure of a stability control device for a power quality management system provided in an embodiment of the present invention;

[0098] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0099] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0100] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0101] Figure 1This invention provides a flowchart of a stability control method for a power quality management system. This embodiment is applicable to situations requiring precise control of both static var generators and fixed capacitors in a power quality compensation system. The method can be executed by a stability control device for the power quality management system, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0102] S110. Collect three-phase power grid data of the power quality compensation system; extract characteristic parameters based on the three-phase power grid data to determine system state characteristic data.

[0103] The power quality compensation system is a compensation system composed of a static var generator and fixed capacitors. The power quality compensation system can effectively improve the power quality of the traction power supply network.

[0104] Among them, the three-phase data of the power grid can be the instantaneous three-phase voltage and instantaneous three-phase current of the power grid corresponding to the power quality compensation system.

[0105] The system state characteristic data can be the key characteristic parameters of the power quality compensation system. System state characteristic data may include active power, reactive power, total harmonic distortion rate, and power fluctuation index.

[0106] Optionally, when collecting three-phase power grid data for the power quality compensation system, high-speed synchronous sampling technology can be used to obtain the three-phase power grid data. High-speed synchronous sampling technology can significantly improve the acquisition accuracy.

[0107] Specifically, for the power quality compensation system, the three-phase data of the power grid are collected, and the characteristic parameters of the three-phase data are extracted to determine the system state characteristic data of the power quality compensation system.

[0108] Optionally, in another optional embodiment of the present invention, the step of collecting three-phase power grid data of the power quality compensation system and extracting feature parameters based on the three-phase power grid data to determine system state feature data includes:

[0109] The three-phase power grid data of the power quality compensation system are collected to obtain the raw data.

[0110] The original data is subjected to wavelet threshold denoising processing to obtain the three-phase power grid data;

[0111] The three-phase data of the power grid is converted to a two-phase orthogonal coordinate system to obtain the converted three-phase data;

[0112] The converted three-phase data are used to perform parameter estimation to obtain signal parameter estimation results;

[0113] Based on the signal parameter estimation results, an analytical expression for the signal is constructed, thus obtaining the analytical expression for the signal;

[0114] The system power data is determined by calculating based on the signal analytical expression.

[0115] Time-frequency analysis is performed on the system power data to obtain power fluctuation characteristic data. Key feature parameters are extracted from the power fluctuation characteristic data to obtain system state characteristic data.

[0116] The original sampling data can be the instantaneous three-phase voltage and instantaneous three-phase current of the power grid directly acquired using high-speed synchronous sampling technology; alternatively, the instantaneous three-phase voltage and current data of the power grid of the power quality compensation system can be obtained by acquiring the sampling frequency of 20kHz using high-speed synchronous sampling technology to obtain the original sampling data.

[0117] Optionally, after obtaining the original sampled data, wavelet decomposition is performed on the original sampled data using a wavelet function to obtain wavelet coefficients. The noise standard deviation estimate for each layer of wavelet coefficients is calculated to obtain noise standard deviation data. Based on the noise standard deviation data and the signal length, an adaptive threshold for each layer is calculated to obtain threshold data. The wavelet coefficients are then subjected to soft thresholding using the threshold data to obtain denoised wavelet coefficients. Wavelet reconstruction is then performed on the denoised wavelet coefficients to obtain the three-phase power grid data. For example, the wavelet function can be a fourth-order Daubechies wavelet.

[0118] Among them, the converted three-phase data can be the converted three-phase data; the signal parameter estimation result can be the result data obtained by estimating the frequency, amplitude and phase of the signal based on the three-phase data of the power grid. Optionally, the instantaneous three-phase currents of the three-phase power grid data are substituted into the Clarke transformation matrix to perform matrix transformation on the three-phase power grid data, obtaining instantaneous three-phase current data in a two-phase stationary coordinate system (α-β coordinate system), i.e., transformed three-phase data. The Prony algorithm is then applied to the transformed three-phase data. The order of the Prony algorithm is initialized; based on the initialized order, a signal model matrix is ​​constructed to obtain model coefficients; the residual variance is calculated based on the model coefficients to obtain residual data; using the residual data and the number of data points, the Akaike information criterion value is calculated to obtain the information criterion value data; the order of the Prony algorithm is adjusted, and the above process is repeated until the order that minimizes the information criterion value is found, obtaining the optimal order; the Prony algorithm is reapplied using the optimal order to estimate the frequency, amplitude, and phase of the instantaneous three-phase current data, obtaining the signal parameter estimation results.

[0119] Optionally, embodiments of the present invention can also construct an improved Clarke transform matrix, perform matrix transformation on the three-phase power grid data based on the improved Clarke transform matrix to obtain the transformed voltage data and transformed current data in the two-phase stationary coordinate system (α-β coordinate system), construct Hankel matrices for the transformed voltage data and transformed current data respectively, perform singular value decomposition on each Hankel matrix to obtain the left singular matrix, singular value matrix and right singular matrix, determine the dimension of the signal subspace based on the singular value matrix using the minimum description length (MDL) criterion to obtain the signal subspace dimension data; based on the signal subspace dimension, construct the signal subspace matrix based on the left singular matrix, calculate the pseudo-inverse matrix of the signal subspace matrix, construct the displacement-invariant subspace, namely the first displacement-invariant subspace and the second displacement-invariant subspace, where the first displacement-invariant subspace is the signal subspace matrix with the last row removed, and the second displacement-invariant subspace is the signal subspace matrix with the first row removed, calculate the generalized eigenvalues ​​of the first displacement-invariant subspace and the second displacement-invariant subspace to obtain the frequency estimate of the complex exponential signal; according to the complex exponential signal... The frequency estimate is used to construct a Vandermonde matrix. The Vandermonde matrix is ​​solved using the least squares method to obtain a complex amplitude vector. The amplitude and phase are extracted from the complex amplitude vector. The frequency, amplitude, and phase are combined to obtain the signal parameter data for each signal component. The signal parameter data is sorted, with the frequency arranged from low to high. The energy contribution of each signal component is calculated to obtain the energy contribution data. Based on the energy contribution data, the top L components with the largest energy contributions are selected, where L is determined by the cumulative energy contribution reaching 99% of the total energy. The frequency, amplitude, and phase data of the selected L components are combined to obtain the final signal parameter estimation result.

[0120] The analytical expression of the signal can be an analytical expression for the instantaneous three-phase current and instantaneous three-phase voltage constructed in a two-phase stationary coordinate system. Optionally, the analytical expressions for the instantaneous three-phase voltage and instantaneous three-phase current are constructed based on the signal parameter estimation results in a two-phase stationary coordinate system. Optionally, the analytical expression of the signal includes a first voltage expression, a second voltage expression, a first current expression, and a second current expression. For example, the two-phase stationary coordinate system is represented as (α-β coordinate system), where the first voltage expression is represented as u_α(t), the second voltage expression is represented as u_β(t), the first current expression is represented as i_α(t), and the second current expression is represented as i_β(t).

[0121] The system power data can be the instantaneous reactive power and instantaneous active power of the power quality compensation system. Optionally, after obtaining the signal analytical expression, calculations are performed based on the first voltage expression and the first current expression in the signal analytical expression to obtain a first intermediate result. Calculations are then performed based on the second voltage expression and the second current expression in the signal analytical expression to obtain a second intermediate result. The first and second intermediate results are added together to obtain the instantaneous active power. Calculations are then performed based on the second voltage expression and the first current expression to obtain a third intermediate result. Calculations are then performed based on the first voltage expression and the second current expression to obtain a fourth intermediate result. The third and fourth intermediate results are subtracted to obtain the instantaneous reactive power. The system power data is determined based on the instantaneous reactive power and the instantaneous active power.

[0122] The power fluctuation characteristic data can be the power fluctuation situation of the power quality compensation system. Optionally, empirical mode decomposition is performed on the system power data to obtain a series of intrinsic mode functions (IMFs). Hilbert transform is applied to each IMF to calculate the instantaneous frequency and amplitude, thus obtaining Hilbert spectrum data. Based on the Hilbert spectrum data, the power fluctuation characteristics are analyzed to obtain power fluctuation characteristic data.

[0123] Optionally, in the above embodiments, empirical mode decomposition of the system power data is performed as follows: All local extrema are identified; an upper and lower envelope are generated using cubic spline interpolation; the mean values ​​of the upper and lower envelopes are calculated to obtain the mean curve; the mean curve is subtracted from the original signal to obtain candidate intrinsic mode functions (EMFs); the decomposition process is repeated, and the candidate EMF that satisfies the condition that the number of extrema is equal to or differs from the number of zero-crossings by no more than 1, and that the mean of the upper and lower envelopes is close to zero at any point, is taken as the first EMF; the above process is repeated to obtain all EMFs and the residual signal, resulting in a complete decomposition of the signal; Hilbert transform is applied to each EMF to calculate the instantaneous frequency and amplitude, obtaining Hilbert spectrum data; based on the Hilbert spectrum data, power fluctuation characteristics are analyzed to obtain power fluctuation characteristic data.

[0124] Optionally, data extraction is performed based on power fluctuation characteristic data to extract fundamental active power, reactive power, and total harmonic distortion rate to obtain power quality characteristic data. Power fluctuation index is calculated to obtain system dynamic characteristic data. The power quality characteristic data and system dynamic characteristic data are combined to obtain the final system state characteristic data.

[0125] S120. Based on the system state characteristic data and the three-phase data of the power grid, the system compensation parameters of the power quality compensation system are estimated, and the estimated system compensation parameters are determined.

[0126] Among them, the system compensation parameter estimation data can be the estimated values ​​of the system compensation parameters of the power quality compensation system. The system compensation parameters consist of the estimated values ​​of the reactive power generator parameters and the estimated values ​​of the fixed capacitor parameters.

[0127] Specifically, based on the system state characteristic data and the three-phase data of the power grid, the system compensation parameters of the power quality compensation system are estimated, and the estimated system compensation parameters are determined.

[0128] Optionally, in another optional embodiment of the present invention, the step of estimating system compensation parameters for the power quality compensation system based on the system state characteristic data and the three-phase data of the power grid, and determining the estimated system compensation parameters, includes:

[0129] A nonlinear state-space model of the power quality compensation system is constructed based on system state characteristic data to obtain the system dynamic equations. The unscented Kalman filter algorithm is used to perform state estimation and parameter identification based on the system dynamic equations and real-time measurement data of the power quality compensation system, yielding estimated values ​​for the reactive power generator parameters. An adaptive forgetting factor and the least squares method are used to estimate the fixed capacitor parameters based on the converted three-phase data. Finally, the estimated system compensation parameters are determined based on the estimated values ​​for the reactive power generator parameters and the fixed capacitor parameters.

[0130] The system dynamic equations can be the equations corresponding to a nonlinear state-space model. Optionally,

[0131] Based on the system state characteristic data, a first state vector, a control input vector, and a system parameter vector are constructed. The system dynamic equations are then established based on these vectors. The first state vector includes: current and voltage in a synchronous rotating coordinate system, and the DC-side voltage of the reactive power generator. The control input vector includes the modulation ratio of the reactive power generator. The system parameter vector includes the system equivalent inductance, system equivalent resistance, DC-side capacitance of the reactive power generator, the inductance of the fixed capacitor, and the capacitance of the fixed capacitor.

[0132] The reactive power transmitter parameter estimates can be the system compensation estimates of the reactive power transmitter for the power quality compensation system. Optionally, a measurement equation is established based on the first state vector and the control input vector. The measurement equation and the system dynamic equation are combined to obtain a nonlinear state-space model. The first state vector and the system parameter vector are merged to construct an extended state vector. Based on the extended state vector X and the system dynamic equation, a Sigma point set is generated to obtain a priori state estimate. The predicted measurement value is calculated using the measurement equation and the priori state estimate to obtain the measurement prediction data. The measurement error is obtained based on the actual measurement value and the measurement prediction data. The measurement error covariance matrix and the state-measurement cross-covariance matrix are calculated. The Kalman gain is calculated based on the measurement error covariance matrix and the state-measurement cross-covariance matrix. The state estimate is updated based on the Kalman gain, the measurement error, and the priori state estimate to obtain the posterior state estimate. The reactive power transmitter parameter estimates are extracted from the posterior state estimate to obtain the updated reactive power transmitter parameter estimates.

[0133] The estimated parameters of the fixed capacitor can be the estimated values ​​of the fixed capacitor for system compensation in the power quality compensation system. Optionally, the input-output relationship of the reactive power transmitter is constructed based on the voltage and current data in the two-phase stationary coordinate system to obtain the regression vector; the estimated parameters and covariance matrix of the reactive power transmitter are initialized, and the predicted output is calculated based on the estimated parameters and regression vector to obtain the output prediction data; the prediction error is calculated based on the actual output and the output prediction data to obtain the error data; the adaptive adjustment forgetting factor is adjusted based on the error data and the state of the reactive power transmitter, and the gain matrix is ​​calculated based on the adaptive adjustment forgetting factor, regression vector, and covariance matrix; the estimated parameters and covariance matrix are updated based on the gain matrix and prediction error to obtain the updated estimated parameters and covariance matrix, and the steps of updating the updated estimated parameters and covariance matrix are repeated until the estimated parameters converge to obtain the final estimated parameters of the fixed capacitor.

[0134] Optionally, the estimated values ​​of the reactive power generator parameters and the estimated values ​​of the fixed capacitor parameters can be combined to obtain complete system compensation parameter estimation data.

[0135] S130. Determine the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data.

[0136] Among them, the optimal control parameters of the power quality compensation system can be the optimal control variables for realizing the control of the power quality compensation system.

[0137] Specifically, the power quality compensation system is optimized through multi-objective optimization using system state characteristic data and system compensation parameter estimation data to obtain the optimal control parameters of the power quality compensation system.

[0138] Optionally, in another optional embodiment of the present invention, determining the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data includes: constructing a multi-objective optimization function and a first constraint condition from the system state characteristic data and the system parameter estimation data; solving the multi-objective optimization function and the constraint condition using a multi-objective differential evolution algorithm to determine a non-dominated solution set; and applying a fuzzy decision method to the non-dominated solution set to select the best compensation strategy to obtain the optimal control parameters.

[0139] The multi-objective optimization function can be a function that optimizes each objective of the power system compensation system; the first constraint condition can be used to make the multi-objective optimization function fit the power system compensation system better.

[0140] Optionally, a system stability index function is constructed based on system state characteristic data, a compensation efficiency index function is constructed based on the total harmonic distortion rate of the system state characteristic data, an economic index function is constructed based on the static var generator capacity data of the power system compensation system, and the system stability index function, compensation efficiency index function, and economic index function are combined to obtain a multi-objective optimization function. The first constraint condition is constructed on the multi-objective optimization function based on the system parameter estimation data.

[0141] Optionally, the process of solving the multi-objective optimization function and the constraints using a multi-objective differential evolution algorithm is as follows: Initialize the population to an initial population; randomly generate multiple individuals (i.e., decision variable vectors) in the decision variable space; evaluate the multi-objective optimization function for each individual to obtain the objective function value of the initial population; for each individual in the population, randomly select three different individuals from the initial population and generate a mutation vector based on a scaling factor; determine whether the mutation vector satisfies the boundary constraints; if the mutation vector does not satisfy the boundary constraints, perform boundary processing by cross-operating the mutation vectors of individuals to obtain an experimental vector; evaluate the function value of the experimental vector in the multi-objective optimization function to obtain the function evaluation result; and apply the Pareto dominance relation to the individuals and experimental vectors based on the function evaluation result. Domination is determined to establish the updated initial population and external archive. A fast non-dominated sort is performed on the individuals in both the updated initial population and the external archive to obtain at least one non-dominated front. Individuals at each non-dominated front are sorted according to the function evaluation results to obtain the non-dominated sorting result. The crowding degree of the boundary solutions is set to infinity. For other solutions, a crowding degree index is calculated to obtain the crowding degree of each individual. The next generation of individuals is selected based on the non-dominated sorting result and the individual crowding degree to obtain a new population. The non-dominated solutions of the new population are added to the external archive. The solutions in the external archive are then non-dominated sorted, retaining only the solutions at the first non-dominated front. If the size of the external archive exceeds the preset maximum capacity, redundant solutions are deleted based on the individual crowding degree until the size of the external archive equals the preset maximum capacity. A pre-set termination condition is checked. If the pre-set termination condition is not met, the mutation operation is returned to update the external archive until the termination condition is met. The solution set of the external archive is then used as the final non-dominated solution set. The method for selecting the next generation of individuals based on the non-dominated ranking result and the crowding degree of the individuals is to prioritize individuals with higher non-dominated ranking results, and if the non-dominated ranking results are the same, then individuals with greater crowding degree are prioritized; the termination condition can be reaching the pre-set maximum number of iterations.

[0142] Optionally, in this embodiment of the invention, for each solution in the non-dominated solution set, the normalized value on each function of the multi-objective optimization function is calculated, the maximum and minimum values ​​of each function of the multi-objective optimization function in the non-dominated solution set are determined, the fuzzy membership function of each objective function is defined, the membership value of each solution on each function of the multi-objective optimization function is calculated, a membership matrix is ​​obtained, the analytic hierarchy process (AHP) is used to determine the function weights of each function of the multi-objective optimization function, a judgment matrix is ​​constructed to represent the importance of each objective, the eigenvector corresponding to the largest eigenvalue of the judgment matrix is ​​calculated, the eigenvector is normalized to obtain the weight vector of each function of the multi-objective optimization function, the comprehensive membership degree of each solution is calculated using the membership matrix and the weight vector, the comprehensive membership degree data is obtained, and the solution with the largest comprehensive membership degree is selected as the best compensation strategy based on the comprehensive membership degree data to obtain the optimal control parameters.

[0143] S140. Based on the estimated data of the system compensation parameters, perform system control prediction on the power quality compensation system to determine the real-time control quantity of the power quality compensation system.

[0144] Among them, the real-time control quantity can be a parameter for real-time control of the power quality compensation system; the real-time control quantity enables the power quality compensation system to respond quickly to instantaneous changes in the power grid.

[0145] Specifically, the power quality compensation system is predicted by estimating the system compensation parameters to determine the real-time control parameters of the power quality compensation system.

[0146] S150. Perform control operations on the power quality compensation system according to the real-time control quantity, and determine the control operation result.

[0147] The control operation result can be the result of the power quality compensation system performing control operations.

[0148] Specifically, in a power quality compensation system, the drive circuit is subjected to actual compensation operations through real-time control quantities to obtain the control operation results.

[0149] S160. Based on the control operation results, optimize the optimal control parameters to obtain target control parameters, and determine the target compensation current command according to the target control parameters.

[0150] The target control parameters can be the control parameters that achieve optimal performance in the power quality compensation system. These target control parameters include the optimal modulation ratio of the static var generator (SVM), the optimal compensation capacity of the fixed capacitor, the optimal coordination control coefficients of the SVM and the fixed capacitor, the optimal cutoff frequency of the bandpass filter, the optimal cutoff frequency of the low-pass filter, and the optimal compensation current command. The target compensation current command can be the instruction information for controlling the power quality compensation system.

[0151] Specifically, after obtaining the control operation results of the power quality compensation system based on real-time control quantities, the optimal control parameters are optimized based on the control operation results to obtain the target control parameters, and the target compensation current command is determined based on the target control parameters.

[0152] The technical solution of this invention collects three-phase power grid data for a power quality compensation system; extracts feature parameters from the three-phase power grid data to determine system state feature data, which can serve as the basis for optimized power system scheduling and improve the economic efficiency of the power system; estimates system compensation parameters for the power quality compensation system based on the system state feature data and the three-phase power grid data to determine estimated system compensation parameters; and effectively controls the reactive power of the power system based on the estimated system compensation parameters, thereby improving the stability of the power system; finally, it determines the optimal control parameters for the power quality compensation system based on the system state feature data and the estimated system compensation parameters, which further improves the stability and operational efficiency of the power system. This method improves the efficiency and effectively controls the cost of the power system, enhancing its economic benefits. Based on the estimated system compensation parameters, it predicts system control for the power quality compensation system, determines the real-time control quantity, performs control operations on the system based on the real-time control quantity, determines the control operation result, and achieves rapid control response of the power system, improving power quality. Based on the control operation result, it optimizes the optimal control parameters to obtain target control parameters, determines the target compensation current command based on the target control parameters, and through precise control of the target compensation current, effectively improves the response efficiency of the power system, enhances power quality, and strengthens stability and economic benefits. It solves the technical problem of system instability caused by the joint compensation of static var generators and fixed capacitors, achieving efficient control and meeting the requirements of economy, energy efficiency, and reliability.

[0153] Figure 2 This is a flowchart of another stability control method for a power quality management system provided by an embodiment of the present invention. The relationship between this embodiment and the above embodiments is that this is a specific method for optimizing the optimal control parameters. Figure 2 As shown, the method includes:

[0154] S210. Collect three-phase power grid data of the power quality compensation system; extract characteristic parameters based on the three-phase power grid data to determine system state characteristic data.

[0155] S220. Based on the system state characteristic data and the three-phase data of the power grid, the system compensation parameters of the power quality compensation system are estimated, and the estimated system compensation parameters are determined.

[0156] S230. Determine the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data.

[0157] S240. Based on the estimated data of the system compensation parameters, perform system control prediction on the power quality compensation system to determine the real-time control quantity of the power quality compensation system.

[0158] S250. Perform control operations on the power quality compensation system according to the real-time control quantity, and determine the control operation result.

[0159] Optionally, in another optional embodiment of the present invention, the step of performing system control prediction on the power quality compensation system based on the system compensation parameter estimation data to determine the real-time control quantity of the power quality compensation system includes:

[0160] A discrete-time prediction model is constructed based on the system parameter estimation data to determine the system state prediction equation; a rolling time-domain optimization problem is constructed based on the system state prediction equation to obtain the rolling time-domain optimization function and the second constraint; the rolling time-domain optimization function and the second constraint are solved using a sequential quadratic programming algorithm to determine the optimal control sequence; feedforward control and feedback correction are performed on the optimal control sequence to determine the real-time control quantity.

[0161] The system state prediction equation can be the prediction equation corresponding to the discrete-time prediction model. Optionally, the system state matrix, input matrix, output matrix, and direct transfer matrix are constructed based on the system parameter estimation data; the disturbance matrix is ​​estimated based on the historical data of the power quality compensation system; the discrete-time state prediction equation of the discrete-time prediction model is constructed based on the system state matrix, input matrix, output matrix, direct transfer matrix, and disturbance matrix; the output prediction equation is constructed based on the output matrix and direct transfer matrix; and the system state prediction equation is determined based on the discrete-time state prediction equation and the output prediction equation.

[0162] The rolling time-domain optimization function can be an optimization function applied to the rolling time-domain optimization problem; the second constraint can be a control objective pre-set based on the physical constraints of the power quality compensation system; the second constraint includes: upper and lower limits of control quantity, upper and lower limits of control increment, and upper and lower limits of state variables; the second constraint can make the multiple rolling time-domain optimization functions more closely fit the constraints of the rolling time-domain optimization problem. Optionally, based on the discrete-time prediction model corresponding to the system state prediction equation, a prediction time domain is defined, and a reference trajectory in the prediction time domain is constructed. A rolling time-domain optimization function is constructed by defining a state deviation weight matrix and a control increment weight matrix. Based on the physical constraints of the power quality compensation system, upper and lower limits of control quantity, control increment, and state variables are set as upper and lower limits. The rolling time-domain optimization function and the second constraint are combined to obtain the complete rolling time-domain optimization problem.

[0163] Optionally, the sequential quadratic programming algorithm solves for the objective optimization function and the second constraint as follows: Based on the rolling time-domain optimization function and the second constraint corresponding to the rolling time-domain optimization problem, a Lagrangian function is constructed, and the gradient and Hessian matrix of the Lagrangian function are calculated to obtain the gradient vector and Hessian matrix data. Based on the gradient vector and Hessian matrix data, a quadratic programming subproblem is constructed and solved to obtain the search direction. A one-dimensional search is performed based on the search direction to determine the optimal step size. Based on the optimal step size, a new iteration point is obtained. If the convergence condition is met, the optimal control sequence is obtained; if the convergence condition is not met, the process returns to the step of constructing and solving the quadratic programming subproblem. The convergence condition can be a pre-set maximum number of iterations.

[0164] Optionally, the process of feedforward control and feedback correction for the optimal control sequence is as follows: Extract the control quantity of the power quality compensation system at the current moment from the optimal control sequence as the feedforward control quantity. Decompose the feedforward control quantity into the modulation ratio component of the static var generator (SVM) and the compensation capacity component of the fixed capacitor. Obtain the current system state of the power quality compensation system, calculate the state deviation, and then design a state feedback controller. Decompose the state feedback controller into the modulation ratio correction quantity of the SVM and the compensation capacity correction quantity of the fixed capacitor. Add the modulation ratio component and the modulation ratio correction quantity of the SVM, and add the compensation capacity component and the compensation capacity correction quantity of the fixed capacitor to synthesize a composite control quantity. Based on the composite control quantity, generate the PWM pulse sequence of the SVM and the switching control signal of the fixed capacitor. Combine the PWM pulse sequence and the switching control signal to form a complete real-time control quantity.

[0165] S260. Calculate the comprehensive performance index of the power quality compensation system based on the system state characteristic data and the control operation results, and determine the system performance evaluation data corresponding to the power quality compensation system.

[0166] Among them, the system performance evaluation data can be the comprehensive performance index corresponding to the control operation results of the power quality compensation system.

[0167] Specifically, after the power quality compensation system executes real-time control quantities, the control operation results of the power quality compensation system are obtained. From the control operation results of the power quality compensation system, the system state characteristic data are used to calculate the stability index, the degree of harmonic distortion reduction is calculated based on the total harmonic distortion data, and the economic index is calculated based on the static var generator capacity data. Based on the stability index, the degree of harmonic distortion reduction, and the calculated economic index, the system performance evaluation data is determined by calculating the function weights of each function in the multi-objective optimization function.

[0168] S270. Construct a performance prediction model based on the system performance evaluation data to obtain a performance prediction function.

[0169] Among them, the performance prediction function can be a function that predicts the performance corresponding to the control parameters of the power quality compensation system.

[0170] Optionally, an input feature vector and an output vector are constructed based on system performance evaluation data. The input weight matrix and bias vector are randomly initialized. The hidden layer output is calculated based on the input feature vector, input weight matrix, and bias vector. The output weight vector is then solved using the least squares method based on the hidden layer output and the output vector. Finally, a performance prediction function is obtained based on the input feature vector, input weight matrix, bias vector, and weight vector. The input feature vector includes the control parameters and system state characteristic data of the power quality compensation system; the output vector corresponds to the system performance index of the power quality compensation system.

[0171] S280. The optimal control parameters are optimized by applying a performance prediction function using a Bayesian optimization algorithm to obtain target control parameters, and the target compensation current command is determined based on the target control parameters.

[0172] Specifically, the optimal control parameters are optimized using a Bayesian optimization algorithm and a performance prediction function to obtain the target control parameters, and the target compensation current command is determined based on the target control parameters.

[0173] Optionally, in another optional embodiment of the present invention, the step of optimizing the optimal control parameters by applying a performance prediction function using a Bayesian optimization algorithm to obtain target control parameters, and determining the target compensation current command based on the target control parameters, includes:

[0174] Define the upper and lower bounds of the control parameter search space, set a reasonable range of variation for each parameter in the control parameter search space, and normalize each parameter to obtain a standard control parameter search space; construct a performance dataset based on the standard control parameter search space and the performance prediction function; initialize a pre-set first surrogate model based on the performance dataset to obtain a second surrogate model; find the maximum value in the standard control parameter search space using a preset expected improvement function to determine the target parameter point; predict the performance of the target parameter point based on the performance prediction function to determine the target performance data, and update the performance dataset with the target performance data; optimize and update the second surrogate model based on the performance dataset to determine the target surrogate model; if the target surrogate model meets the preset convergence condition, obtain the target control parameters based on the performance dataset, and determine the target compensation current command based on the target control parameters.

[0175] The control parameter search space can be a set of possible control parameter value ranges defined by the Bayesian optimization algorithm. Optionally, based on the physical constraints and prior knowledge corresponding to the control parameters of the power quality compensation system, a reasonable range of variation for each control parameter is set, and each control parameter is normalized to obtain a standard control parameter search space. The standard control parameter search space is the control parameter search space after normalization.

[0176] The performance dataset can be the performance corresponding to the initial points in the standard control parameter search space. Optionally, multiple initial points are uniformly sampled in the standard control parameter search space, and the performance of each initial point is evaluated using the performance prediction function described above. The performance dataset is then constructed based on the initial points and their performance.

[0177] The first surrogate model can be a pre-defined Gaussian process. It should be noted that by using a Gaussian process as the first surrogate model, defining its kernel function, setting its hyperparameters, and optimizing the hyperparameters of the Gaussian process using maximum likelihood estimation or cross-validation, the first surrogate model is obtained.

[0178] The second proxy model can be a model obtained by training the first proxy model on the performance dataset. Optionally, the second proxy model can be obtained by training the first proxy model on the performance dataset.

[0179] The preset expected improvement function can be a pre-set acquisition function. Optionally, the target parameter point is determined by searching for the maximum value of the preset expected improvement function in the standard control parameter search space. The target parameter point can be the point where the preset expected improvement function reaches its maximum value in the standard control parameter search space.

[0180] Optionally, the performance of the target parameter points is predicted based on the performance prediction function to determine the target performance data. This target performance data is then updated to the performance dataset to determine the updated performance dataset. The second surrogate model is then optimized and updated based on the updated performance dataset to determine the target surrogate model. If the target surrogate model meets a preset convergence condition, the target control parameters are obtained from the performance dataset, and the target compensation current command is determined based on these parameters. The target surrogate model can be a model obtained by training the second surrogate model using the updated performance dataset; the preset convergence condition can be reaching the maximum number of iterations.

[0181] This invention significantly improves the dynamic response capability and control accuracy of the system by applying predictive control and composite control strategies. Through the combination of forward-looking control and real-time feedback, the power quality compensation system can anticipate changes in the power grid and accurately compensate for various power quality problems. This enables the system to maintain efficient and stable operation even when facing complex and ever-changing power grid conditions. Comprehensive performance evaluation and adaptive optimization provide the power quality compensation system with the ability to continuously improve. Through real-time performance evaluation and prediction, combined with intelligent parameter optimization, the power quality compensation system can continuously learn and adapt to new power grid environments, achieving long-term performance improvement.

[0182] Figure 3 This invention provides a schematic diagram of the process control of a power quality management system according to an embodiment of the present invention; for example... Figure 3 As shown: In a power quality management system, instantaneous three-phase current and voltage are detected and collected. Instantaneous reactive power is calculated based on these data. A corresponding reactive power compensation strategy is then calculated based on this reactive power. The system determines whether protection trigger conditions are triggered. If a protection trigger condition is triggered, fault information is recorded and the system is immediately shut down. If no protection trigger condition is triggered, a control signal is sent to switch capacitors. Dynamic reactive power compensation is performed using a static var generator, and the reactive power compensation strategy is optimized through human-machine interaction control. Instantaneous reactive power can be calculated using instantaneous reactive power theory algorithms. The reactive power compensation strategy can increase or decrease reactive power. The protection trigger conditions can be pre-set conditions in the power quality management system, such as voltage or current anomalies.

[0183] Optionally, dynamic reactive power compensation can be achieved by adjusting the connection or disconnection of capacitor banks through reactive power compensation strategies. The power quality management system provides a human-machine interface, allowing operators to monitor the operating status of the power quality management system and manually adjust or automatically optimize the reactive power compensation strategy to adapt to changes in the power grid, ensuring the stability and efficiency of the power system, while reducing energy loss and improving power quality.

[0184] Figure 4 This is a schematic diagram of a stability control device for a power quality management system provided in an embodiment of the present invention. Figure 4 As shown, the device includes: a system data acquisition module 410, a system parameter estimation module 420, an optimal operating parameter calculation module 430, a system control calculation module 440, a system control module 450, and a compensation current calculation module 460, wherein...

[0185] The system data acquisition module 410 is used to acquire three-phase power grid data of the power quality compensation system; extract characteristic parameters based on the three-phase power grid data to determine system state characteristic data; wherein, the three-phase power grid data includes instantaneous three-phase voltage and instantaneous three-phase current.

[0186] The system parameter estimation module 420 is used to estimate the system compensation parameters of the power quality compensation system based on the system state characteristic data and the three-phase data of the power grid, and to determine the system compensation parameter estimation data.

[0187] The optimal operating parameter calculation module 430 is used to determine the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data.

[0188] The system control calculation module 440 is used to perform system control prediction on the power quality compensation system based on the system compensation parameter estimation data, and to determine the real-time control quantity of the power quality compensation system.

[0189] The system control module 450 is used to perform control operations on the power quality compensation system according to the real-time control quantity and determine the control operation result;

[0190] The compensation current calculation module 460 is used to optimize the optimal control parameters based on the control operation results to obtain target control parameters, and determine the target compensation current command based on the target control parameters.

[0191] The technical solution of this invention collects three-phase power grid data for a power quality compensation system; extracts feature parameters from the three-phase power grid data to determine system state feature data, which can serve as the basis for optimized power system scheduling and improve the economic efficiency of the power system; estimates system compensation parameters for the power quality compensation system based on the system state feature data and the three-phase power grid data to determine estimated system compensation parameters; and effectively controls the reactive power of the power system based on the estimated system compensation parameters, thereby improving the stability of the power system; finally, it determines the optimal control parameters for the power quality compensation system based on the system state feature data and the estimated system compensation parameters, which further improves the stability and operational efficiency of the power system. This method improves the efficiency and effectively controls the cost of the power system, enhancing its economic benefits. Based on the estimated system compensation parameters, it predicts system control for the power quality compensation system, determines the real-time control quantity, performs control operations on the system based on the real-time control quantity, determines the control operation result, and achieves rapid control response of the power system, improving power quality. Based on the control operation result, it optimizes the optimal control parameters to obtain target control parameters, determines the target compensation current command based on the target control parameters, and through precise control of the target compensation current, effectively improves the response efficiency of the power system, enhances power quality, and strengthens stability and economic benefits. It solves the technical problem of system instability caused by the joint compensation of static var generators and fixed capacitors, achieving efficient control and meeting the requirements of economy, energy efficiency, and reliability.

[0192] Optionally, the compensation current calculation module is specifically used for:

[0193] The system performance evaluation unit is used to calculate the comprehensive performance index of the power quality compensation system based on the system state characteristic data and the control operation results, and to determine the system performance evaluation data corresponding to the power quality compensation system.

[0194] The performance prediction function construction unit is used to construct a performance prediction model based on the system performance evaluation data and obtain a performance prediction function.

[0195] The instruction calculation unit is used to optimize the optimal control parameters by applying a performance prediction function through a Bayesian optimization algorithm to obtain target control parameters, and to determine the target compensation current instruction based on the target control parameters.

[0196] Optionally, the instruction calculation unit is used for:

[0197] The parameter calculation subunit is used to define the upper and lower bounds of the control parameter search space, set the reasonable variation range of each parameter in the control parameter search space, and normalize each parameter to obtain the standard control parameter search space.

[0198] The dataset computation subunit is used to construct a performance dataset based on the search space of the standard control parameters and the performance prediction function.

[0199] The proxy model update subunit is used to initialize a pre-set first proxy model based on the performance dataset to obtain a second proxy model;

[0200] The function computation subunit is used to find the maximum value in the standard control parameter search space through a preset expected improvement function to determine the target parameter point;

[0201] The performance prediction subunit is used to predict the performance of the target parameter point according to the performance prediction function, determine the target performance data, update the target performance data to the performance dataset, and determine the updated performance dataset.

[0202] The proxy model optimization subunit is used to optimize and update the second proxy model based on the updated performance dataset to determine the target proxy model.

[0203] The compensation current command calculation subunit is used to obtain the target control parameters based on the performance dataset and determine the target compensation current command based on the target control parameters, provided that the target proxy model meets the preset convergence conditions.

[0204] Optionally, the system data acquisition module is used for:

[0205] The data acquisition unit is used to acquire the three-phase power grid data of the power quality compensation system to obtain the raw data.

[0206] A denoising unit is used to perform wavelet threshold denoising processing on the original acquired data to obtain three-phase power grid data.

[0207] The three-phase power grid calculation unit is used to convert the three-phase power grid data to a two-phase orthogonal coordinate system to obtain the converted three-phase data.

[0208] A signal parameter estimation unit is used to perform parameter estimation on the converted three-phase data to obtain signal parameter estimation results;

[0209] The signal analysis unit is used to construct an analytical expression for the signal based on the signal parameter estimation results, thereby obtaining the analytical expression for the signal.

[0210] The system power calculation unit is used to calculate and determine the system power data based on the signal analytical expression;

[0211] The feature data extraction unit is used to perform time-frequency analysis on the system power data to obtain power fluctuation characteristic data, and to extract key feature parameters from the power fluctuation characteristic data to obtain system state feature data.

[0212] Optionally, the system parameter estimation module is used for:

[0213] The system equation construction unit is used to construct a nonlinear state-space model of the power quality compensation system based on system state characteristic data, and obtain the system dynamic equations.

[0214] The unscented Kalman filter calculation unit is used to perform state estimation and parameter identification based on the system dynamic equation and the real-time measurement data of the power quality compensation system through the unscented Kalman filter algorithm, and obtain the estimated value of the reactive power generator parameters.

[0215] The least squares recursive unit is used to estimate the parameters of the fixed capacitor by using an adaptive forgetting factor and the least squares method based on the converted three-phase data.

[0216] The data calculation unit is used to determine the estimated data of the system compensation parameters based on the estimated values ​​of the reactive power generator parameters and the estimated values ​​of the fixed capacitor parameters.

[0217] Optionally, the optimal operating parameter calculation module is used for:

[0218] The optimization function construction unit is used to construct a multi-objective optimization function and the first constraint condition from the system state characteristic data and system parameter estimation data;

[0219] The solution unit is used to solve the multi-objective optimization function and the constraints using a multi-objective differential evolution algorithm to determine the non-dominated solution set;

[0220] The control parameter calculation unit is used to apply a fuzzy decision method to the non-dominated solution set to select the best compensation strategy and obtain the optimal control parameters.

[0221] Optionally, the system control module is used for:

[0222] The system state prediction unit is used to construct a discrete-time prediction model based on the system parameter estimation data and determine the system state prediction equation.

[0223] The system optimization unit is used to construct a rolling time-domain optimization problem based on the system state prediction equation, and obtain the objective optimization function and the second constraint condition.

[0224] The optimal control sequence solving unit is used to solve the objective optimization function and the second constraint condition through a sequence quadratic programming algorithm to determine the optimal control sequence.

[0225] A real-time control unit is used to perform feedforward control and feedback correction on the optimal control sequence and to determine the real-time control quantity.

[0226] The stability control device for power quality management system provided in the embodiments of the present invention can execute the stability control method for power quality management system provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0227] Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their patterns are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0228] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0229] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer grids such as the Internet and / or various telecommunications grids.

[0230] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as stability control methods for power quality management systems.

[0231] In some embodiments, the stability control method for a power quality management system may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the stability control method for a power quality management system described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the stability control method for a power quality management system by any other suitable means (e.g., by means of firmware).

[0232] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0233] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the patterns / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0234] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0235] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0236] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or grid browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication grid). Examples of communication grids include local area networks (LANs), wide area networks (WANs), blockchain grids, and the Internet.

[0237] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact through a communication mesh. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0238] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0239] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the stability control method steps for a power quality management system as provided in any embodiment of the present invention. The method includes:

[0240] Collect three-phase power grid data for the power quality compensation system; extract characteristic parameters from the three-phase power grid data to determine system state characteristic data; wherein, the three-phase power grid data includes instantaneous three-phase voltage and instantaneous three-phase current.

[0241] Based on the system state characteristic data and the three-phase power grid data, the system compensation parameters of the power quality compensation system are estimated, and the estimated system compensation parameter data are determined.

[0242] The optimal control parameters of the power quality compensation system are determined based on the system state characteristic data and the system compensation parameter estimation data.

[0243] Based on the estimated data of the system compensation parameters, system control prediction is performed on the power quality compensation system to determine the real-time control quantity of the power quality compensation system.

[0244] The power quality compensation system is controlled according to the real-time control quantity, and the control operation result is determined.

[0245] Based on the control operation results, the optimal control parameters are optimized to obtain target control parameters, and the target compensation current command is determined according to the target control parameters.

[0246] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0247] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0248] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0249] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of mesh, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0250] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a grid of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0251] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0252] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A stability control method for a power quality management system, characterized in that, include: Collect three-phase power grid data for the power quality compensation system; extract characteristic parameters from the three-phase power grid data to determine system state characteristic data; wherein, the three-phase power grid data includes instantaneous three-phase voltage and instantaneous three-phase current. Based on the system state characteristic data and the three-phase power grid data, the system compensation parameters of the power quality compensation system are estimated, and the estimated system compensation parameter data are determined. The optimal control parameters of the power quality compensation system are determined based on the system state characteristic data and the system compensation parameter estimation data. Based on the estimated data of the system compensation parameters, system control prediction is performed on the power quality compensation system to determine the real-time control quantity of the power quality compensation system. The power quality compensation system is controlled according to the real-time control quantity, and the control operation result is determined. Based on the control operation results, the optimal control parameters are optimized to obtain target control parameters, and the target compensation current command is determined according to the target control parameters. The process of optimizing the optimal control parameters based on the control operation results to obtain target control parameters, and determining the target compensation current command based on the target control parameters, includes: Based on the system state characteristic data and the control operation results, the comprehensive performance index of the power quality compensation system is calculated to determine the system performance evaluation data corresponding to the power quality compensation system. A performance prediction model is constructed based on the system performance evaluation data, and a performance prediction function is obtained. The optimal control parameters are optimized by applying a performance prediction function using a Bayesian optimization algorithm to obtain target control parameters, and the target compensation current command is determined based on the target control parameters.

2. The method according to claim 1, characterized in that, The process of optimizing the optimal control parameters using a performance prediction function via a Bayesian optimization algorithm to obtain target control parameters, and determining the target compensation current command based on the target control parameters, includes: Define the upper and lower bounds of the control parameter search space, set a reasonable range of variation for each parameter in the control parameter search space, and normalize each parameter to obtain the standard control parameter search space; A performance dataset is constructed based on the search space of the standard control parameters and the performance prediction function; The first agent model is initialized based on the performance dataset to obtain the second agent model; The target parameter point is determined by searching for the maximum value in the standard control parameter search space using a preset expected improvement function; The target parameter point is predicted according to the performance prediction function to determine the target performance data, the target performance data is updated to the performance dataset, and the updated performance dataset is determined. The second agent model is optimized and updated based on the updated performance dataset to determine the target agent model; If the target proxy model satisfies the preset convergence condition, the target control parameters are obtained based on the performance dataset, and the target compensation current command is determined based on the target control parameters.

3. The method according to claim 1, characterized in that, The system collects three-phase power grid data from the power quality compensation system; based on the three-phase power grid data, it extracts feature parameters to determine system state feature data, including: The three-phase power grid data of the power quality compensation system are collected to obtain the raw data. The original data is subjected to wavelet threshold denoising processing to obtain the three-phase power grid data; The three-phase data of the power grid is converted to a two-phase orthogonal coordinate system to obtain the converted three-phase data; The converted three-phase data are used to perform parameter estimation to obtain signal parameter estimation results; Based on the signal parameter estimation results, an analytical expression for the signal is constructed, thus obtaining the analytical expression for the signal; The system power data is determined by calculating based on the signal analytical expression. Time-frequency analysis is performed on the system power data to obtain power fluctuation characteristic data. Key feature parameters are extracted from the power fluctuation characteristic data to obtain system state characteristic data.

4. The method according to claim 3, characterized in that The step of estimating system compensation parameters for the power quality compensation system based on the system state characteristic data and the three-phase data of the power grid, and determining the estimated system compensation parameters, includes: A nonlinear state-space model of the power quality compensation system is constructed based on system state characteristic data, and the system dynamic equations are obtained. The state estimation and parameter identification of the reactive power generator are performed by using the unscented Kalman filter algorithm based on the system dynamic equation and the real-time measurement data of the power quality compensation system, and the estimated values ​​of the reactive power generator parameters are obtained. The estimated values ​​of the fixed capacitor parameters are determined by estimating the converted three-phase data using an adaptive forgetting factor and the least squares method. The estimated data of the system compensation parameters are determined based on the estimated values ​​of the reactive power generator parameters and the estimated values ​​of the fixed capacitor parameters.

5. The method according to claim 4, characterized in that, Determining the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data includes: Construct a multi-objective optimization function and the first constraint condition using system state characteristic data and system parameter estimation data; The multi-objective optimization function and the constraints are solved by a multi-objective differential evolution algorithm to determine the non-dominated solution set; The optimal compensation strategy is selected by applying the fuzzy decision method to the non-dominated solution set, and the optimal control parameters are obtained.

6. The method according to claim 5, characterized in that, The step of performing system control prediction on the power quality compensation system based on the estimated system compensation parameters, and determining the real-time control quantities of the power quality compensation system, includes: Based on the system parameter estimation data, a discrete-time prediction model is constructed, and the system state prediction equation is determined. Based on the system state prediction equation, a rolling time-domain optimization problem is constructed to obtain the objective optimization function and the second constraint condition. The optimal control sequence is determined by solving the objective optimization function and the second constraint condition using a sequential quadratic programming algorithm. The optimal control sequence is subjected to feedforward control and feedback correction to determine the real-time control quantity.

7. A stability control device for a power quality management system, characterized in that, include: The system data acquisition module is used to collect three-phase power grid data for the power quality compensation system; extract characteristic parameters based on the three-phase power grid data to determine system state characteristic data; wherein, the three-phase power grid data includes instantaneous three-phase voltage and instantaneous three-phase current. The system parameter estimation module is used to estimate the system compensation parameters of the power quality compensation system based on the system state characteristic data and the three-phase data of the power grid, and to determine the system compensation parameter estimation data. The optimal operating parameter calculation module is used to determine the optimal control parameters of the power quality compensation system based on the system state characteristic data and the system compensation parameter estimation data. The system control calculation module is used to perform system control prediction on the power quality compensation system based on the estimated data of the system compensation parameters, and to determine the real-time control quantity of the power quality compensation system. The system control module is used to perform control operations on the power quality compensation system according to the real-time control quantity and determine the control operation result; The compensation current calculation module is used to optimize the optimal control parameters based on the control operation results to obtain target control parameters, and determine the target compensation current command based on the target control parameters; The compensation current calculation module includes: The system performance evaluation unit is used to calculate the comprehensive performance index of the power quality compensation system based on the system state characteristic data and the control operation results, and to determine the system performance evaluation data corresponding to the power quality compensation system. The performance prediction function construction unit is used to construct a performance prediction model based on the system performance evaluation data and obtain a performance prediction function. The instruction calculation unit is used to optimize the optimal control parameters by applying a performance prediction function through a Bayesian optimization algorithm to obtain target control parameters, and to determine the target compensation current instruction based on the target control parameters.

8. The apparatus according to claim 7, characterized in that, The instruction calculation unit includes: The parameter calculation subunit is used to define the upper and lower bounds of the control parameter search space, set the reasonable variation range of each parameter in the control parameter search space, and normalize each parameter to obtain the standard control parameter search space. The dataset computation subunit is used to construct a performance dataset based on the search space of the standard control parameters and the performance prediction function. The proxy model update subunit is used to initialize a pre-set first proxy model based on the performance dataset to obtain a second proxy model; The function computation subunit is used to find the maximum value in the standard control parameter search space through a preset expected improvement function to determine the target parameter point; The performance prediction subunit is used to predict the performance of the target parameter point according to the performance prediction function, determine the target performance data, update the target performance data to the performance dataset, and determine the updated performance dataset. The proxy model optimization subunit is used to optimize and update the second proxy model based on the updated performance dataset to determine the target proxy model. The compensation current command calculation subunit is used to obtain the target control parameters based on the performance dataset and determine the target compensation current command based on the target control parameters, provided that the target proxy model meets the preset convergence conditions.

9. The apparatus according to claim 7, characterized in that, The system data acquisition module includes: The data acquisition unit is used to acquire the three-phase power grid data of the power quality compensation system to obtain the raw data. A denoising unit is used to perform wavelet threshold denoising processing on the original acquired data to obtain three-phase power grid data. The three-phase power grid calculation unit is used to convert the three-phase power grid data to a two-phase orthogonal coordinate system to obtain the converted three-phase data. A signal parameter estimation unit is used to perform parameter estimation on the converted three-phase data to obtain signal parameter estimation results; The signal analysis unit is used to construct an analytical expression for the signal based on the signal parameter estimation results, thereby obtaining the analytical expression for the signal. The system power calculation unit is used to calculate and determine the system power data based on the signal analytical expression; The feature data extraction unit is used to perform time-frequency analysis on the system power data to obtain power fluctuation characteristic data, and to extract key feature parameters from the power fluctuation characteristic data to obtain system state feature data.

10. The apparatus according to claim 9, characterized in that, The system parameter estimation module includes: The system equation construction unit is used to construct a nonlinear state-space model of the power quality compensation system based on system state characteristic data, and obtain the system dynamic equations. The unscented Kalman filter calculation unit is used to perform state estimation and parameter identification based on the system dynamic equation and the real-time measurement data of the power quality compensation system using the unscented Kalman filter algorithm, and to obtain the estimated values ​​of the reactive power generator parameters. The least squares recursive unit is used to estimate the parameters of the fixed capacitor based on the converted three-phase data using an adaptive forgetting factor and the least squares method. The data calculation unit is used to determine the estimated data of the system compensation parameters based on the estimated values ​​of the reactive power generator parameters and the estimated values ​​of the fixed capacitor parameters.

11. The apparatus according to claim 10, characterized in that, The optimal operating parameter calculation module includes: The optimization function construction unit is used to construct a multi-objective optimization function and the first constraint condition from the system state characteristic data and system parameter estimation data; The solution unit is used to solve the multi-objective optimization function and the constraints using a multi-objective differential evolution algorithm to determine the non-dominated solution set; The control parameter calculation unit is used to apply a fuzzy decision method to the non-dominated solution set to select the best compensation strategy and obtain the optimal control parameters.

12. The apparatus according to claim 11, characterized in that, The system control module includes: The system state prediction unit is used to construct a discrete-time prediction model based on the system parameter estimation data and determine the system state prediction equation. The system optimization unit is used to construct a rolling time-domain optimization problem based on the system state prediction equation, and obtain the objective optimization function and the second constraint condition. The optimal control sequence solving unit is used to solve the objective optimization function and the second constraint condition through a sequence quadratic programming algorithm to determine the optimal control sequence. A real-time control unit is used to perform feedforward control and feedback correction on the optimal control sequence and to determine the real-time control quantity.

13. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the stability control method for a power quality management system as described in any one of claims 1-6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the stability control method for a power quality management system as described in any one of claims 1-6.

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