Power distribution network current response simulation method and system based on multi-frequency coupling effect
By extracting the frequency domain admittance matrix of the distribution network and constructing a discrete state-space model, combined with an adaptive weighting strategy, the problem of neglecting harmonic coupling in existing technologies is solved, thereby improving the simulation accuracy and time-domain response of the distribution network and making it suitable for various distribution network scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2026-03-10
- Publication Date
- 2026-07-03
AI Technical Summary
Existing power distribution network modeling and simulation methods neglect the coupling effect between harmonics of different frequencies, resulting in models that cannot accurately reflect the nonlinear characteristics of the power distribution network, have large errors, and exhibit distorted time-domain response. Furthermore, there is a lack of effective time-domain verification indicators and methods.
By obtaining the frequency domain admittance matrix of the distribution network, extracting the same-frequency admittance vector and coupled admittance vector, converting them into transfer functions and constructing a discrete state-space model, and using dq transformation and adaptive weighting strategy to reconstruct the voltage signal and perform linear superposition, the real-time simulated total current response is obtained.
It improves simulation accuracy, is applicable to distribution networks containing distributed power sources and nonlinear loads, supports real-time simulation and control strategy design, and has strong versatility and accuracy.
Smart Images
Figure CN122333713A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power system modeling and simulation technology, and particularly relates to a method and system for simulating the current response of distribution networks based on multi-frequency coupling effects. Background Technology
[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.
[0003] With the widespread integration of high-proportion renewable energy sources such as wind and solar power into the power grid, the nonlinear characteristics of grid operation are becoming increasingly prominent. The coupling effect between harmonics of different frequencies has become a key factor affecting the operation of distribution networks, which may lead to problems such as increased voltage distortion and additional equipment losses, posing a potential threat to the safe and stable operation of distribution networks, and also placing higher demands on the modeling, simulation accuracy, and dynamic response speed of distribution networks.
[0004] The inventors have discovered the following shortcomings in existing power distribution network modeling and simulation methods: Traditional modeling methods are mostly based on the assumption of harmonics of the same frequency, which only considers the relationship between the same frequency components of voltage and current, and ignores the coupling effect between harmonics of different frequencies. As a result, the model cannot accurately reflect the actual nonlinear characteristics of the distribution network and has a large error.
[0005] Existing methods often employ simple linear transformations or fixed-parameter discretization when converting frequency-domain admittance models into time-domain simulation models, failing to fully consider the differences in frequency-varying characteristics of admittances at different frequencies, which can easily lead to distortion in the time-domain response.
[0006] Existing methods mostly focus on frequency domain fitting, lacking systematic verification metrics and methods for time domain waveform consistency and dynamic response tracking accuracy, thus failing to ensure the reliability of the model in real-world time domain applications. Summary of the Invention
[0007] To overcome the shortcomings of the prior art, this invention provides a method and system for simulating the current response of distribution networks based on multi-frequency coupling effects, which is a distribution network modeling and simulation scheme that can make full use of multi-frequency coupling information and pass effective time-domain verification.
[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: Firstly, a simulation method for the current response of distribution networks based on multi-frequency coupling effects is disclosed, including: Obtain the frequency domain admittance matrix of the distribution network, and extract the same-frequency admittance vector and coupled admittance vector; Each admittance vector is converted into a transfer function, and then the transfer function is converted into a discrete state-space model. The voltage input signal is preprocessed using dq transform and reconstructed into components of each specified frequency. Substitute the preprocessed voltage signal into the discrete state-space model to obtain the current response of each frequency component; The optimal weighting coefficients are solved by an adaptive weighting strategy, and the current responses are linearly superimposed to obtain the real-time simulated total current response.
[0009] As a further technical solution, obtaining the frequency domain admittance matrix of the distribution network specifically includes: Generate multiple sinusoidal injection signals; Multiple sinusoidal signals are injected into the node between the grid side and the phase-shifting transformer, and the response signals of voltage and current are collected simultaneously. Fourier analysis was performed on the acquired voltage and current signals to extract the complex values of voltage and current for each frequency component, and a frequency domain admittance matrix was constructed based on this.
[0010] As a further technical solution, based on the frequency domain admittance matrix, the main diagonal admittance vector of the frequency domain admittance matrix is extracted. Adjacent diagonal admittance vectors are two separate vectors. The main diagonal admittance vectors point to interactions between components of the same frequency, while adjacent diagonal admittance vectors represent coupling relationships between components of different frequencies.
[0011] As a further technical solution, the transfer function is transformed into a discrete state-space model, specifically: Each transfer function is converted into a continuous state-space model; The continuous state-space model is transformed into discrete state-space equations using the bilinear transformation method.
[0012] As a further technical solution, when solving for the optimal weight coefficients using an adaptive weighting strategy, the goal is to minimize the error between the response current of each frequency component and the actual current, and the optimal weight coefficients are solved using the least squares method.
[0013] As a further technical solution, the voltage input signal is preprocessed using dq transformation, which is used to reconstruct a single-phase signal into a component of a specified frequency.
[0014] Secondly, a simulation system for the current response of a distribution network based on multi-frequency coupling effects is disclosed, including: The admittance vector extraction module is configured to: obtain the frequency domain admittance matrix of the distribution network, and extract the same-frequency admittance vector and the coupled admittance vector; The discrete state-space model construction module is configured to convert each admittance vector into a transfer function, and then convert the transfer function into a discrete state-space model. The voltage input signal preprocessing module is configured to preprocess the voltage input signal using dq transformation and reconstruct it into components of each specified frequency. The total current response simulation module is configured to: substitute the preprocessed voltage signal into the discrete state-space model to obtain the current response of each frequency component; The optimal weighting coefficients are solved by an adaptive weighting strategy, and the current responses are linearly superimposed to obtain the real-time simulated total current response.
[0015] The above one or more technical solutions have the following beneficial effects: This invention's technical solution simultaneously considers the effects of both same-frequency admittance and coupling admittance, preserving multi-frequency coupling characteristics and improving real-time simulation accuracy. The acquisition method for frequency domain admittance data is not limited, and the weighting coefficients of each frequency current component can be adaptively adjusted according to the actual scenario. It is applicable to various distribution networks containing distributed power sources, multi-pulse rectifiers, nonlinear loads, and other equipment, possessing strong versatility. This invention considers the coupling effect between harmonics of different frequencies, solving the problems of time-domain response distortion and insufficient simulation accuracy in traditional models, and can provide effective support for the assessment of safe and stable operation of distribution networks and the design of control strategies.
[0016] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.
[0018] Figure 1 A flowchart of a distribution network modeling and simulation method based on multi-frequency coupling effects; Figure 2 This is a schematic diagram showing the structure of the frequency domain admittance matrix and the extraction of its diagonal vectors. Figure 3 A schematic diagram of the adaptive weighted linear superposition process of current components at different frequencies; Figure 4 This is a waveform comparison diagram of the simulated current and the measured current for each phase in the embodiment. Detailed Implementation
[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0020] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.
[0021] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0022] Example 1 This embodiment takes a distribution network with an 18-pulse rectifier as the research object and discloses a simulation method for the current response of the distribution network based on the multi-frequency coupling effect, such as... Figure 1 As shown, it includes: Obtain the frequency domain admittance matrix of the distribution network, and extract the same-frequency admittance vector and coupled admittance vector; Each admittance vector is converted into a transfer function, and then the transfer function is converted into a discrete state-space model. The voltage input signal is preprocessed using dq transform and reconstructed into components of each specified frequency. Substitute the preprocessed voltage signal into the discrete state-space model to obtain the current response of each frequency component; The optimal weighting coefficients are solved by an adaptive weighting strategy, and the current responses are linearly superimposed to obtain the real-time simulated total current response.
[0023] In one implementation example, step 1: Obtain the frequency domain admittance matrix of the distribution network and extract the same-frequency admittance vector and coupled admittance vector. Traditional modeling methods usually only consider the relationship between voltage and current at the same frequency, ignoring the mutual influence between different frequencies, and cannot accurately reflect the nonlinear characteristics of the distribution network. This step, by extracting the same-frequency admittance and coupled admittance, fully preserves the multi-frequency coupling characteristics of the distribution network, laying a data foundation for subsequent accurate simulation.
[0024] Step 1-1) uses a sinusoidal signal injection method to generate a multi-sinusoidal injection signal. The amplitude of each frequency component is distributed according to a preset ratio, including a 50Hz fundamental wave and 2 to 10 harmonics, for a total of 10 frequency components. The amplitude of each component decreases, and the signal peak value is controlled within 380V. The amplitude of each frequency component of the multi-sinusoidal injection signal decreases according to the preset value, and the signal peak value is controlled within the rated operating voltage range of the distribution network to avoid causing node voltage distortion.
[0025] Steps 1-2) Inject multiple sinusoidal signals into the node between the grid side and the phase-shifting transformer, and simultaneously collect the voltage and current response signals. Set the collection duration to 1 second and the sampling frequency to 25600Hz.
[0026] Steps 1-3) involve performing Fourier analysis on the acquired voltage and current signals to extract the complex voltage values of each frequency component. and complex values of current On this basis, build The frequency domain admittance matrix is of the form: (1) in, For the highest harmonic order, define the nth harmonic in the matrix. Line 1 The elements of the column are , for Complex number of second harmonic currents for Complex number of second harmonic voltages.
[0027] Steps 1-4), such as Figure 2 As shown, based on the frequency domain admittance matrix, the admittance vectors on the main diagonal of the matrix are extracted. Adjacent diagonal admittance vectors, the interaction between the main diagonal admittance vectors pointing to the same frequency, only taking the values in the matrix The main diagonal element is expressed as
[0028] The adjacent diagonal admittance vectors represent the coupling relationship between different frequency components, and include the upper adjacent diagonal vector and the lower adjacent diagonal vector. Taking the position closest to the main diagonal as an example, the upper adjacent diagonal vector is expressed as...
[0029] The lower adjacent diagonal vector is expressed as
[0030] Step 2: Obtain the frequency response of the transfer function. Frequency domain admittance is discrete frequency data and cannot be directly used for time domain simulation. By using a partial fraction fitting algorithm, the discrete frequency domain data can be fitted into a continuous, parameterized transfer function, thereby accurately describing the law of admittance changing with frequency. At the same time, the model accuracy can be controlled by adjusting the fitting order.
[0031] Step 2-1) employs a partial fraction fitting algorithm to fit the transfer function to each admittance vector. The core objective is to ensure that the frequency response of the transfer function matches the measured admittance data as closely as possible at the relevant frequency points. Each admittance vector corresponds to an independent transfer function, in the following form: (2) In the formula, is the index of the admittance vector. , For residuals, As the extreme point, For constant terms, The fitting order is denoted as .
[0032] Step 2-2): Solve for the transfer function parameters using the least squares method. Define the fitting objective as: minimizing the error between the transfer function's response and the measured admittance data at all frequencies of interest, i.e.
[0033] In the formula, For transfer function At frequency The response at the location, Let be the complex value of the admittance in the admittance vector; to ensure system stability, the real parts of all poles are constrained to be negative, and the final set of parameters is obtained through iterative solution. Together, they constitute the required transfer function model.
[0034] Step 3: Convert each transfer function model into state-space form. Although transfer functions are relatively intuitive in the frequency domain, they are difficult to apply directly to time-domain simulations, resulting in poor convenience. State-space models are the standard form for iterative calculations in time-domain simulations, so it is necessary to convert the transfer function models into state-space models first. In addition, computers can only process data at discrete time points, so continuous models need to be discretized.
[0035] Step 3-1), as shown in the formula in Step 2-1, for each transfer function The parallel implementation method is adopted, treating it as a parallel connection of multiple first-order subsystems and a direct term, that is...
[0036] Among them, the first-order subsystem direct item , For residuals, As the extreme point, For constant terms, The fitting order is denoted as .
[0037] For each first-order subsystem Its differential equation is
[0038] in, For the first One state variable, For the first One output variable, It is the system's total input.
[0039] For direct terms Its differential equation is
[0040] Combine the state variables of all subsystems into a state vector Then the state-space expression of the entire system is:
[0041] In the formula, For state vectors, For the input vector, This is the output vector.
[0042] More generally, for the first The transfer function is represented by its continuous state-space model as follows: (3) Wherein, the state matrix is The input matrix is The output matrix is The direct transmission matrix is .
[0043] Step 3-2) discretizes the continuous state-space model using the bilinear transformation method. This ensures that the discretized model maintains a high degree of consistency with the original continuous system in terms of dynamic response characteristics. Simultaneously, matching the simulation step size ensures the stability and accuracy of the simulation. The discrete state-space equations are: (4) in, , , , , The sampling period is defined as follows: the discretized sampling frequency is consistent with the sampling frequency during the data acquisition stage, that is, the discretized sampling frequency is consistent with that in steps 1-2.
[0044] Step 4: Perform dq transformation preprocessing on the voltage input signal used for real-time simulation.
[0045] Through the above steps, discrete state-space sub-models corresponding to each specified frequency component have been established. In order to use these sub-models for real-time simulation, the actual acquired time-domain voltage input signal needs to be decomposed into frequency components that correspond one-to-one with the sub-models.
[0046] Considering the model characteristics and real-time simulation requirements, this method uses the dq transform as a preprocessing technique. Its purpose is to reconstruct the time-domain voltage signal into voltage components at a specified frequency through the dq rotation coordinate transformation, facilitating subsequent calculations. The specific implementation is as follows: (5) in, The original discrete voltage sequence obtained from sampling. This indicates that at a rotation frequency of Voltage components at time, For rotation factor, For sampling point index, The sampling period.
[0047] Repeat the above process for all frequencies of interest to obtain the voltage input sequence for each frequency component, which serves as the input for the subsequent discrete state-space model.
[0048] Step 5: Substitute the preprocessed voltage signals of each frequency component into the corresponding discrete state-space sub-model, and perform point-by-point iteration to obtain the current response corresponding to each frequency component. The specific process is as follows: Based on step 3-2, establish the corresponding frequency. The discrete state space sub-model is
[0049] in, , , , It is a matrix in the obtained state-space description. Is the system in The state vector at each moment; setting the initial state. For time Given the current state and voltage input Substituting into the output equation, we obtain the current response. Substituting into the state equation, the next time step can be calculated. status Further calculate the next time step. Current response Repeat the above steps until the simulation ends. By iterating point by point, the current response corresponding to that frequency component over the entire time series can be obtained.
[0050] Step 6: Obtain the weights of the current components at each frequency using an adaptive weighting strategy.
[0051] Adaptive weighting is a data-driven optimization method that automatically adjusts the weights of each frequency component based on the error between simulation and measured results, so that the final synthesized waveform is as close as possible to the real situation in the time domain, thereby improving simulation accuracy. At the same time, the adaptive weighting optimization process introduces regularization to prevent overfitting and improve the model's generalization ability.
[0052] Specifically, with the goal of minimizing the error between the response current and the actual current at each frequency component, the optimal weighting coefficients are solved using the least squares method. The optimization objective is: (6) in, For the reason A matrix consisting of current response sequences of 10 frequency components, where each column corresponds to a current vector of one frequency component, with a length of 1. One sampling point; Let be the weight coefficient vector to be solved. This is the measured current data vector. This is a regularization parameter used to prevent overfitting.
[0053] The optimized weight coefficient combination obtained by least squares solution can be used for subsequent real-time simulation.
[0054] Step 7: Linearly superimpose the current components of each frequency to obtain the real-time simulated total current response.
[0055] According to the superposition principle of linear systems, if a system can be approximated as being composed of multiple mutually decoupled linear subsystems, then the total response can be regarded as the superposition of the responses produced by the individual action of each frequency component.
[0056] In the real-time simulation phase, using the optimal weighting coefficient combination obtained in step 6, the adaptive weighted linear superposition process of the current components of each frequency component for the real-time voltage input that did not participate in the weighting optimization process is as follows: Figure 3 As shown, the real-time simulated total current response after linear superposition is: (7) In the formula, This refers to the total current response, i.e., the final output result; This is the total number of frequency components considered, corresponding to the number of admittance vectors extracted in step 1; Corresponding to the The optimal weighting coefficients for each frequency component are obtained by solving the adaptive weighting strategy in step 6. Corresponding to the The current response of each frequency component is calculated in step 5.
[0057] like Figure 4 As shown, the time-domain waveforms of the simulated current and the measured current for each phase are compared.
[0058] Compared to traditional methods, the simulation model obtained by this approach takes into account both same-frequency admittance and coupled admittance, preserving multi-frequency coupling effects and achieving higher simulation accuracy. It employs an adaptive weighting strategy, with weight coefficients optimized based on measured data, enabling the model to adapt to nonlinear characteristics under different operating conditions and avoiding the limitations of fixed-parameter models. The discrete state-space model supports point-by-point iterative calculations and can be embedded into real-time simulation platforms, meeting the timeliness requirements of online monitoring or hardware-in-the-loop testing. The current response and weight coefficients of each frequency component have clear physical meanings; the former reflects the contribution of a single frequency, while the latter characterizes the actual proportion of that frequency in the overall response, facilitating the analysis of dominant harmonic sources. Subsequently, the simulated current can be used for scenarios such as distribution network operation status assessment, as well as for the design and verification of control strategies.
[0059] This embodiment employs a multi-sinusoidal signal injection method to obtain the frequency domain admittance matrix in a multi-frequency coupling scenario of a power distribution network, extracting the admittance vectors from the main diagonal and adjacent diagonals. The frequency domain admittance matrix is a complex matrix; its main diagonal elements form the same-frequency admittance vector, corresponding to the admittance characteristics of a single frequency component; the off-diagonal elements form the coupled admittance vector, corresponding to the interaction between different frequency components. A partial fraction fitting method is used to calculate the transfer function of each admittance vector. The core objective of the fitting method is to minimize the complex error between the transformed transfer function and the original admittance vector; each admittance vector corresponds to an independent transfer function that accurately reproduces the frequency domain characteristics of the corresponding admittance vector; to ensure transformation accuracy, the model transformation method supports adjusting relevant parameters to ensure the error meets a preset threshold requirement. Each transfer function is then converted into a discrete state-space model.
[0060] First, each transfer function is converted into a continuous state-space model, and then each continuous state-space sub-model is converted into a discrete state-space model. The voltage input signal is preprocessed and reconstructed into components of specified frequencies. The preprocessing method is the dq transform, used to reconstruct the signal into components of specified frequencies, thus preserving multi-frequency coupling characteristics. The preprocessed voltage input signal is substituted into each discrete state-space sub-model to obtain the current response corresponding to each model. An adaptive weighting strategy is adopted to solve for the optimal weight coefficients with the objective of minimizing the error between the current response of each frequency component and the actual current. The optimization process aims to minimize the current time-domain error and introduces regularization to avoid overfitting. The optimized weight coefficients can reasonably allocate the contribution ratio of each frequency component, making the superimposed total current response closer to the actual operating state of the distribution network. The current responses of each frequency component are linearly superimposed to obtain the real-time simulated total current response.
[0061] The simulated current response considering coupling admittance, the simulated current response considering only the admittance at the same frequency, and the measured current data were compared, and the root mean square error was calculated for each. Mean absolute error and goodness of fit The results are shown in Table 1. As can be seen from the table, compared with the model that only considers the same-frequency admittance, the real-time simulation results of the method of the present invention have a high degree of agreement with the measured data, and all indicators have been significantly improved, verifying the feasibility of the multi-frequency coupled admittance model for improving the accuracy of real-time simulation.
[0062] Table 1 Comparison of simulation results for different models
[0063] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.
[0064] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.
[0065] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.
[0066] Example 4 The purpose of this embodiment is to provide a distribution network current response simulation system based on multi-frequency coupling effects, including: The admittance vector extraction module is configured to: obtain the frequency domain admittance matrix of the distribution network, and extract the same-frequency admittance vector and the coupled admittance vector; The discrete state-space model construction module is configured to convert each admittance vector into a transfer function, and then convert the transfer function into a discrete state-space model. The voltage input signal preprocessing module is configured to preprocess the voltage input signal using dq transformation and reconstruct it into components of each specified frequency. The total current response simulation module is configured to: substitute the preprocessed voltage signal into the discrete state-space model to obtain the current response of each frequency component; The optimal weighting coefficients are solved by an adaptive weighting strategy, and the current responses are linearly superimposed to obtain the real-time simulated total current response.
[0067] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions involved in any of the above embodiments.
[0068] The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.
[0069] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.
[0070] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.
Claims
1. A simulation method for distribution network current response based on multi-frequency coupling effect, characterized by: include: Obtain the frequency domain admittance matrix of the distribution network, and extract the same-frequency admittance vector and coupled admittance vector; Each admittance vector is converted into a transfer function, and then the transfer function is converted into a discrete state-space model. The voltage input signal is preprocessed using dq transform and reconstructed into components of specified frequencies; Substitute the preprocessed voltage signal into the discrete state-space model to obtain the current response of each frequency component; The optimal weighting coefficients are solved by an adaptive weighting strategy, and the current responses are linearly superimposed to obtain the real-time simulated total current response.
2. The simulation method for distribution network current response based on multi-frequency coupling effect as described in claim 1, characterized in that, The acquisition of the frequency domain admittance matrix of the distribution network specifically includes: Generate multiple sinusoidal injection signals; Multiple sinusoidal signals are injected into the node between the grid side and the phase-shifting transformer, and the voltage and current response signals are collected simultaneously. Fourier analysis was performed on the acquired voltage and current signals to extract the complex values of voltage and current for each frequency component, and a frequency domain admittance matrix was constructed based on this.
3. The simulation method for distribution network current response based on multi-frequency coupling effect as described in claim 1, characterized in that, Based on the frequency domain admittance matrix, extract the admittance vectors from the main diagonal of the frequency domain admittance matrix. Adjacent diagonal admittance vectors are two separate vectors. The main diagonal admittance vectors point to interactions between components of the same frequency, while adjacent diagonal admittance vectors represent coupling relationships between components of different frequencies.
4. The simulation method for distribution network current response based on multi-frequency coupling effect as described in claim 1, characterized in that, The transfer function is transformed into a discrete state-space model, specifically as follows: Each transfer function is converted into a continuous state-space model; The continuous state-space model is transformed into discrete state-space equations using the bilinear transformation method.
5. The simulation method for distribution network current response based on multi-frequency coupling effect as described in claim 1, characterized in that, When solving for the optimal weighting coefficients using an adaptive weighting strategy, the goal is to minimize the error between the response current of each frequency component and the actual current, and the optimal weighting coefficients are solved using the least squares method.
6. The simulation method for distribution network current response based on multi-frequency coupling effect as described in claim 1, characterized in that, The voltage input signal is preprocessed using dq transformation, which is used to reconstruct a single-phase signal into a component of a specified frequency.
7. A simulation system for distribution network current response based on multi-frequency coupling effect, characterized in that, include: The admittance vector extraction module is configured to: obtain the frequency domain admittance matrix of the distribution network, and extract the same-frequency admittance vector and the coupled admittance vector; The discrete state-space model construction module is configured to convert each admittance vector into a transfer function, and then convert the transfer function into a discrete state-space model. The voltage input signal preprocessing module is configured to preprocess the voltage input signal using dq transformation and reconstruct it into components of each specified frequency. The total current response simulation module is configured to: substitute the preprocessed voltage signal into the discrete state-space model to obtain the current response of each frequency component; The optimal weighting coefficients are solved by an adaptive weighting strategy, and the current responses are linearly superimposed to obtain the real-time simulated total current response.
8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-6.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the method described in any one of claims 1-6 above.