High-proportion new energy system voltage stability domain prediction and correction method and system

Abstract

The invention relates to the technical field of power system optimization, in particular to a high-proportion new energy system voltage stability domain prediction and correction method and system, and the method comprises the steps: obtaining an initial quadratic approximation expression; based on the node voltage equation and the injection power unbalance amount, an accurate alternating current power flow model of the power system is established, and a final Jacobian matrix is obtained; starting from an initial operation point on the initial stability boundary, performing iteration according to the final Jacobian matrix to obtain an accurate stability critical point; obtaining a local quadratic approximation expression of each expansion reference point, and obtaining a plurality of approximate stable critical points; starting from a plurality of approximate stability critical points, performing iteration according to a final Jacobian matrix to obtain a plurality of accurate stability critical points, and obtaining a global optimal quadratic expression; and predicting and correcting the voltage stability domain of the new energy system through a global optimal quadratic expression. The method improves the prediction and correction accuracy of the voltage stability domain of the new energy system.

Description

Technical Field

[0001] This invention relates to the field of power system optimization technology, and in particular to a method and system for predicting and correcting the voltage stability domain of a high-proportion renewable energy system. Background Technology

[0002] With the rapid development of the global economy, the electricity load demand of high-proportion renewable energy power systems continues to grow. Especially in renewable energy sending-end bases and DC transmission scenarios, the system operating point is increasingly approaching the voltage stability region boundary (VSRB), significantly increasing the risk of voltage instability. Constructing an accurate VSRB is crucial for ensuring stable system operation. Currently, VSRB construction methods are mainly divided into fitting methods and analytical methods. Fitting methods rely on a large number of stability critical point searches and data fitting. Although the coverage capability can be improved through continuous power flow, direct methods, nonlinear programming, and various optimization strategies (such as fixed-direction search and variable-step-size iteration), the computation time is too long to meet the needs of real-time analysis. Analytical methods, while computationally fast, suffer from insufficient accuracy due to neglecting higher-order terms, and their applicability is limited, especially in complex systems. In addition, the traditional Newton method is prone to divergence near the voltage instability point, and fitting methods are prone to getting trapped in local optima, failing to balance efficiency and accuracy. Therefore, a new method that can efficiently and accurately construct VSRBs and is applicable to high-proportion renewable energy power systems is urgently needed. Summary of the Invention

[0003] This invention provides a method and system for predicting and correcting the voltage stability domain of a high-proportion renewable energy system, which solves the problems of low computational efficiency, insufficient accuracy and poor convergence of traditional voltage stability domain construction methods in high-proportion renewable energy power systems.

[0004] The objective of this invention can be achieved through the following technical solutions: The first aspect of this invention is to provide a method for predicting and correcting the voltage stability domain of a high-proportion renewable energy system, comprising: A critical condition function for voltage stability is obtained, and a stable operating point close to the stability boundary is determined. The critical condition function is then subjected to a Taylor expansion at the stable operating point to obtain a general form of a quadratic expression ignoring higher-order terms. The first and second partial derivatives of the critical condition function with respect to the control variables are calculated and compared with the coefficients of the general form of the quadratic expression to obtain the initial coefficients. These initial coefficients are then substituted into the general form of the quadratic expression to obtain an initial quadratic approximation expression. Based on the nodal voltage equations and the injected power imbalance, an accurate AC power flow model of the power system is established, and the final Jacobian matrix is ​​obtained. From the set of all feasible and stable operating points of the power system, select a point on the initial stability boundary and denote it as the initial operating point. Starting from the initial operating point on the initial stability boundary, iterate according to the final Jacobian matrix to obtain an exact stability critical point. Determine several expansion reference points through the exact stability critical point. Through the process of obtaining the initial quadratic approximation expression, obtain the local quadratic approximation expression for each expansion reference point, and obtain several approximate stability critical points through the local quadratic approximation expressions. Starting from several approximate stability critical points, iterate according to the final Jacobian matrix to obtain several exact stability critical points, and form a set of exact stability critical points. A set of precise stable critical points is fitted using the trust domain least squares method to obtain the globally optimal quadratic expression; the voltage stability domain prediction and correction of the new energy system is realized through the globally optimal quadratic expression.

[0005] Furthermore, the Taylor expansion of the critical condition function at the stable operating point to obtain the general form of the quadratic expression ignoring higher-order terms includes:

[0006] In the formula, Indicates the first One control variable, Indicates the first One control variable, Indicates the first The coefficients of the linear terms of the control variables. Indicates the first The first control variable and the first The coefficients of the quadratic terms of the control variables, This indicates the number of all control variables.

[0007] Furthermore, based on the node voltage equations and injected power imbalance, an accurate AC power flow model of the power system is established, and the final Jacobian matrix is ​​obtained, including: Based on the nodal voltage equations and injected power imbalance, an accurate AC power flow model of the power system is established; the AC power flow model is specifically expressed by the following formula:

[0008] In the formula, This represents the imbalance of active power injected into the bus. This represents the unbalanced amount of reactive power injected into the bus. This represents the correction amount for the voltage amplitude. This represents the correction amount for the voltage phase angle. This indicates the sensitivity of active power to voltage amplitude. This indicates the sensitivity of active power to voltage phase angle. This indicates the sensitivity of reactive power to voltage amplitude. This indicates the sensitivity of reactive power to voltage phase angle. Represents the partial differential symbol. This represents the active power injected into the node. This represents the reactive power injected into the node. Indicates the voltage amplitude at the node. Indicates the voltage phase angle at the node. Represents the Jacobian matrix. Indicates change or variable; By inverting the Jacobian matrix and combining it with an accurate AC power flow model of the power system, the corrections for voltage amplitude and voltage phase angle are obtained, specifically expressed by the following formula:

[0009] In the formula, Describes the inverse of the Jacobian matrix. This represents the sensitivity matrix of voltage amplitude to active power. This represents the sensitivity matrix of voltage amplitude to reactive power. This represents the sensitivity matrix of voltage phase angle to active power. This represents the sensitivity matrix of voltage phase angle to reactive power. Based on the AC power flow model, the voltage amplitude and phase angle are iteratively corrected several times to obtain the inverse of the final Jacobian matrix.

[0010] Furthermore, the step of performing several iterative corrections on the voltage amplitude and phase angle based on the AC power flow model to obtain the inverse of the final Jacobian matrix includes: The specific process of performing several iterative corrections is as follows: Step 1: Select an initial voltage state value, denoted as the initial voltage parameter, and use it as the input for the voltage parameter. Then adjust it to step 2. Step 2: Substitute the input voltage parameters into the node voltage equation to obtain the calculated value of the injected power for each bus, and record it as the calculated injected power for each bus; set a reference injected power, subtract the calculated injected power for each bus from the reference injected power to obtain the power imbalance for each bus, and then jump to step 3. Step 3: When the balance of all buses is less than the first convergence accuracy threshold, stop the iterative calculation and jump to step 5; otherwise, perform the subsequent iterative calculation and jump to step 4. Step 4: Obtain the overall power imbalance of the system through the power imbalance of all buses; obtain the Jacobian matrix under the current voltage parameters; based on the overall power imbalance of the system, the Jacobian matrix under the current voltage parameters, and the AC power flow model, obtain the correction amount of the current voltage amplitude and the correction amount of the voltage phase angle; take the sum of the current voltage amplitude and the correction amount of the current voltage amplitude as the correction amplitude of the current voltage, and take the sum of the current voltage phase angle and the correction amount of the voltage phase angle as the correction phase angle of the current voltage; take the correction amplitude and correction phase angle of the current voltage as the input of the voltage parameters and jump to step 1. Step 5: End the iteration process and output the final Jacobian matrix after stopping the iteration, and use it as the final Jacobian matrix; Specifically, the overall power imbalance of the system is obtained by considering the power imbalance of all buses, which is expressed as: forming a vector from the power imbalance of all buses. ,in for ;in, This represents the imbalance of active power at the first bus. This represents the unbalance of reactive power at the first bus. Indicates the first The imbalance of active power of each bus. Indicates the first The unbalance of reactive power of each bus. This represents the transpose symbol.

[0011] Furthermore, the process of obtaining an exact stable critical point by iterating from the initial running point on the initial stable boundary according to the final Jacobian matrix includes: Starting from the initial running point on the initial stability boundary, an exact stable critical point is obtained by iterating using Newton's downhill method based on the final Jacobian matrix. The iterative formula for Newton's downhill method is specifically expressed as follows:

[0012] In the formula, Indicates the first The current actual value of the system control variable at the next iteration. Indicates the first The trial value of the control variable in the next iteration; Indicates the first In the next iteration, the system is at the current actual value The criterion function value used to determine the degree of voltage stability at the operating point; express The derivative of the function at the current actual value The function value at the execution point, Indicates the downslope factor; in, ;in, This represents the final Jacobian matrix.

[0013] Further, the process involves determining several expansion reference points through precise stable critical points; obtaining a local quadratic approximation expression for each expansion reference point through the initial quadratic approximation expression acquisition process, and obtaining several approximate stable critical points through these local quadratic approximation expressions; starting from these approximate stable critical points, iterating according to the final Jacobian matrix to obtain several precise stable critical points, forming a set of precise stable critical points, including: Several stable search directions are determined, and the precise stable critical point is moved by a preset step length in the opposite direction of the several stable search directions to obtain several expansion reference points; according to the process of obtaining the initial quadratic approximation expression, the local quadratic approximation expression of each expansion reference point is obtained. Based on the stable search direction and preset step size of each expanded reference point, obtain the linear expression of the control variable corresponding to each expanded reference point; solve the system of linear expression of the control variable corresponding to each expanded reference point and the local quadratic approximation expression of each expanded reference point to obtain the approximate stable critical point corresponding to each expanded reference point; similarly, obtain the approximate stable critical points corresponding to all expanded reference points; thus, obtain several approximate stable critical points. Starting from each approximately stable critical point, an exact stable critical point is obtained by iterating using Newton's downhill method based on the final Jacobian matrix; similarly, several exact stable critical points are obtained; thus, a set of exact stable critical points is obtained. The iterative formula for Newton's downhill method is specifically expressed as follows:

[0014] In the formula, Indicates the first The current actual value of the system control variable at the next iteration. Indicates the first The trial value of the control variable in the next iteration; Indicates the first In the next iteration, the system is at the current actual value The criterion function value used to determine the degree of voltage stability at the operating point; express The derivative of the function at the current actual value The function value at the execution point, Indicates the downslope factor; in, ;in, This represents the final Jacobian matrix.

[0015] Furthermore, the step of fitting a set of exact stable critical points using the trust-domain least squares method to obtain the globally optimal quadratic expression includes: The global objective function is defined by a quadratic approximation expression for each precise stable critical point; a trust domain subproblem is established; the global objective function and the trust domain subproblem are iterated repeatedly until the error value of the global objective function is less than a preset second threshold, and finally the optimized coefficient vector is obtained, which includes the optimal first-order coefficient and the optimal quadratic coefficient; the globally optimal quadratic expression is obtained through the optimal first-order coefficient and the optimal quadratic coefficient. The global objective function is specifically expressed as follows:

[0016] In the formula, Indicates the first At the precise stable critical point, the first... The values ​​of the control variables, Indicates the first At the precise stable critical point, the first... The values ​​of the control variables, Indicates the first The coefficients of the linear terms of the control variables. Indicates the first The first control variable and the first The coefficients of the quadratic terms of the control variables, This indicates the number of all control variables. This represents the number of all precisely stable critical points. Represents the coefficient vector. This represents the error value of the global objective function. These represent the coefficients of the first and second terms when the error value of the global objective function is minimized. The nonlinear least squares problem is transformed into a trust domain subproblem; the trust domain subproblem is established, and its standard form is as follows:

[0017] In the formula, This indicates updating the step size vector. Indicates updating the step size vector transpose, This represents the gradient vector (i.e., the first derivative of the global objective function at the current iteration point). This represents the Hessian matrix (i.e., the second derivative matrix of the global objective function at the current iteration point). Represents the scaling matrix. Indicates the radius of the trust domain. Represents the L2 norm, This represents the function that takes the minimum value. This represents the update step size vector corresponding to the minimum value. , This indicates a constraint or a condition that satisfies the following conditions; in, ; where, scaling matrix Through the Hessian matrix It is constructed in this way.

[0018] A second aspect of the present invention is to provide a voltage stability domain prediction and correction system for a high-proportion renewable energy system, comprising: Initial Analysis Module: This module is used to obtain the critical condition function for voltage stability, determine a stable operating point close to the stability boundary, perform a Taylor expansion of the critical condition function at the stable operating point to obtain a general form of the quadratic expression ignoring higher-order terms, calculate the first and second partial derivatives of the critical condition function with respect to the control variables, and compare the coefficients with those of the general form of the quadratic expression to obtain the initial coefficients in the general form of the quadratic expression, substitute these initial coefficients into the general form of the quadratic expression to obtain an initial quadratic approximation expression, and establish an accurate AC power flow model of the power system based on the nodal voltage equations and injected power imbalance, thereby obtaining the final Jacobian matrix. The precise stability critical point search module is used to select a point on the initial stability boundary from the set of all feasible and stable operating points of the power system, denoted as the initial operating point; starting from the initial operating point on the initial stability boundary, it iterates according to the final Jacobian matrix to obtain a precise stability critical point; several expansion reference points are determined through the precise stability critical point; through the process of obtaining the initial quadratic approximation expression, a local quadratic approximation expression is obtained for each expansion reference point, and several approximate stability critical points are obtained through the local quadratic approximation expression; starting from several approximate stability critical points, iterates according to the final Jacobian matrix to obtain several precise stability critical points, and forms a set of precise stability critical points. The global optimal fitting module is used to fit a set of precise stable critical points using the trust domain least squares method to obtain the globally optimal quadratic expression; the voltage stability domain prediction and correction of the new energy system is realized through the globally optimal quadratic expression.

[0019] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned method for predicting and correcting the voltage stability domain of a high-proportion renewable energy system.

[0020] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for predicting and correcting the voltage stability domain of a high-proportion new energy system.

[0021] Compared with existing technologies, the beneficial effects of this invention are as follows: It obtains the critical condition function for voltage stability, determining a stable operating point close to the stability boundary; it performs a Taylor expansion of the critical condition function at the stable operating point to obtain a general form of a quadratic expression ignoring higher-order terms; it calculates the first and second-order partial derivatives of the critical condition function with respect to the control variables and compares the coefficients with those of the general form of the quadratic expression to obtain the initial coefficients in the general form of the quadratic expression; it substitutes the initial coefficients into the general form of the quadratic expression to obtain an initial quadratic approximation expression; based on the node voltage equations and injected power imbalance, it establishes an accurate AC power flow model of the power system and obtains the final Jacobian matrix, improving the accuracy of voltage amplitude and phase angle corrections, thereby enhancing the accuracy of Jacobian matrix acquisition; it selects a point on the initial stability boundary from the set of all feasible and stable operating points of the power system, denoted as the initial operating point; and it further refines the initial stability boundary... Starting from the initial running point, an exact stable critical point is obtained through iteration based on the final Jacobian matrix. Several expansion reference points are determined through the exact stable critical point. Through the process of obtaining the initial quadratic approximation expression, a local quadratic approximation expression is obtained for each expansion reference point, and several approximate stable critical points are obtained through the local quadratic approximation expressions. Starting from several approximate stable critical points, several exact stable critical points are obtained through iteration based on the final Jacobian matrix, and a set of exact stable critical points is formed, which improves the accuracy of obtaining the exact stable critical points. The trust domain least squares method is used to fit a set of exact stable critical points to obtain the globally optimal quadratic expression. The globally optimal quadratic expression is used to realize the prediction and correction of the voltage stability domain of the new energy system, which solves the problems of low computational efficiency, insufficient accuracy and poor convergence of the traditional voltage stability domain construction method in high-proportion new energy power systems, and improves the accuracy of the prediction and correction of the voltage stability domain of the new energy system. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0023] Figure 1This invention provides a flowchart illustrating the steps of a method for predicting and correcting the voltage stability domain of a high-proportion renewable energy system. Figure 2 This invention provides a schematic diagram of the module flow of a voltage stability domain prediction and correction system for a high-proportion new energy system; Figure 3 Diagram of WSCC 3-machine 9-bus system; Figure 4 The probability density plot of the frequency response of the WSCC 3-machine 9-bus system under different wind power penetration rates; Figure 5 Diagram of an IEEE 118 bus test system; Figure 6 This is a probability density plot of the frequency response of the IEEE 118 bus test system under different wind power penetration rates. Detailed Implementation

[0024] 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.

[0025] 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.

[0026] To address the problems existing in the background technology, a method and system for predicting and correcting the voltage stability domain of a high-proportion new energy system are designed, which has important practical significance.

[0027] like Figure 1 As shown, the first aspect of this invention is to provide a method for predicting and correcting the voltage stability domain of a high-proportion renewable energy system, comprising the following steps: Step S001: Obtain the critical condition function for voltage stability and determine a stable operating point close to the stability boundary; perform a Taylor expansion of the critical condition function at the stable operating point to obtain the general form of the quadratic expression ignoring higher-order terms; calculate the first and second partial derivatives of the critical condition function with respect to the control variables and compare the coefficients with the general form of the quadratic expression to obtain the initial coefficients in the general form of the quadratic expression; substitute the initial coefficients into the general form of the quadratic expression to obtain the initial quadratic approximation expression; based on the node voltage equations and the injected power imbalance, establish an accurate AC power flow model of the power system and obtain the final Jacobian matrix.

[0028] It's important to note that this step is necessary to quickly and initially approximate the voltage stability boundary (VSRB) of the system and provide direction and gradient information for subsequent precise searches. This step is crucial because in large-scale power systems with a high proportion of renewable energy sources, directly searching for the precise stability critical point from the current operating point is computationally intensive and inefficient. By employing an analytical method based on the voltage stability critical condition (singularity of the Jacobian matrix), the time-consuming point-by-point search can be bypassed. Using Taylor expansion and matrix operations, an approximate quadratic boundary expression can be quickly obtained. Simultaneously, calculating the voltage sensitivity matrix to the control variables reveals the pattern of system state changes with these variables. This acts like drawing a "map" and a "compass" for the subsequent precise search, ensuring that the second step, Newton's downhill method, converges efficiently to the true stability critical point in the correct direction. This lays the foundation for the entire method, balancing speed and directional accuracy.

[0029] Specifically, a voltage stability critical condition model is established; wherein, the voltage stability critical condition model is specifically expressed as follows:

[0030] In the formula, For the power flow equation, Represents the Jacobian matrix. Represents state variables (such as voltage magnitude and phase angle). This represents control variables (such as generator active / reactive power, load power). Indicates the computation of the Jacobian matrix The determinant value.

[0031] This is the theoretical foundation of the entire method. The model shows that at the stability boundary, the system must satisfy the power flow equations, and its Jacobian matrix must be singular (determinant is 0). This point is also called the saddle-nodal bifurcation point (SNB). A smooth surface Θ( ) exists in the space containing the SNB point. z )=0, meaning that the surface can be used to construct a VSRB. Where, Θ( z)=0 represents the hypersurface formed by the set of voltage stability critical points (SNB points), namely the voltage stability domain boundary (VSRB) (i.e., the critical surface that divides "safe" and "dangerous").

[0032] By definition , near the surface Θ( z The stable point of )=0 Expanding the equation by Taylor and ignoring higher-order quadratic terms, we obtain the quadratic polynomial equation Θ( z )=0; where, the quadratic polynomial equation Θ( z The general form of the quadratic expression corresponding to )=0 is:

[0033] In the formula, Indicates the first One control variable, Indicates the first One control variable, Indicates the first The coefficients of the linear terms of the control variables. Indicates the first The first control variable and the first The coefficients of the quadratic terms of the control variables, This indicates the number of all control variables.

[0034] The control variables include generator output (active power setpoint, extreme voltage setpoint, reactive power output) and load power (active load and reactive load). The VSRB surface is determined in The degree of tilt and translation in the dimension.

[0035] After calculating Θ( z The first and second partial derivatives of the eigenvalues ​​with respect to the control variable are given by the function )=0, and are related to the function By comparing the first and second partial derivatives of the control variables, the coefficients of the approximate expression can be obtained. and ,Will Let these be the initial coefficients of the single control variable. Let these be the initial coefficients of the cross-control variables.

[0036] Initial coefficients of a single control variable Initial coefficients of cross-control variables Substituting into the general form of the quadratic expression yields the initial quadratic approximation expression; specifically, the initial quadratic approximation expression is expressed as:

[0037] In the formula, Indicates the first One control variable, Indicates the first One control variable, Indicates the first The initial linear coefficients of the control variables, Indicates the first The first control variable and the first The initial quadratic coefficients of the control variables, This indicates the number of all control variables.

[0038] Thus, the initial quadratic approximation expression is obtained through the above method.

[0039] It should be noted that this method provides accurate initial system state and key sensitivity data for constructing the entire voltage stability domain algorithm. By solving the power flow equations, the bus voltage magnitude and phase angle at a given operating point are obtained, thereby determining the steady-state operating condition of the system. Simultaneously, the Jacobian matrix and its sensitivity information generated in this process provide essential mathematical models and directional guidance for subsequent calculations of the stability boundary approximation expression and critical point search, forming the foundation and prerequisite of the entire method.

[0040] Specifically, based on the nodal voltage equations and the injected power imbalance, an accurate AC power flow model of the power system is established; the AC power flow model is specifically expressed by the following formula:

[0041] In the formula, This represents the imbalance of active power injected into the bus. This represents the unbalanced amount of reactive power injected into the bus. This represents the correction amount for the voltage amplitude. This represents the correction amount for the voltage phase angle. This indicates the sensitivity of active power to voltage amplitude. This indicates the sensitivity of active power to voltage phase angle. This indicates the sensitivity of reactive power to voltage amplitude. This indicates the sensitivity of reactive power to voltage phase angle. Represents the partial differential symbol. This represents the active power injected into the node. This represents the reactive power injected into the node. Indicates the voltage amplitude at the node. Indicates the voltage phase angle at the node. Represents the Jacobian matrix. It indicates change or variable.

[0042] By inverting the Jacobian matrix and combining it with an accurate AC power flow model of the power system, the corrections for voltage amplitude and voltage phase angle are obtained, specifically expressed by the following formula:

[0043] In the formula, Describes the inverse of the Jacobian matrix. This represents the sensitivity matrix of voltage amplitude to active power. This represents the sensitivity matrix of voltage amplitude to reactive power. This represents the sensitivity matrix of voltage phase angle to active power. This represents the sensitivity matrix of voltage phase angle to reactive power.

[0044] The inverse of the final Jacobian matrix is ​​obtained by iteratively correcting the voltage amplitude and phase angle according to the AC power flow model. This includes obtaining several correction values ​​for voltage amplitude and phase angle based on the several correction values ​​for voltage amplitude and phase angle, and obtaining several corrected voltage amplitudes and phase angles based on these correction values. The inverse of the final Jacobian matrix is ​​then obtained based on these corrected voltage amplitudes and phase angles. (The purpose of this step is to solve for the state variable y of the system under a given control variable z. y includes voltage amplitude and voltage phase angle. A Jacobian matrix J is generated as a byproduct during the solution of variable y. The J matrix is ​​the absolutely necessary input data for calculating the coefficients of the expression.)

[0045] The specific process of performing several iterative corrections is as follows: Step 1: Select an initial voltage state value (including voltage amplitude and phase angle), and record it as the initial voltage parameter (initial voltage amplitude and initial voltage phase angle), and use it as the input of the voltage parameter; then adjust to step 2; Step 2: Substitute the input voltage parameters into the node voltage equation to obtain the calculated value of the injected power for each bus, and record it as the calculated injected power for each bus; set a reference injected power, subtract the calculated injected power for each bus from the reference injected power to obtain the power imbalance for each bus; then jump to step 3. Step 3: When the balance of all buses is less than the first convergence accuracy threshold, stop the iterative calculation and jump to step 5; otherwise, perform the subsequent iterative calculation and jump to step 4. Step 4: Obtain the overall power imbalance of the system through the power imbalance of all buses; obtain the Jacobian matrix under the current voltage parameters; based on the overall power imbalance of the system, the Jacobian matrix under the current voltage parameters, and the AC power flow model, obtain the correction amount of the current voltage amplitude and the correction amount of the voltage phase angle; take the sum of the current voltage amplitude and the correction amount of the current voltage amplitude as the correction amplitude of the current voltage, and take the sum of the current voltage phase angle and the correction amount of the voltage phase angle as the correction phase angle of the current voltage; take the correction amplitude and correction phase angle of the current voltage as the input of the voltage parameters and jump to step 1. Step 5: End the iteration process and output the final Jacobian matrix after stopping the iteration, and use it as the final Jacobian matrix.

[0046] Specifically, the overall power imbalance of the system is obtained by considering the power imbalance of all buses, which is expressed as: forming a vector from the power imbalance of all buses. ,in for ;in, This represents the imbalance of active power at the first bus. This represents the unbalance of reactive power at the first bus. Indicates the first The imbalance of active power of each bus. Indicates the first The unbalance of reactive power of each bus. This represents the transpose symbol.

[0047] In this embodiment, the first convergence accuracy threshold is: In this embodiment, the first convergence accuracy threshold is not specifically limited; the implementer can determine it according to specific circumstances. The node voltage equation is a well-known technique and will not be described in detail here.

[0048] Thus, the final Jacobian matrix is ​​obtained through the above method.

[0049] Step S002: Starting from the initial running point on the initial stable boundary, iterate according to the final Jacobian matrix to obtain an exact stable critical point; determine several expansion reference points through the exact stable critical point; obtain the local quadratic approximation expression for each expansion reference point through the process of obtaining the initial quadratic approximation expression, and obtain several approximate stable critical points through the local quadratic approximation expression; starting from several approximate stable critical points, iterate according to the final Jacobian matrix to obtain several exact stable critical points, and form a set of exact stable critical points.

[0050] It should be noted that, in order to obtain a high-precision voltage stability critical point to construct a reliable stability domain boundary, a Newton method with a downslope factor is adopted. Starting from the analytical approximate boundary, the search direction is guided by sensitivity, and a dynamic step size adjustment mechanism is introduced to overcome the convergence problem caused by the singularity of the Jacobian matrix. Thus, the set of accurate saddle-node bifurcation points that satisfy the strict critical conditions is robustly solved iteratively.

[0051] Specifically, a point on the initial stability boundary is selected from the set of all feasible and stable operating points of the power system and denoted as the initial operating point. Starting from the initial operating point on the initial stability boundary, an exact stable critical point is obtained by iterating through the Newton downhill method according to the final Jacobian matrix.

[0052] Thus, a precise stable critical point is obtained through the above method.

[0053] Several stable search directions are determined, and the precise stable critical point is moved by a preset step length in the opposite direction of several stable search directions to obtain several expansion reference points (i.e. points that retreat from the boundary back into the stable domain).

[0054] Based on the process of obtaining the initial quadratic approximation expression, the local quadratic approximation expression for each expanded reference point is obtained; thus, the local quadratic approximation expressions for all expanded reference points can be obtained.

[0055] Based on the stable search direction and the preset step size of each unfolded reference point, obtain the linear expression of the control variable z corresponding to each unfolded reference point; solve the system of linear expression of control variable z corresponding to each unfolded reference point and local quadratic approximation expression of each unfolded reference point to obtain the approximate stable critical point corresponding to each unfolded reference point; similarly, obtain the approximate stable critical points corresponding to all unfolded reference points; thus, obtain several approximate stable critical points.

[0056] Starting from each approximately stable critical point, an exact stable critical point is obtained by iterating using Newton's downhill method based on the final Jacobian matrix. Several exact stable critical points are obtained similarly.

[0057] Thus, a set of precise stable critical points has been obtained through the above method.

[0058] The iterative formula for Newton's downhill method is specifically expressed as follows:

[0059] In the formula, Indicates the first The current actual value of the system control variable at the next iteration. Indicates the first The trial value (i.e. the predicted value) of the control variable in the next iteration; Indicates the first In the next iteration, the system is at the current actual value The criterion function value used to determine the degree of voltage stability at the operating point; express The derivative function at the current actual value The function value at the execution point, This represents the downslope factor. Wherein, ;in, This represents the final Jacobian matrix.

[0060] The initial value of the downslope factor is 1. In subsequent iterations, if the calculation fails (i.e., the power flow does not converge), the value is reduced... (Such as halving) to shorten the step size, making the search process more conservative and stable, ensuring that it can "go downhill" to approach the solution, rather than diverging.

[0061] Thus, a set of precise stable critical points has been obtained.

[0062] Step S003: Use the trust domain least squares method to fit a set of precise stable critical points to obtain the globally optimal quadratic expression; use the globally optimal quadratic expression to realize the voltage stability domain prediction and correction of the new energy system.

[0063] It should be noted that, in order to transform the discrete and precise critical point data into a high-precision, globally optimal analytical model of the voltage stability domain boundary, the trust domain reflection least squares method is used to fit and solve the critical point set, and finally obtain the quadratic expression coefficients of the stability boundary, thereby realizing rapid quantitative evaluation and efficient monitoring of voltage stability.

[0064] Specifically, a global objective function is defined using a quadratic approximation expression for each precise stable critical point; a trust domain subproblem is established; and iterative iterations are performed based on the global objective function and the trust domain subproblem until the error value of the global objective function is less than a preset second threshold (i.e., it no longer significantly decreases or the iteration converges). Finally, an optimized coefficient vector is obtained, which includes the optimal linear coefficient and the optimal quadratic coefficient; the globally optimal quadratic expression is obtained using the optimal linear and optimal quadratic coefficients. In this embodiment, the preset second threshold is... In this embodiment, the preset second threshold is not specifically limited, and the implementer can determine it according to the specific situation.

[0065] The global objective function is specifically expressed as follows:

[0066] In the formula, Indicates the first At the precise stable critical point, the first... The values ​​of the control variables, Indicates the first At the precise stable critical point, the first... The values ​​of the control variables, Indicates the first The coefficients of the linear terms of the control variables. Indicates the first The first control variable and the first The coefficients of the quadratic terms of the control variables, This indicates the number of all control variables. This represents the number of all precisely stable critical points. Represents the coefficient vector. This represents the error value of the global objective function. These represent the coefficients of the first and second terms when the error value of the global objective function is minimized. The global objective function is a standard least-squares error function.

[0067] The nonlinear least squares problem is transformed into a trust domain subproblem; the trust domain subproblem is established, wherein the standard form of the trust domain subproblem is:

[0068] In the formula, This indicates updating the step size vector. Indicates updating the step size vector transpose, This represents the gradient vector (i.e., the first derivative of the global objective function at the current iteration point). This represents the Hessian matrix (i.e., the second derivative matrix of the global objective function at the current iteration point). Represents the scaling matrix. Indicates the radius of the trust domain. Represents the L2 norm, This represents the function that takes the minimum value. This represents the update step size vector corresponding to the minimum value. , This indicates a constraint or that the following conditions must be met.

[0069] in, Among them, the scaling matrix Through the Hessian matrix It is constructed in this way.

[0070] The voltage stability domain prediction and correction of the new energy system is realized by using a globally optimal quadratic expression.

[0071] Among them, the method of using the globally optimal quadratic expression to predict and correct the voltage stability domain of the new energy system is a well-known technique, and will not be elaborated on here.

[0072] This completes the voltage stability domain prediction and correction for the new energy system.

[0073] The specific implementation data for obtaining the globally optimal quadratic expression is shown below: Example 1: The system configuration and initialization scheme is as follows: The test system adopts a WSCC 3-machine 9-bus system, such as... Figure 3 As shown, the system includes 3 generators (one of which is a slack node), 9 buses, and 3 load nodes. Control variables include the active power and terminal voltage of generators 2 and 3, and the active and reactive power of load nodes 5, 6, and 8. The initial operating point is the rated operating point, the power flow equations are in polar coordinates, and the convergence accuracy is set to [value missing]. .

[0074] Experimental results show that, in terms of accuracy, compared with traditional DSA, TPPCA and OSA methods, the VSRB constructed by this method has an average error of 0.187% and a standard deviation of 0.053%, as shown in Table 1.

[0075] Table 1: Error (%) in constructing the stability region boundary

[0076] In terms of efficiency, this method takes only 0.247 seconds to construct a six-dimensional VSRB, which is 73% shorter than DSA, as shown in Table 2.

[0077] Table 2: Comparison of computation time for different stable voltage safety domain (VSRB) construction methods

[0078] In terms of visualization, the probability density of frequency response under different wind power penetration rates, such as... Figure 4 As shown, the two-dimensional projection displays the geometric characteristics of the VSRB in the generator and load power space, and the three-dimensional VSRB shape conforms to theoretical expectations.

[0079] Example 2: The system configuration and initialization scheme is as follows: The test system adopts the IEEE 118 bus test system, such as... Figure 5 As shown, the Midwestern United States interconnected system includes 54 generators, 118 buses, and 176 lines. The control variables cover the power injection of multiple generators and load nodes.

[0080] Step 1: Initial boundary calculation. Select load nodes 3 and 75 as observation dimensions, fix other control variables to zero, and calculate the initial boundary expression.

[0081] Step 2: Local optimization triggering. When the active power fluctuation at a node exceeds the threshold of 5MW, local optimization is performed, and the reactive power regulation is calculated:

[0082] In the formula, This indicates a suggestion at the node. The amount of reactive power compensation added above, This represents the proportionality coefficient (usually a value slightly less than 1, such as 0.95). Represents a node The upper limit voltage setting value, Represents a node The current voltage measurement value, This represents the reactive power-voltage sensitivity coefficient. This represents the function that takes the minimum value.

[0083] It should be noted that, since the system in Example 2 is large in scale and complex in structure, and is closer to the actual power grid, local optimization triggering is used; while the system in Example 1 is small in scale and the result is simple, so local optimization triggering is not required.

[0084] Experimental results show that, in terms of accuracy, the average error of the three-dimensional VSRB is 0.2321%, and the standard deviation is 0.0653%, as shown in Table 3.

[0085] Table 3: Stability region boundary construction error (%)

[0086] In terms of time efficiency, the construction time of this method is only 1.332 seconds, which is 47% better than the DSA method, as shown in Table 4.

[0087] Table 4: Comparison of computation time for different stable voltage safety domain (VSRB) construction methods

[0088] In terms of visualization, the probability density of frequency response under different wind power penetration rates, such as... Figure 6 As shown.

[0089] Verification using the WSCC 3-machine 9-bus and IEEE 118 bus systems shows that this method significantly outperforms traditional algorithms in terms of accuracy, efficiency, and applicability. It is particularly suitable for voltage stability analysis of power grids with a high proportion of new energy sources, providing a reliable tool for practical engineering applications.

[0090] This completes the embodiment.

[0091] like Figure 2 As shown, a second aspect of the present invention is to provide a voltage stability domain prediction and correction system for a high-proportion renewable energy system, comprising: Initial Analysis Module 101: This module is used to obtain the critical condition function for voltage stability, determine a stable operating point close to the stability boundary, perform a Taylor expansion of the critical condition function at the stable operating point to obtain a general form of the quadratic expression ignoring higher-order terms, calculate the first and second partial derivatives of the critical condition function with respect to the control variables, and compare the coefficients with those of the general form of the quadratic expression to obtain the initial coefficients in the general form of the quadratic expression, substitute the initial coefficients into the general form of the quadratic expression to obtain an initial quadratic approximation expression, and establish an accurate AC power flow model of the power system based on the node voltage equations and the injected power imbalance, and obtain the final Jacobian matrix. The precise stability critical point search module 102 is used to select a point on the initial stability boundary from the set of all feasible and stable operating points of the power system, denoted as the initial operating point; starting from the initial operating point on the initial stability boundary, iterates according to the final Jacobian matrix to obtain a precise stability critical point; determine several expansion reference points through the precise stability critical point; obtain a local quadratic approximation expression for each expansion reference point through the process of obtaining the initial quadratic approximation expression, and obtain several approximate stability critical points through the local quadratic approximation expression; starting from several approximate stability critical points, iterate according to the final Jacobian matrix to obtain several precise stability critical points, and form a set of precise stability critical points. The global optimal fitting module 103 is used to fit a set of precise stable critical points using the trust domain least squares method to obtain the global optimal quadratic expression; the voltage stability domain prediction and correction of the new energy system is realized through the global optimal quadratic expression.

[0092] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for predicting and correcting the voltage stability domain of a high-proportion new energy system.

[0093] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for predicting and correcting the voltage stability domain of a high-proportion new energy system.

[0094] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting and correcting the voltage stability domain of a high-proportion renewable energy system, characterized in that, include: Obtain the critical condition function for voltage stability and determine a stable operating point close to the stability boundary; The critical condition function is subjected to a Taylor expansion at the stable operating point to obtain the general form of the quadratic expression that ignores higher-order terms; The initial coefficients in the general form of the quadratic expression are obtained by calculating the first and second partial derivatives of the critical condition function with respect to the control variables and comparing them with the coefficients of the general form of the quadratic expression. Substituting the initial coefficients into the general form of the quadratic expression yields the initial quadratic approximation expression; Based on the nodal voltage equations and injected power imbalance, an accurate AC power flow model of the power system is established, and the final Jacobian matrix is ​​obtained. From the set of all feasible and stable operating points of the power system, select a point on the initial stability boundary and denote it as the initial operating point. Starting from the initial operating point on the initial stability boundary, iterate according to the final Jacobian matrix to obtain an exact stability critical point. Determine several expansion reference points through the exact stability critical point. Through the process of obtaining the initial quadratic approximation expression, obtain the local quadratic approximation expression for each expansion reference point, and obtain several approximate stability critical points through the local quadratic approximation expressions. Starting from several approximate stability critical points, iterate according to the final Jacobian matrix to obtain several exact stability critical points, and form a set of exact stability critical points. A set of precise stable critical points is fitted using the trust domain least squares method to obtain the globally optimal quadratic expression; the voltage stability domain prediction and correction of the new energy system is realized through the globally optimal quadratic expression.

2. The method for predicting and correcting the voltage stability domain of a high-proportion new energy system according to claim 1, characterized in that, The Taylor expansion of the critical condition function at the stable operating point yields a general form of a quadratic expression ignoring higher-order terms, including: In the formula, Indicates the first One control variable, Indicates the first One control variable, Indicates the first The coefficients of the linear terms of the control variables. Indicates the first The first control variable and the first The coefficients of the quadratic terms of the control variables, This indicates the number of all control variables.

3. The method for predicting and correcting the voltage stability domain of a high-proportion new energy system according to claim 1, characterized in that, The method establishes an accurate AC power flow model of the power system based on the nodal voltage equations and injected power imbalance, and obtains the final Jacobian matrix, including: Based on the nodal voltage equations and injected power imbalance, an accurate AC power flow model of the power system is established; the AC power flow model is specifically expressed by the following formula: In the formula, This represents the imbalance of active power injected into the bus. This represents the unbalanced amount of reactive power injected into the bus. This represents the correction amount for the voltage amplitude. This represents the correction amount for the voltage phase angle. This indicates the sensitivity of active power to voltage amplitude. This indicates the sensitivity of active power to voltage phase angle. This indicates the sensitivity of reactive power to voltage amplitude. This indicates the sensitivity of reactive power to voltage phase angle. Represents the partial differential symbol. This represents the active power injected into the node. This represents the reactive power injected into the node. Indicates the voltage amplitude at the node. Indicates the voltage phase angle at the node. Represents the Jacobian matrix. Indicates change or variable; By inverting the Jacobian matrix and combining it with an accurate AC power flow model of the power system, the corrections for voltage amplitude and voltage phase angle are obtained, specifically expressed by the following formula: In the formula, Describes the inverse of the Jacobian matrix. This represents the sensitivity matrix of voltage amplitude to active power. This represents the sensitivity matrix of voltage amplitude to reactive power. This represents the sensitivity matrix of voltage phase angle to active power. This represents the sensitivity matrix of voltage phase angle to reactive power. Based on the AC power flow model, the voltage amplitude and phase angle are iteratively corrected several times to obtain the inverse of the final Jacobian matrix.

4. The method for predicting and correcting the voltage stability domain of a high-proportion new energy system according to claim 3, characterized in that, The process of performing several iterative corrections on the voltage amplitude and phase angle based on the AC power flow model to obtain the inverse of the final Jacobian matrix includes: The specific process of performing several iterative corrections is as follows: Step 1: Select an initial voltage state value, denoted as the initial voltage parameter, and use it as the input for the voltage parameter. Then adjust it to step 2. Step 2: Substitute the input voltage parameters into the node voltage equation to obtain the calculated value of the injected power for each bus, and record it as the calculated injected power for each bus; set a reference injected power, subtract the calculated injected power for each bus from the reference injected power to obtain the power imbalance for each bus, and then jump to step 3. Step 3: When the balance of all buses is less than the first convergence accuracy threshold, stop the iterative calculation and jump to step 5; otherwise, perform the subsequent iterative calculation and jump to step 4. Step 4: Obtain the overall power imbalance of the system through the power imbalance of all buses; obtain the Jacobian matrix under the current voltage parameters; based on the overall power imbalance of the system, the Jacobian matrix under the current voltage parameters, and the AC power flow model, obtain the correction amount of the current voltage amplitude and the correction amount of the voltage phase angle; take the sum of the current voltage amplitude and the correction amount of the current voltage amplitude as the correction amplitude of the current voltage, and take the sum of the current voltage phase angle and the correction amount of the voltage phase angle as the correction phase angle of the current voltage; take the correction amplitude and correction phase angle of the current voltage as the input of the voltage parameters and jump to step 1. Step 5: End the iteration process and output the final Jacobian matrix after stopping the iteration, and use it as the final Jacobian matrix; Specifically, the overall power imbalance of the system is obtained by considering the power imbalance of all buses, which is expressed as: forming a vector from the power imbalance of all buses. ,in for ;in, This represents the imbalance of active power at the first bus. This represents the unbalance of reactive power at the first bus. Indicates the first The imbalance of active power of each bus. Indicates the first The unbalance of reactive power of each bus. This represents the transpose symbol.

5. The method for predicting and correcting the voltage stability domain of a high-proportion new energy system according to claim 1, characterized in that, The process of obtaining an exact stable critical point by iterating from the initial running point on the initial stable boundary according to the final Jacobian matrix includes: Starting from the initial running point on the initial stability boundary, an exact stable critical point is obtained by iterating using Newton's downhill method based on the final Jacobian matrix. The iterative formula for Newton's downhill method is specifically expressed as follows: In the formula, Indicates the first The current actual value of the system control variable at the next iteration. Indicates the first The trial value of the control variable in the next iteration; Indicates the first In the next iteration, the system is at the current actual value The criterion function value used to determine the degree of voltage stability at the operating point; express The derivative of the function at the current actual value The function value at the execution point, Indicates the downslope factor; in, ;in, This represents the final Jacobian matrix.

6. The method for predicting and correcting the voltage stability domain of a high-proportion new energy system according to claim 1, characterized in that, The process involves determining several expansion reference points using precise stable critical points; obtaining a local quadratic approximation expression for each expansion reference point through the initial quadratic approximation expression acquisition process; and obtaining several approximate stable critical points using these local quadratic approximation expressions. Starting from these approximate stable critical points, the process iterates based on the final Jacobian matrix to obtain several precise stable critical points, forming a set of precise stable critical points, including: Several stable search directions are determined, and the precise stable critical point is moved by a preset step length in the opposite direction of the several stable search directions to obtain several expansion reference points; according to the process of obtaining the initial quadratic approximation expression, the local quadratic approximation expression of each expansion reference point is obtained. Based on the stable search direction and preset step size of each expanded reference point, obtain the linear expression of the control variable corresponding to each expanded reference point; solve the system of linear expression of the control variable corresponding to each expanded reference point and the local quadratic approximation expression of each expanded reference point to obtain the approximate stable critical point corresponding to each expanded reference point; similarly, obtain the approximate stable critical points corresponding to all expanded reference points; thus, obtain several approximate stable critical points. Starting from each approximately stable critical point, an exact stable critical point is obtained by iterating using Newton's downhill method based on the final Jacobian matrix; similarly, several exact stable critical points are obtained; thus, a set of exact stable critical points is obtained. The iterative formula for Newton's downhill method is specifically expressed as follows: In the formula, Indicates the first The current actual value of the system control variable at the next iteration. Indicates the first The trial value of the control variable in the next iteration; Indicates the first In the next iteration, the system is at the current actual value The criterion function value used to determine the degree of voltage stability at the operating point; express The derivative of the function at the current actual value The function value at the execution point, Indicates the downslope factor; in, ;in, This represents the final Jacobian matrix.

7. The method for predicting and correcting the voltage stability domain of a high-proportion new energy system according to claim 1, characterized in that, The method of fitting a set of exact stable critical points using the trust domain least squares method to obtain the globally optimal quadratic expression includes: The global objective function is defined by a quadratic approximation expression for each precise stable critical point; a trust domain subproblem is established; the global objective function and the trust domain subproblem are iterated repeatedly until the error value of the global objective function is less than a preset second threshold, and finally the optimized coefficient vector is obtained, which includes the optimal first-order coefficient and the optimal quadratic coefficient; the globally optimal quadratic expression is obtained through the optimal first-order coefficient and the optimal quadratic coefficient. The global objective function is specifically expressed as follows: In the formula, Indicates the first At the precise stable critical point, the first... The values ​​of the control variables, Indicates the first At the precise stable critical point, the first... The values ​​of the control variables, Indicates the first The coefficients of the linear terms of the control variables. Indicates the first The first control variable and the first The coefficients of the quadratic terms of the control variables, This indicates the number of all control variables. This represents the number of all precisely stable critical points. Represents the coefficient vector. This represents the error value of the global objective function. These represent the coefficients of the first and second terms when the error value of the global objective function is minimized. The nonlinear least squares problem is transformed into a trust domain subproblem; the trust domain subproblem is established, and its standard form is as follows: In the formula, This indicates updating the step size vector. Indicates updating the step size vector transpose, This represents the gradient vector (i.e., the first derivative of the global objective function at the current iteration point). This represents the Hessian matrix (i.e., the second derivative matrix of the global objective function at the current iteration point). Represents the scaling matrix. Indicates the radius of the trust domain. Represents the L2 norm, This represents the function that takes the minimum value. This represents the update step size vector corresponding to the minimum value. , This indicates a constraint or a condition that satisfies the following conditions; in, ; where, scaling matrix Through the Hessian matrix It is constructed in this way.

8. A voltage stability domain prediction and correction system for a high-proportion renewable energy system, characterized in that, include: Initial analysis module: used to obtain the critical condition function for voltage stability and determine a stable operating point close to the stability boundary; The critical condition function is subjected to a Taylor expansion at the stable operating point to obtain the general form of the quadratic expression that ignores higher-order terms; The initial coefficients in the general form of the quadratic expression are obtained by calculating the first and second partial derivatives of the critical condition function with respect to the control variables and comparing them with the coefficients of the general form of the quadratic expression. Substituting the initial coefficients into the general form of the quadratic expression yields the initial quadratic approximation expression; Based on the nodal voltage equations and injected power imbalance, an accurate AC power flow model of the power system is established, and the final Jacobian matrix is ​​obtained. The precise stability critical point search module is used to select a point on the initial stability boundary from the set of all feasible and stable operating points of the power system, denoted as the initial operating point; starting from the initial operating point on the initial stability boundary, it iterates according to the final Jacobian matrix to obtain a precise stability critical point; several expansion reference points are determined through the precise stability critical point; through the process of obtaining the initial quadratic approximation expression, a local quadratic approximation expression is obtained for each expansion reference point, and several approximate stability critical points are obtained through the local quadratic approximation expression; starting from several approximate stability critical points, iterates according to the final Jacobian matrix to obtain several precise stability critical points, and forms a set of precise stability critical points. The global optimal fitting module is used to fit a set of precise stable critical points using the trust domain least squares method to obtain the globally optimal quadratic expression; the voltage stability domain prediction and correction of the new energy system is realized through the globally optimal quadratic expression.

9. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the voltage stability domain prediction and correction method for a high-proportion new energy system as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the voltage stability domain prediction and correction method for a high-proportion new energy system as described in any one of claims 1-7.

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