Short-circuit parameter calculation method considering influence of power electronic equipment

Abstract

This invention discloses a short-circuit parameter calculation method considering the influence of power electronic equipment, belonging to the field of power system relay protection technology. The method includes: acquiring multi-dimensional operating data of the power system; fusing the system state vector model and the equivalent model of the power electronic equipment to obtain a hierarchical system calculation model; performing system initialization to determine the initial state and obtain the initialized system calculation model; constructing a short-circuit parameter calculation framework, performing preliminary short-circuit parameter solutions, and obtaining preliminary short-circuit parameter results; adjusting model parameters in real time through dynamic compensation and multi-objective optimization mechanisms to obtain the optimal parameter set; using an iterative algorithm to solve the short-circuit parameters, and combining multi-dimensional error evaluation to determine whether the calculation results converge; and outputting the final short-circuit parameters based on the convergence judgment result. This invention achieves high-precision, adaptive calculation of short-circuit parameters, significantly improving the accuracy and robustness of short-circuit analysis in power systems containing power electronic equipment.

Description

Technical Field

[0001] This invention relates to the field of power system relay protection technology, and in particular to a method for calculating short-circuit parameters that takes into account the influence of power electronic equipment. Background Technology

[0002] With the widespread application of power electronic equipment in power systems, traditional methods for calculating short-circuit parameters face new challenges. Existing methods are mainly based on Kirchhoff's laws and the principle of circuit superposition, failing to fully consider the nonlinear characteristics and dynamic response features of power electronic equipment. This leads to significant deviations between calculated and actual short-circuit parameters in systems containing a large number of power electronic devices. Furthermore, existing methods struggle to accurately describe the rapid dynamic response process of power electronic equipment at the moment of a fault, affecting the accuracy of short-circuit protection. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a short-circuit parameter calculation method that considers the influence of power electronic equipment. This solves the problem that existing short-circuit parameter calculation methods do not fully consider the nonlinear characteristics and rapid dynamic response features of power electronic equipment, resulting in large deviations between the calculated results and the actual values, making it difficult to accurately reflect the transient process of the fault.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for calculating short-circuit parameters considering the influence of power electronic devices, comprising:

[0007] Acquire multidimensional operational data of the power system;

[0008] Based on the preprocessed multidimensional operational data, the hierarchical system computation model is obtained by fusing the system state vector model and the equivalent model of power electronic equipment.

[0009] Based on the hierarchical system computation model, system initialization is performed to determine the initial state and obtain the initialized system computation model.

[0010] Based on the initialized system calculation model, a short-circuit parameter calculation framework is constructed, and the preliminary solution of the short-circuit parameters is performed to obtain the preliminary results of the short-circuit parameters.

[0011] Based on the preliminary results of the short-circuit parameters, the model parameters are adjusted in real time through dynamic compensation and multi-objective optimization mechanisms to obtain the optimal parameter set;

[0012] Based on the optimal parameter set, an iterative algorithm is used to solve the short-circuit parameters, and a multi-dimensional error evaluation is combined to determine whether the calculation results converge. Based on the convergence judgment result, the final short-circuit parameters are output.

[0013] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, the preprocessed multidimensional operating data includes standardization of the multidimensional operating data and elimination of outliers and high-frequency noise by combining median filtering and wavelet transform.

[0014] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, wherein obtaining the hierarchical system calculation model includes:

[0015] Based on the preprocessed multidimensional operational data, and combined with the power system topology and power electronic equipment parameters, a hierarchical model architecture is constructed, which includes an electrical characteristic layer, a control characteristic layer, and a fault characteristic layer.

[0016] Based on the hierarchical modular structure design, a system state vector model is established by integrating node voltage, branch current, power and equipment control parameters;

[0017] Based on the real-time operating state and control parameters contained in the system state vector model, and combined with the main loop characteristics and control response mechanism, an equivalent model of power electronic equipment is constructed.

[0018] By integrating the system state vector model and the equivalent model of power electronic equipment into a hierarchical model architecture, a hierarchical system computation model is obtained.

[0019] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, wherein obtaining the initialized system calculation model includes:

[0020] Based on the preprocessed multidimensional operating data and hierarchical system calculation model, the initial voltage vector, system impedance matrix and control parameters of power electronic equipment are obtained.

[0021] Based on the initial voltage vector, system impedance matrix and power electronic equipment control parameters, the initial current vector is solved by calling the system state equation;

[0022] Based on the initial current vector, initial voltage vector, system impedance matrix, and power electronic equipment control parameters, a complete system initial operating point is formed.

[0023] Based on the initial operating point of the system, state values ​​are assigned to the system computation model to obtain the initialized system computation model.

[0024] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, the preliminary results of the short-circuit parameters include:

[0025] Based on the initialized system calculation model and combined with the fault scenario configuration, the basic short-circuit impedance and dynamic correction term are extracted.

[0026] Based on the basic short-circuit impedance and dynamic correction term, combined with the dynamic equivalent model and real-time operating status of each power electronic device, the equivalent impedance of each power electronic device during the short-circuit process is calculated and the device weighting coefficient is determined.

[0027] Based on the equivalent impedance and equipment weighting coefficient of power electronic equipment, the dynamic compensation coefficient and equipment weighting coefficient are adjusted through an iterative optimization algorithm, and finally substituted into the short-circuit parameter calculation framework to obtain preliminary results of the short-circuit parameters.

[0028] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, wherein obtaining the optimal parameter set includes:

[0029] Define a multi-objective optimization function that includes multiple sub-objective functions and regularization terms;

[0030] Based on the multi-objective optimization function, parameter sensitivity analysis is conducted to determine the key optimization parameters.

[0031] Based on key optimization parameters, a dynamic compensation mechanism is designed to generate dynamic compensation quantities using real-time error signals.

[0032] Based on the dynamic compensation amount, a sliding window adaptive update strategy is adopted to iteratively update the model parameters and obtain the updated parameter vector.

[0033] Based on the updated parameter vector, the parameters are optimized and the optimal parameter set is obtained through a parallel computing architecture and a dynamic weight adjustment mechanism.

[0034] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, the final output short-circuit parameters include:

[0035] An improved Newton-Raphson iterative algorithm combined with adaptive step size control is used to solve the nonlinear short-circuit parameters of power electronic devices, generating iterative solution vectors and objective function values;

[0036] Based on the iterative solution vector and external reference values, the errors in each dimension are calculated, and a multi-dimensional error evaluation function is constructed using a weighted sum of squares method to obtain a comprehensive error index.

[0037] Based on errors in each dimension and external reference values, a relative error algorithm is used to evaluate the calculation accuracy and obtain a global accuracy value.

[0038] Compare the objective function values ​​of the current iteration with those of the previous iteration, determine whether the difference between the objective function values ​​of the current iteration and the previous iteration is less than a preset convergence threshold, and obtain convergence status information;

[0039] Based on comprehensive error index, global accuracy value and convergence status information, a result traceability mechanism is established and fed back to the parameter update and optimization steps to dynamically adjust the calculation strategy and output the final short-circuit parameters.

[0040] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, wherein: the equivalent model of the power electronic equipment Represented as:

[0041]

[0042] in, For the main circuit transfer function of the equipment, For the control system response function, It is a nonlinear characteristic function. These are the weight coefficients of the corresponding feature functions. The total number of nonlinear characteristic functions. This is the order index of the nonlinear characteristic function. For the Laplace operator.

[0043] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, wherein: the short-circuit parameter calculation framework Represented as:

[0044]

[0045] in, The basic short-circuit impedance of the system, A dynamic correction term that takes into account the impact of power electronic equipment; The dynamic compensation coefficient is based on the system state. This represents the equivalent impedance of power electronic equipment. For equipment weighting coefficients, This represents the total number of power electronic devices in the system. Number and index power electronic equipment.

[0046] As a preferred embodiment of the short-circuit parameter calculation method considering the influence of power electronic equipment described in this invention, wherein: the multi-objective optimization function Represented as:

[0047]

[0048] in, For each optimization sub-objective function, For the corresponding weighting coefficients, For regularization terms, To optimize the parameter vector, The number of sub-objective functions. Index of the sub-objective function.

[0049] The beneficial effects of this invention are as follows: This invention proposes for the first time a dynamic equivalent model of power electronic equipment with control characteristics, and describes nonlinear effects by introducing a combination of characteristic functions and weighting coefficients; it develops an adaptive dynamic compensation algorithm based on improved PID control to achieve real-time and accurate compensation of the dynamic characteristics of the equipment; and it constructs a multi-objective optimization short-circuit parameter calculation framework that comprehensively considers calculation accuracy, convergence speed and system stability. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0051] Figure 1 This is a basic flowchart illustrating a method for calculating short-circuit parameters considering the influence of power electronic devices, as provided in one embodiment of the present invention. Detailed Implementation

[0052] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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 protection scope of the present invention.

[0053] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for calculating short-circuit parameters considering the influence of power electronic equipment is provided, such as... Figure 1 As shown, it includes:

[0054] S100: Acquire multi-dimensional operational data of the power system;

[0055] In this embodiment of the invention, high-precision sensors and data acquisition devices are used to collect multi-dimensional operating data of the power system in real time, including node voltage, branch current, active power, reactive power and control parameters of power electronic equipment; the sampling frequency is not less than 10kHz to ensure that the high-frequency dynamic process of the system is captured; the data covers the normal operation and transient process of the system, providing raw input for modeling and state estimation.

[0056] S200: Based on preprocessed multidimensional operating data, a hierarchical system calculation model is obtained by fusing the system state vector model and the equivalent model of power electronic equipment.

[0057] In this embodiment of the invention, the original collected data is standardized:

[0058] Median filtering is used to eliminate pulse-type abnormal values ​​(such as sensor jumps).

[0059] Combining wavelet transform for multi-scale denoising effectively suppresses high-frequency noise and interference signals;

[0060] Output clean, smooth, and high-fidelity preprocessed data sequences.

[0061] In this embodiment of the invention, a hierarchical system computation model is obtained, including:

[0062] Based on the preprocessed multidimensional operational data, and combined with the power system topology and power electronic equipment parameters, a hierarchical model architecture is constructed, which includes an electrical characteristic layer, a control characteristic layer, and a fault characteristic layer.

[0063] Based on the hierarchical modular structure design, a system state vector model is established by integrating node voltage, branch current, power and equipment control parameters;

[0064] In this embodiment of the invention, the system state vector model is constructed based on a clearly defined hierarchical modular structure, which is divided into three logical layers: an electrical characteristic layer, a power layer, and a control characteristic layer. Voltage data of each node and current data of its connected branches are obtained from the electrical characteristic layer to form node voltage vectors and branch current vectors. Active power and reactive power corresponding to each node are extracted from the power layer. Simultaneously, operating control parameters of power electronic devices or controllable devices associated with each node, such as modulation commands, firing angles, and reference values, are collected from the control characteristic layer to form device control parameter vectors. Using a unified node number as a basis, the multidimensional parameters from the electrical characteristic layer, power layer, and control characteristic layer are matched one-to-one and structurally integrated, ensuring that the state information of each node contains a complete set of its electrical quantities, power quantities, and control quantities, thereby constructing a system state vector that comprehensively reflects the real-time operating characteristics of the system. This modeling method, through a combination of hierarchical decoupling and cross-layer association, achieves the organic integration of multi-source heterogeneous data within a unified framework, significantly improving the completeness of state representation and the accuracy of subsequent analysis.

[0065] Based on the real-time operating state and control parameters contained in the system state vector model, and combined with the main loop characteristics and control response mechanism, an equivalent model of power electronic equipment is constructed.

[0066] By integrating the system state vector model and the equivalent model of power electronic equipment into a hierarchical model architecture, a hierarchical system computation model is obtained.

[0067] In this embodiment of the invention, the specific process of integrating the system state vector model and the equivalent model of power electronic equipment into the hierarchical model architecture is as follows: Based on the system state vector model The included real-time operating information is used to construct an equivalent model of power electronic equipment that matches the current operating conditions. Specifically, from Extracting device control parameter vectors As the response function of the control system The input is used to accurately characterize the impact of control commands on the dynamic behavior of the equipment; utilizing Node voltage vector and branch current vector The data is used to determine the transfer function of the main circuit of the equipment through parameter identification methods. The key parameters of the model (such as the equivalent inductance and equivalent resistance of the main circuit, the open-loop gain of the current control loop, and the system pole locations determined by the dynamic characteristics of the phase-locked loop) are used to establish a dynamic model that reflects the characteristics of the actual electrical path; at the same time, based on Active power injected into each node and reactive power injected at each node The nonlinear operating characteristics exhibited, and the nonlinear characteristic function is dynamically calculated. Weighting coefficients This enables the fault response model to adapt to different power operating conditions. Based on this, at the electrical characteristic layer, Coupled with the system impedance matrix, a network topology model incorporating the dynamic characteristics of power electronic devices is formed; at the control characteristic layer, and Establish feedback correlations to achieve closed-loop description of the control loop in the system-level model; at the fault characteristic layer, As a dynamic correction term, it is superimposed on the basic short-circuit impedance model to accurately characterize the nonlinear response behavior of power electronic equipment during faults. Furthermore, a standardized inter-layer data exchange interface is established, and a system state vector model is used. It serves as a unified information carrier, transmitting key operational parameters (including node voltage vectors) between different layers. Branch current vector Active power injected into each node Reactive power injected at each node and device control parameter vector (etc.) to ensure that electrical, control, and fault characteristics remain consistent and synchronized in timing within a unified framework. Through the organic combination of the above data-driven modeling and multi-level coupling mechanism, a hierarchical system computation model with a clear structure, traceable parameters, and consistent response is finally constructed, which can comprehensively and accurately characterize the dynamic behavior of power grids containing a high proportion of power electronic equipment under normal operation and fault conditions.

[0068] In this embodiment of the invention, the power system topology includes node connection relationships, lines, and transformer configurations; the power electronic equipment parameters include rated power, filter parameters, and control bandwidth.

[0069] In this embodiment of the invention, the system state vector model Represented as:

[0070]

[0071] In the formula, This is the node voltage vector, which contains the voltage magnitude and phase angle information of each node in the system; This is the branch current vector, which describes the current distribution of each branch in the network; The active power injected into each node characterizes the power transmission characteristics; The reactive power injected into each node reflects the reactive power balance of the system. This is a vector of equipment control parameters, including key control quantities such as modulation ratio and firing angle; Nodes are indexed and numbered. This state model fully considers the dynamic characteristics of power electronic equipment, laying the foundation for short-circuit calculations.

[0072] In this embodiment of the invention, the equivalent model of power electronic equipment Represented as:

[0073]

[0074] in, The transfer function of the main circuit of the equipment has parameters passed through the node voltage vector. and branch current vector Identification and acquisition; The control system response function is represented by the device control parameter vector. As input; It is a nonlinear characteristic function; These are the weighting coefficients corresponding to the characteristic function, and their values ​​are based on the active power injected into each node. and reactive power injected at each node The reflected operating status is dynamically determined; This represents the total number of nonlinear characteristic functions; This is the order index of the nonlinear characteristic function. This is the Laplace operator. The model achieves a high-precision equivalent description of the complex dynamic characteristics of power electronic equipment by superimposing multiple nonlinear correction terms on the product of the linear main loop and the control response.

[0075] S300: Based on a hierarchical system computation model, perform system initialization, determine the initial state, and obtain the initialized system computation model;

[0076] In this embodiment of the invention, the initialized system computation model is obtained, including:

[0077] Based on the preprocessed multidimensional operating data and hierarchical system calculation model, the initial voltage vector, system impedance matrix and control parameters of power electronic equipment are obtained.

[0078] Based on the initial voltage vector, system impedance matrix and power electronic equipment control parameters, the initial current vector is solved by calling the system state equation;

[0079] Based on the initial current vector, initial voltage vector, system impedance matrix, and power electronic equipment control parameters, a complete system initial operating point is formed.

[0080] Based on the initial operating point of the system, state values ​​are assigned to the system computation model to obtain the initialized system computation model.

[0081] In this embodiment of the invention, the initial current vector Represented as:

[0082]

[0083] in, The initial current vector; This is the initial voltage vector; This is the initial impedance matrix of the system; These are the initial control parameters for power electronic equipment; This is the system state equation. The initialization process fully considers the initial operating state of the system, laying the foundation for subsequent calculations.

[0084] In this embodiment of the invention, the initial current vector The determination is based on the specific implementation of the system state equations: according to the node voltage data at the initial moment of the system (denoted as the initial voltage vector). The network topology forms the system's fundamental impedance matrix. This matrix only includes the impedance characteristics of traditional passive components such as lines and transformers; at the same time, it is based on the control parameters of power electronic equipment in the initial control state (denoted as...). ), calculate its corresponding equivalent impedance matrix, which reflects the dynamic electrical characteristics of the equipment to the power grid under the current control command; then, the basic impedance matrix with by The determined equivalent impedance matrices of power electronic equipment are superimposed to form a comprehensive impedance model that includes all network components (including power electronic equipment); based on this, the initial voltage vector is used... As an incentive, linear circuit equations are established, and the coefficient matrix of the equation system is solved numerically using the LU decomposition method to obtain the initial current vector of each branch or node. This process will... , and As the core input to the system state equation, it ensures that the initial conditions accurately reflect the control parameters. The impact on the impedance characteristics of power electronic equipment provides a high-fidelity starting state for subsequent transient or fault analysis.

[0085] In this embodiment of the invention, the system initial operating point Represented as:

[0086]

[0087] S400: Based on the initialized system calculation model, a short-circuit parameter calculation framework is constructed, and the preliminary solution of the short-circuit parameters is performed to obtain the preliminary results of the short-circuit parameters;

[0088] In this embodiment of the invention, preliminary results of short-circuit parameters are obtained, including:

[0089] Based on the initialized system calculation model and combined with the fault scenario configuration, the basic short-circuit impedance and dynamic correction term are extracted.

[0090] Based on the basic short-circuit impedance and dynamic correction term, combined with the dynamic equivalent model and real-time operating status of each power electronic device, the equivalent impedance of each power electronic device during the short-circuit process is calculated and the device weighting coefficient is determined.

[0091] Based on the equivalent impedance and equipment weighting coefficient of power electronic equipment, the dynamic compensation coefficient and equipment weighting coefficient are adjusted through an iterative optimization algorithm, and finally substituted into the short-circuit parameter calculation framework to obtain preliminary results of the short-circuit parameters.

[0092] In this embodiment of the invention, the electrical characteristic layer includes impedance parameters and voltage-current relationships; the control characteristic layer includes proportional-integral-derivative (PID) control and pulse width modulation (PWM); and the fault characteristic layer includes short-circuit impedance and fault current.

[0093] In this embodiment of the invention, a short-circuit parameter calculation framework is provided. Represented as:

[0094]

[0095] in, It serves as the system's fundamental short-circuit impedance, reflecting the characteristics of traditional network components; A dynamic correction term that takes into account the impact of power electronic equipment; These are dynamic compensation coefficients based on the system state; This represents the equivalent impedance of the power electronic equipment. This is the equipment weighting coefficient, reflecting the degree of its influence on short-circuit characteristics; This represents the total number of power electronic devices in the system. Indexing is used to assign numbers to power electronic equipment. This calculation method achieves accurate short-circuit parameter calculation through dynamic correction and equipment equivalence. During the calculation process, iterative optimization is used to determine each compensation coefficient, ensuring the accuracy of the calculation results.

[0096] S500: Based on the preliminary results of short-circuit parameters, the model parameters are adjusted in real time through dynamic compensation and multi-objective optimization mechanisms to obtain the optimal parameter set;

[0097] In this embodiment of the invention, the optimal parameter set is obtained, including:

[0098] Define a multi-objective optimization function that includes multiple sub-objective functions and regularization terms;

[0099] Based on the multi-objective optimization function, parameter sensitivity analysis is conducted to determine the key optimization parameters.

[0100] Based on key optimization parameters, a dynamic compensation mechanism is designed to generate dynamic compensation quantities using real-time error signals.

[0101] Based on the dynamic compensation amount, a sliding window adaptive update strategy is adopted to iteratively update the model parameters and obtain the updated parameter vector.

[0102] Based on the updated parameter vector, the parameters are optimized and the optimal parameter set is obtained through a parallel computing architecture and a dynamic weight adjustment mechanism.

[0103] In this embodiment of the invention, the dynamic compensation amount Represented as:

[0104]

[0105] in, This is the proportional coefficient, representing the rapid response capability of the control system. These are the integral coefficients used to eliminate the steady-state error of the system; These are differential coefficients, which improve the dynamic performance of the system. This is a real-time error signal that reflects the deviation between the calculation result and the actual value. These three control parameters are time variables. Through the coordinated operation of these three control parameters, precise compensation of the dynamic characteristics of power electronic equipment is achieved.

[0106] In this embodiment of the invention, the multi-objective optimization function Represented as:

[0107]

[0108] in, The various optimization sub-objective functions include short-circuit impedance error, voltage deviation, etc. These are the corresponding weighting coefficients, reflecting the importance of each sub-objective; This is a regularization term used to prevent overfitting; ξ To optimize the parameter vector, which includes model parameters and control parameters; The number of sub-objective functions; Index of the sub-objective function.

[0109] It should be noted that, in order to improve the accuracy and robustness of short-circuit parameter calculation, adaptive optimization of the calculation process was achieved by dynamically adjusting the weighting coefficients.

[0110] In this embodiment of the invention, parameter sensitivity analysis Represented as:

[0111]

[0112] in, A set of functions that describe the state of a system, including state variables such as voltage and current; These are the sensitivity coefficients for each state variable, reflecting their importance. These are partial derivatives; ξ To optimize the parameter vector; For system performance indicators; The total number of state functions; This serves as an index for the system state function. Sensitivity analysis can effectively identify key parameters affecting computational accuracy, thereby improving optimization efficiency.

[0113] In this embodiment of the invention, the sliding window adaptive update strategy is represented as follows:

[0114]

[0115] in, and These are the optimization parameter vectors for the current time step and the next time step, respectively. To optimize the increment of the parameter vector; The learning rate controls the speed at which parameters are updated. The update process uses a sliding window approach, with the window size adaptively adjusted based on the system's dynamic characteristics.

[0116] In this embodiment of the invention, the increment of the optimized parameter vector is... Represented as:

[0117]

[0118] in, It is the gain coefficient, used to match the control signal and the parameter space scale.

[0119] This invention employs an optimization strategy combining a parallel computing architecture and a dynamic weight adjustment mechanism, significantly improving the efficiency and adaptability of short-circuit parameter calculation. The parallel computing architecture involves decomposing the multi-objective optimization problem into multiple independent sub-tasks (such as optimizing weight coefficients, PID parameters, and equipment influence factors separately), and using multi-core processors or graphics processing units (GPUs) for parallel solving, greatly shortening the computation time. Simultaneously, a dynamic weight adjustment mechanism is introduced, adaptively adjusting the weight coefficients of each sub-objective based on the real-time system state (such as fault type and voltage drop degree). For example, the weight of the impedance accuracy term is automatically increased when a three-phase short circuit occurs, while the weight of the voltage deviation term is increased under voltage instability conditions. This ensures that the optimization process always focuses on the most critical performance indicators, achieving the optimal balance between computational accuracy and system response characteristics.

[0120] S600: Based on the optimal parameter set, an iterative algorithm is used to solve the short-circuit parameters, and a multi-dimensional error evaluation is combined to determine whether the calculation results converge. Based on the convergence judgment result, the final short-circuit parameters are output.

[0121] In this embodiment of the invention, the final short-circuit parameters are output, including:

[0122] An improved Newton-Raphson iterative algorithm combined with adaptive step size control is used to solve the nonlinear short-circuit parameters of power electronic devices, generating iterative solution vectors and objective function values;

[0123] Based on the iterative solution vector and external reference values, the errors in each dimension are calculated, and a multi-dimensional error evaluation function is constructed using a weighted sum of squares method to obtain a comprehensive error index.

[0124] Based on errors in each dimension and external reference values, a relative error algorithm is used to evaluate the calculation accuracy and obtain a global accuracy value.

[0125] Compare the objective function values ​​of the current iteration with those of the previous iteration, determine whether the difference between the objective function values ​​of the current iteration and the previous iteration is less than a preset convergence threshold, and obtain convergence status information;

[0126] Based on comprehensive error index, global accuracy value and convergence status information, a result traceability mechanism is established and fed back to the parameter update and optimization steps to dynamically adjust the calculation strategy and output the final short-circuit parameters.

[0127] In this embodiment of the invention, external reference values ​​are obtained through high-precision electromagnetic transient simulation software (such as PSCAD / EMTDC) or on-site fault recording devices, and are used to construct an error assessment benchmark.

[0128] In this embodiment of the invention, an improved Newton-Raphson iterative algorithm is used to numerically solve for the short-circuit parameters. This method features fast convergence speed and high accuracy, and is particularly suitable for processing nonlinear systems containing power electronic equipment. The iterative calculation formula is as follows:

[0129]

[0130] in, and They represent the first Subsequent The solution vector for the next iteration; is the Jacobian matrix, which describes the sensitivity of the objective function to the variables; It is a nonlinear objective function that includes the dynamic characteristics of the system; This is the index for the number of iterations. Adaptive step size control is introduced during the iteration process, which improves the convergence performance of the algorithm.

[0131] In this embodiment of the invention, the comprehensive error index for:

[0132]

[0133] in, These are the weighting coefficients for each error component, reflecting the importance of different error terms; For the first Error values ​​for each evaluation dimension, including voltage deviation, current deviation, and other aspects; The total number of evaluation dimensions; This serves as an index for error assessment dimensions. The assessment results guide the optimization and adjustment of algorithm parameters, ensuring continuous improvement in computational accuracy. This assessment system, through quantitative analysis, provides a reliable basis for improving computational methods. The entire assessment process employs real-time monitoring, enabling timely detection and correction of computational deviations.

[0134] In this embodiment of the invention, the global precision value for:

[0135]

[0136] in, For the first The calculation error of each sampling point reflects the deviation between the calculation result and the actual value; For the first The external reference value for each sampling point comes from measured data or high-precision simulation; This represents the total number of sampling points. This evaluation metric serves as the sampling point index. It provides a quantitative basis for computational accuracy, contributing to continuous improvement in algorithm performance. The evaluation results are directly fed back to the optimization module, forming a closed-loop optimization mechanism to ensure continuous improvement in computational accuracy. The entire evaluation process employs real-time monitoring, enabling timely detection and handling of anomalies.

[0137] To avoid all external reference values To address the issue of undefined formulas due to zero denominators when all values ​​are zero or close to zero, this invention employs a robust handling mechanism: when the sum of the absolute values ​​of all reference values ​​is less than or equal to a preset minimum positive threshold, the system automatically switches to a precision calculation method based on normalized root mean square error. This threshold is set according to the measurement accuracy and numerical calculation stability requirements of typical electrical quantities in power systems, for example, it is set to... Its magnitude is referenced to the minimum effective resolution of a conventional phasor measurement unit (PMU) or smart meter (typically within). to Within the per-unit range, and taking into account the engineering practice requirements of avoiding division by zero and rounding errors in floating-point operations, this method calculates the square of the error between the predicted value and the reference value at each sampling point, takes the square root of the average value, and obtains the normalized root mean square error. This error value is then subtracted from 1, and the result is multiplied by 100% to obtain the final global accuracy value. This method ensures that accuracy evaluation can still be performed stably and continuously under special operating conditions such as zero power, no-load, or shutdown, effectively improving the applicability and reliability of model performance evaluation.

[0138] In this embodiment of the invention, determining whether the difference between the objective function value of the current iteration and the previous iteration is less than a preset convergence threshold is expressed as follows:

[0139]

[0140] in, The objective function value; and The solution vector for two consecutive iterations; This is the preset convergence threshold.

[0141] In this embodiment of the invention, a result traceability mechanism is established to record the results of each iteration. , , , Data such as comprehensive error index and global accuracy value are fed back to the parameter update and optimization steps to form a closed-loop optimization; the calculation strategy is dynamically adjusted based on the evaluation results (such as switching algorithms, adjusting step size, and updating model parameters).

[0142] In this embodiment of the invention, when the comprehensive error index E fails to decrease for three consecutive iterations and A < 90%, a strategy switch is triggered, and the Newton method step size is adjusted. Halve the number of iterations or switch to a quasi-Newton method (such as the Quasi-Newton Optimization Algorithm (BFGS)) to improve convergence.

[0143] In this embodiment of the invention, the system collects operational data in real time, preprocesses this raw data to ensure its accuracy and usability, initializes basic parameters and builds a model framework based on the preprocessed data, providing necessary input and structural support for subsequent accurate calculations, and initializes the state vector using the model and performs preliminary calculations of short-circuit parameters based on it. This step directly depends on the accurate model established in the previous stage. The model parameters are adjusted through a parameter update and optimization process to improve calculation accuracy. This process iterates repeatedly until the accuracy judgment criteria are met, thus closely linking the process from model construction to parameter optimization, once the required accuracy is achieved, the system enters the sensitivity analysis stage to further verify the stability and reliability of the model, and determines whether to return to the parameter update and optimization step for further improvement or proceed to the final calculation accuracy evaluation based on the analysis results and convergence judgment. If all conditions are met, the final fault parameters are output. The entire process demonstrates a complete closed-loop link from data collection to fault diagnosis, with each step closely linked by interdependent operations.

[0144] Example 2, referring to Tables 1-3, is an embodiment of the present invention. This embodiment provides a method for calculating short-circuit parameters considering the influence of power electronic equipment. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through specific implementation methods and implementation effects.

[0145] The specific details of this embodiment are as follows:

[0146] The experiment used actual operating data from a provincial power grid in 2023 for verification. The test data covered typical operating scenarios in all four seasons of the year, and analyzed the dynamic characteristics of areas with dense power electronic equipment (such as new energy power plants and flexible transmission systems). A three-layer verification architecture was adopted to comprehensively evaluate the technical performance of the invention: the first layer was benchmark performance testing, comparing the core performance of different methods under standard operating conditions; the second layer was scenario adaptability testing, verifying the robustness of the algorithm under various typical operating conditions; and the third layer was system reliability testing, evaluating the algorithm's anti-interference ability under non-ideal conditions such as parameter disturbances and noise interference.

[0147] As shown in Table 1, the benchmark performance test focused on evaluating four key indicators: computational accuracy, response time, system stability, and environmental adaptability.

[0148] Table 1 Performance Comparison of Different Calculation Methods

[0149]

[0150] As can be seen from Table 1, the global accuracy of this invention is significantly better than that of traditional methods and improved algorithms; the computation time is reduced to 180ms, which is only 21.2% of that of traditional methods, meeting the real-time requirements; the convergence and dynamic adaptability both exceed 0.94, indicating that the algorithm has excellent stability and fast response capability.

[0151] The scenario adaptability test (Table 2) designed four typical scenarios: normal operation state simulates the stable operation of the system under rated conditions; fault moment test adopts a three-phase short circuit fault with a fault duration of 100ms; recovery process verifies the dynamic characteristics of the system recovering from the fault; extreme conditions include system overload (120% rated load) and large power fluctuation (±50% rated power).

[0152] Table 2 Calculation Results under Different Working Conditions

[0153]

[0154] As can be seen from Table 2, the present invention maintains high accuracy under various complex working conditions, especially during the dynamic and intense stages such as the moment of failure and the recovery process, where the accuracy advantage is more obvious, indicating that it has a stronger modeling capability for the nonlinear response of power electronic equipment.

[0155] The system reliability test (Table 3) included four types of interference conditions: In the parameter mutation test, key parameters changed by more than 50% within 5 seconds; white noise interference was used for system disturbance, reducing the signal-to-noise ratio to 15dB; model deviation was achieved by manually adjusting model parameters, with a deviation range of ±20%; and the dynamic response test used a step change input with an amplitude of 30% of the rated value. Each test item was repeated 5 times, and the average value was taken as the final result.

[0156] Table 3 System Reliability Test Results

[0157]

[0158] Experimental results show that the present invention exhibits strong robustness under various disturbances, with parameter accuracy exceeding 95%. The shortest recovery time is 65ms (system disturbance) and the longest is 110ms (model bias), indicating that it has good adaptive anti-interference capability.

[0159] 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for calculating short-circuit parameters considering the influence of power electronic equipment, characterized in that, include: Acquire multidimensional operational data of the power system; Based on the preprocessed multidimensional operational data, the hierarchical system computation model is obtained by fusing the system state vector model and the equivalent model of power electronic equipment. Based on the hierarchical system computation model, system initialization is performed to determine the initial state and obtain the initialized system computation model. Based on the initialized system calculation model, a short-circuit parameter calculation framework is constructed, and the preliminary solution of the short-circuit parameters is performed to obtain the preliminary results of the short-circuit parameters. Based on the preliminary results of the short-circuit parameters, the model parameters are adjusted in real time through dynamic compensation and multi-objective optimization mechanisms to obtain the optimal parameter set; Based on the optimal parameter set, an iterative algorithm is used to solve the short-circuit parameters, and a multi-dimensional error evaluation is combined to determine whether the calculation results converge. Based on the convergence judgment result, the final short-circuit parameters are output. The resulting hierarchical system computation model includes: Based on the preprocessed multidimensional operational data, and combined with the power system topology and power electronic equipment parameters, a hierarchical model architecture is constructed, which includes an electrical characteristic layer, a control characteristic layer, and a fault characteristic layer. Based on the hierarchical modular structure design, a system state vector model is established by integrating node voltage, branch current, power and equipment control parameters; Based on the real-time operating state and control parameters contained in the system state vector model, and combined with the main loop characteristics and control response mechanism, an equivalent model of power electronic equipment is constructed. By integrating the system state vector model and the equivalent model of power electronic equipment into a hierarchical model architecture, a hierarchical system computation model is obtained. The obtained initialized system computation model includes: Based on the preprocessed multidimensional operating data and hierarchical system calculation model, the initial voltage vector, system impedance matrix and control parameters of power electronic equipment are obtained. Based on the initial voltage vector, system impedance matrix and power electronic equipment control parameters, the initial current vector is solved by calling the system state equation; Based on the initial current vector, initial voltage vector, system impedance matrix, and power electronic equipment control parameters, a complete system initial operating point is formed. Based on the initial operating point of the system, state values ​​are assigned to the system computation model to obtain the initialized system computation model. The preliminary results of the short-circuit parameters obtained include: Based on the initialized system calculation model and combined with the fault scenario configuration, the basic short-circuit impedance and dynamic correction term are extracted. Based on the basic short-circuit impedance and dynamic correction term, combined with the dynamic equivalent model and real-time operating status of each power electronic device, the equivalent impedance of each power electronic device during the short-circuit process is calculated and the device weighting coefficient is determined. Based on the equivalent impedance and equipment weighting coefficient of power electronic equipment, the dynamic compensation coefficient and equipment weighting coefficient are adjusted through an iterative optimization algorithm, and finally substituted into the short-circuit parameter calculation framework to obtain preliminary results of the short-circuit parameters.

2. The method for calculating short-circuit parameters considering the influence of power electronic equipment as described in claim 1, characterized in that: The preprocessed multidimensional operational data includes standardization of the multidimensional operational data and elimination of outliers and high-frequency noise by combining median filtering and wavelet transform.

3. The method for calculating short-circuit parameters considering the influence of power electronic equipment as described in claim 2, characterized in that: The process of obtaining the optimal parameter set includes: Define a multi-objective optimization function that includes multiple sub-objective functions and regularization terms; Based on the multi-objective optimization function, parameter sensitivity analysis is conducted to determine the key optimization parameters. Based on key optimization parameters, a dynamic compensation mechanism is designed to generate dynamic compensation quantities using real-time error signals. Based on the dynamic compensation amount, a sliding window adaptive update strategy is adopted to iteratively update the model parameters and obtain the updated parameter vector. Based on the updated parameter vector, the parameters are optimized and the optimal parameter set is obtained through a parallel computing architecture and a dynamic weight adjustment mechanism.

4. The method for calculating short-circuit parameters considering the influence of power electronic equipment as described in claim 3, characterized in that: The final output short-circuit parameters include: An improved Newton-Raphson iterative algorithm combined with adaptive step size control is used to solve the nonlinear short-circuit parameters of power electronic devices, generating iterative solution vectors and objective function values; Based on the iterative solution vector and external reference values, the errors in each dimension are calculated, and a multi-dimensional error evaluation function is constructed using a weighted sum of squares method to obtain a comprehensive error index. Based on errors in each dimension and external reference values, a relative error algorithm is used to evaluate the calculation accuracy and obtain a global accuracy value. Compare the objective function values ​​of the current iteration with those of the previous iteration, determine whether the difference between the objective function values ​​of the current iteration and the previous iteration is less than a preset convergence threshold, and obtain convergence status information; Based on comprehensive error index, global accuracy value and convergence status information, a result traceability mechanism is established and fed back to the parameter update and optimization steps to dynamically adjust the calculation strategy and output the final short-circuit parameters.

5. The method for calculating short-circuit parameters considering the influence of power electronic equipment as described in claim 4, characterized in that: The equivalent model of the power electronic equipment Represented as: in, For the main circuit transfer function of the equipment, For the control system response function, It is a nonlinear characteristic function; These are the weight coefficients of the corresponding feature functions. This represents the total number of nonlinear characteristic functions; This is the order index of the nonlinear characteristic function. For the Laplace operator.

6. The method for calculating short-circuit parameters considering the influence of power electronic equipment as described in claim 5, characterized in that: The short-circuit parameter calculation framework Represented as: in, The basic short-circuit impedance of the system, A dynamic correction term that takes into account the impact of power electronic equipment; The dynamic compensation coefficient is based on the system state. This represents the equivalent impedance of power electronic equipment. For equipment weighting coefficients, This represents the total number of power electronic devices in the system. Number and index power electronic equipment.

7. The method for calculating short-circuit parameters considering the influence of power electronic equipment as described in claim 6, characterized in that: The multi-objective optimization function Represented as: in, For each optimization sub-objective function, For the corresponding weighting coefficients, For regularization terms, To optimize the parameter vector, The number of sub-objective functions. Index of the sub-objective function.

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