Converter station harmonic parameter identification method and device based on residual driving window length self-adaption
By introducing a residual-driven adaptive window length strategy in the identification of harmonic parameters in converter stations, and adjusting the window length using the harmonic impedance-standardized residual index, the accuracy and robustness problems of traditional methods in complex harmonic environments are solved, achieving higher identification accuracy and adaptability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA ELECTRIC POWER RESEARCH INSTITUTE CO LTD
- Filing Date
- 2025-12-03
- Publication Date
- 2026-05-08
AI Technical Summary
Traditional harmonic parameter identification methods are ill-suited to the randomness, fluctuation, and time-varying nature of harmonic states on the AC side of converter stations. Fixed window lengths can lead to limited identification accuracy or abnormal data contamination, affecting estimation accuracy.
An adaptive window length method based on residual driving is adopted. By acquiring harmonic voltage and current data within the data window length, the harmonic impedance is identified using complex domain regression equations. The window length is then adaptively adjusted based on the harmonic impedance standardized residual index to improve data utilization and eliminate abnormal data.
It improves the accuracy and robustness of harmonic parameter identification, adapts to complex and ever-changing harmonic environments, and ensures the accuracy and reliability of identification results.
Smart Images
Figure CN122000977A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of flexible DC transmission technology, specifically to a method and apparatus for identifying harmonic parameters of converter stations based on adaptive residual driving window length. Background Technology With the large-scale generation of new energy sources and the high proportion of power electronic equipment connected to the grid, the harmonic environment on the AC side of converter stations is becoming increasingly complex and severe. Harmonic pollution not only threatens the safe and stable operation of equipment within the station, but may also penetrate into power grids at all levels, causing widespread power quality problems. Therefore, accurate identification of background harmonic parameters of the power grid has become a key prerequisite for ensuring system safety and power quality.
[0002] Traditional identification methods often rely on regression analysis, using least squares algorithms to calculate background harmonic impedance based on harmonic voltage and current data within a fixed time window. However, in actual operation, the harmonic state on the AC side of the converter station exhibits significant randomness, fluctuation, and time-varying characteristics. Fixed window lengths are ill-suited to these dynamic changes: during periods of stable data, shorter windows cannot fully utilize effective information, limiting identification accuracy; while during periods of drastic data fluctuations or transient interference, fixed long windows introduce a large amount of abnormal data, making it impossible for traditional methods to effectively suppress the contamination of parameter identification results by poor-quality data, leading to increased estimation bias and decreased accuracy.
[0003] Existing technologies typically rely on manually set thresholds or fixed criteria as data filtering mechanisms, lacking the ability to perceive data quality online and to autonomously optimize window length structures. Summary of the Invention
[0004] To overcome the above-mentioned shortcomings, this invention proposes a method and device for identifying harmonic parameters of converter stations based on residual-driven adaptive window length.
[0005] Firstly, a method for identifying harmonic parameters of a converter station based on adaptive residual-driven window length is provided, the method comprising: Acquire harmonic voltage and harmonic current data of the converter station within the data window; Based on the harmonic voltage and harmonic current data of the converter station within the data window, the harmonic impedance of the converter station within the data window is identified using a pre-fitted complex domain regression equation. The data window length is adaptively adjusted based on the harmonic impedance of the converter station within the data window length.
[0006] Preferably, the initial value of the data window length is as follows:
[0007] In the above formula, N 0 is the initial value for the data window length.N max The maximum value of the data window length. N min This is the minimum value of the data window length.
[0008] Preferably, the pre-fitted complex domain regression equation is as follows:
[0009] In the above formula, y is the column vector of dependent variables, A is the matrix of parameters to be fitted, and x is the column vector of independent variables. Let be the residual matrix.
[0010] Furthermore, the dependent variable column vector is as follows:
[0011] The column vector of independent variables is as follows:
[0012] The matrix of parameters to be fitted is as follows:
[0013] In the above formula, This represents the measured harmonic voltage of the h-th converter station at the n-th sampling point within the data window length, where n is the total number of sampling points within the data window length, and T is the transpose sign. This refers to the measured value of the harmonic current of the h-th converter station at the n-th sampling point within the data window of the monitoring point. Background harmonic impedance on the AC side of the converter station, This refers to the background harmonic voltage on the AC side of the converter station.
[0014] Preferably, the data window length is adaptively adjusted based on the converter station harmonic impedance within the data window length, including: The standardized residual index of the converter station harmonic impedance is determined based on the harmonic impedance of the converter station within the data window. The data window length is adjusted based on the harmonic impedance standardization residual index of the converter station.
[0015] Furthermore, the harmonic impedance normalization residual index of the converter station is as follows:
[0016] In the above formula, μ n The standardization residual index for harmonic impedance of converter station s represents the harmonic impedance sequence value of the converter station within the data window, and s is the residual scale.
[0017] Furthermore, the residual scale is as follows:
[0018] In the above formula, Median This is a function that takes the median of a variable.
[0019] Furthermore, adjusting the data window length based on the harmonic impedance standardized residual index of the converter station includes: Adjust the data window length using the following formula:
[0020] In the above formula, To adjust the data window length, Given the current data window length, As the first constant, These are the weighting coefficients.
[0021] Furthermore, the weighting coefficients are as follows:
[0022] In the above formula, It is the second constant.
[0023] Furthermore, the first constant is 2, and the second constant is 5.
[0024] Secondly, a converter station harmonic parameter identification device based on residual-driven adaptive window length is provided, the converter station harmonic parameter identification device based on residual-driven adaptive window length includes: The acquisition module is used to acquire harmonic voltage and harmonic current data of the converter station within the data window. The analysis module is used to identify the harmonic impedance of the converter station within the data window length based on the harmonic voltage and harmonic current data of the converter station within the data window length using a pre-fitted complex domain regression equation. The data window length is adaptively adjusted based on the harmonic impedance of the converter station within the data window length.
[0025] Thirdly, a computer device is provided, comprising: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the method for identifying harmonic parameters of converter stations based on residual-driven adaptive window length is implemented.
[0026] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed, the method for identifying harmonic parameters of a converter station based on adaptive residual-driven window length is implemented.
[0027] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: This invention provides a method and apparatus for identifying harmonic parameters in converter stations based on residual-driven adaptive window length. The method includes: acquiring harmonic voltage and harmonic current data of the converter station within a data window length; identifying the harmonic impedance of the converter station within the data window length using a pre-fitted complex domain regression equation based on the harmonic voltage and harmonic current data within the data window length; wherein the data window length is adaptively adjusted based on the harmonic impedance of the converter station within the data window length. The technical solution provided by this invention improves the performance of harmonic parameter identification by introducing a window length adaptive strategy based on the fitted residual. When the fitted residual is small, the window length is automatically increased to utilize more data and improve estimation accuracy; when the residual is large, the window length is rapidly reduced to eliminate abnormal data and transient interference, thereby enhancing the robustness and adaptability of the algorithm. This is suitable for online monitoring scenarios with complex and variable harmonic environments on the AC side of converter stations, ensuring the accuracy and reliability of the identification results. Attached Figure Description
[0028] Figure 1 This is a schematic diagram of the main steps of the converter station harmonic parameter identification method based on residual-driven window length adaptation in an embodiment of the present invention. Figure 2 This is a harmonic current data diagram according to an embodiment of the present invention; Figure 3 This is a harmonic voltage data diagram according to an embodiment of the present invention. Detailed Implementation
[0029] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.
[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0031] Example 1 See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a converter station harmonic parameter identification method based on residual-driven window length adaptation, according to an embodiment of the present invention. Figure 1 As shown, the converter station harmonic parameter identification method based on residual-driven window length adaptation in this embodiment of the invention mainly includes the following steps: Step S101: Obtain harmonic voltage and harmonic current data of the converter station within the data window; Step S102: Based on the harmonic voltage and harmonic current data of the converter station within the data window, identify the harmonic impedance of the converter station within the data window using a pre-fitted complex domain regression equation. The data window length is adaptively adjusted based on the harmonic impedance of the converter station within the data window length.
[0032] In this embodiment, the initial value of the data window length is as follows:
[0033] In the above formula, N 0 is the initial value for the data window length. N max The maximum value of the data window length. N min This is the minimum value of the data window length.
[0034] In this embodiment, the pre-fitted complex domain regression equation is as follows:
[0035] In the above formula, y is the column vector of dependent variables, A is the matrix of parameters to be fitted, and x is the column vector of independent variables. Let be the residual matrix.
[0036] In one implementation, the dependent variable column vector is as follows:
[0037] The column vector of independent variables is as follows:
[0038] The matrix of parameters to be fitted is as follows:
[0039] In the above formula, This represents the measured harmonic voltage of the h-th converter station at the n-th sampling point within the data window length, where n is the total number of sampling points within the data window length, and T is the transpose sign. This refers to the measured value of the harmonic current of the h-th converter station at the n-th sampling point within the data window of the monitoring point. Background harmonic impedance on the AC side of the converter station, This refers to the background harmonic voltage on the AC side of the converter station.
[0040] In this embodiment, the data window length is adaptively adjusted based on the converter station harmonic impedance within the data window length, including: The standardized residual index of the converter station harmonic impedance is determined based on the harmonic impedance of the converter station within the data window. The data window length is adjusted based on the harmonic impedance standardization residual index of the converter station.
[0041] In one embodiment, the harmonic impedance normalization residual index of the converter station is as follows:
[0042] In the above formula, μ n The standardization residual index for harmonic impedance of converter station s represents the harmonic impedance sequence value of the converter station within the data window, and s is the residual scale.
[0043] In one implementation, the residual scale is as follows:
[0044] In the above formula, Median This is a function that takes the median of a variable.
[0045] In one implementation, adjusting the data window length based on the harmonic impedance standardized residual index of the converter station includes: Adjust the data window length using the following formula:
[0046] In the above formula, To adjust the data window length, Given the current data window length, As the first constant, These are the weighting coefficients.
[0047] In one implementation, the weighting coefficients are as follows:
[0048] In the above formula, It is the second constant.
[0049] In one implementation, the first constant is 2 and the second constant is 5.
[0050] To demonstrate the effectiveness of the proposed method, analysis was conducted using field-measured data of the third harmonic current and harmonic voltage at a specific location. The collected harmonic voltage and harmonic current data are shown below. Figure 2 and Figure 3 As shown.
[0051] The system impedance of the converter station was calculated using the proposed method and the traditional regression method. The calculation results and comparisons are shown in Table 1 below: Table 1
[0052] Example 2 Based on the same inventive concept, this invention also provides a converter station harmonic parameter identification device based on residual-driven adaptive window length, the converter station harmonic parameter identification device based on residual-driven adaptive window length includes: The acquisition module is used to acquire harmonic voltage and harmonic current data of the converter station within the data window. The analysis module is used to identify the harmonic impedance of the converter station within the data window length based on the harmonic voltage and harmonic current data of the converter station within the data window length using a pre-fitted complex domain regression equation. The data window length is adaptively adjusted based on the harmonic impedance of the converter station within the data window length.
[0053] Preferably, the initial value of the data window length is as follows:
[0054] In the above formula, N 0 is the initial value for the data window length. N max The maximum value of the data window length. N min This is the minimum value of the data window length.
[0055] Preferably, the pre-fitted complex domain regression equation is as follows:
[0056] In the above formula, y is the column vector of dependent variables, A is the matrix of parameters to be fitted, and x is the column vector of independent variables. Let be the residual matrix.
[0057] Furthermore, the dependent variable column vector is as follows:
[0058] The column vector of independent variables is as follows:
[0059] The matrix of parameters to be fitted is as follows:
[0060] In the above formula, This represents the measured harmonic voltage of the h-th converter station at the n-th sampling point within the data window length, where n is the total number of sampling points within the data window length, and T is the transpose sign. This refers to the measured value of the harmonic current of the h-th converter station at the n-th sampling point within the data window of the monitoring point. Background harmonic impedance on the AC side of the converter station, This refers to the background harmonic voltage on the AC side of the converter station.
[0061] Preferably, the data window length is adaptively adjusted based on the converter station harmonic impedance within the data window length, including: The standardized residual index of the converter station harmonic impedance is determined based on the harmonic impedance of the converter station within the data window. The data window length is adjusted based on the harmonic impedance standardization residual index of the converter station.
[0062] Furthermore, the harmonic impedance normalization residual index of the converter station is as follows:
[0063] In the above formula, μ n The standardization residual index for harmonic impedance of converter station s represents the harmonic impedance sequence value of the converter station within the data window, and s is the residual scale.
[0064] Furthermore, the residual scale is as follows:
[0065] In the above formula, Median This is a function that takes the median of a variable.
[0066] Furthermore, adjusting the data window length based on the harmonic impedance standardized residual index of the converter station includes: Adjust the data window length using the following formula:
[0067] In the above formula, To adjust the data window length, Given the current data window length, As the first constant, These are the weighting coefficients.
[0068] Furthermore, the weighting coefficients are as follows:
[0069] In the above formula, It is the second constant.
[0070] Furthermore, the first constant is 2, and the second constant is 5.
[0071] Example 3 Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby realizing the steps of the converter station harmonic parameter identification method based on residual-driven window length adaptive in the above embodiments.
[0072] Example 4 Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the converter station harmonic parameter identification method based on residual-driven window length adaptation in the above embodiments.
[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for identifying harmonic parameters of a converter station based on residual-driven adaptive window length, characterized in that, The method includes: Acquire harmonic voltage and harmonic current data of the converter station within the data window; Based on the harmonic voltage and harmonic current data of the converter station within the data window, the harmonic impedance of the converter station within the data window is identified using a pre-fitted complex domain regression equation. The data window length is adaptively adjusted based on the harmonic impedance of the converter station within the data window length.
2. The method as described in claim 1, characterized in that, The initial value for the data window length is as follows: In the above formula, N 0 is the initial value for the data window length. N max The maximum value of the data window length. N min This is the minimum value of the data window length.
3. The method as described in claim 1, characterized in that, The pre-fitted complex domain regression equation is as follows: In the above formula, y is the column vector of dependent variables, A is the matrix of parameters to be fitted, and x is the column vector of independent variables. Let be the residual matrix.
4. The method as described in claim 3, characterized in that, The dependent variable column vector is as follows: The column vector of independent variables is as follows: The matrix of parameters to be fitted is as follows: In the above formula, This represents the measured harmonic voltage of the h-th converter station at the n-th sampling point within the data window length, where n is the total number of sampling points within the data window length, and T is the transpose sign. This refers to the measured value of the harmonic current of the h-th converter station at the n-th sampling point within the data window of the monitoring point. Background harmonic impedance on the AC side of the converter station, This refers to the background harmonic voltage on the AC side of the converter station.
5. The method as described in claim 1, characterized in that, The data window length is adaptively adjusted based on the converter station harmonic impedance within the specified data window length, including: The standardized residual index of the converter station harmonic impedance is determined based on the harmonic impedance of the converter station within the data window. The data window length is adjusted based on the harmonic impedance standardization residual index of the converter station.
6. The method as described in claim 5, characterized in that, The harmonic impedance normalization residual index of the converter station is as follows: In the above formula, μ n The standardization residual index for harmonic impedance of converter station s represents the harmonic impedance sequence value of the converter station within the data window, and s is the residual scale.
7. The method as described in claim 6, characterized in that, The residual scale is as follows: In the above formula, Median This is a function that takes the median of a variable.
8. The method as described in claim 7, characterized in that, The adjustment of the data window length based on the harmonic impedance standardized residual index of the converter station includes: Adjust the data window length using the following formula: In the above formula, To adjust the data window length, Given the current data window length, As the first constant, These are the weighting coefficients.
9. The method as described in claim 8, characterized in that, The weighting coefficients are as follows: In the above formula, It is the second constant.
10. The method as described in claim 9, characterized in that, The first constant is 2, and the second constant is 5.
11. An apparatus for identifying harmonic parameters of a converter station based on the residual-driven window length adaptive method as described in any one of claims 1-10, characterized in that, The device includes: The acquisition module is used to acquire harmonic voltage and harmonic current data of the converter station within the data window. The analysis module is used to identify the harmonic impedance of the converter station within the data window length based on the harmonic voltage and harmonic current data of the converter station within the data window length using a pre-fitted complex domain regression equation. The data window length is adaptively adjusted based on the harmonic impedance of the converter station within the data window length.
12. A computer device, characterized in that, include: One or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for identifying harmonic parameters of a converter station based on residual-driven window length adaptation as described in any one of claims 1 to 10 is implemented.
13. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the converter station harmonic parameter identification method based on residual-driven window length adaptation as described in any one of claims 1 to 10.