Harmonic impedance determination method and device, computer equipment and storage medium

Abstract

The invention relates to a harmonic impedance determination method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining harmonic data of a plurality of common connection points in a power system, and performing clustering analysis on the plurality of harmonic data based on a density clustering algorithm to determine a multi-cluster data group; establishing a Norton equivalent current model corresponding to the power system, and establishing a complex independent component method mathematical model according to the Norton equivalent current model; aiming at each cluster of data groups, determining a plurality of initial harmonic currents of the system side according to the data groups and the complex independent component method mathematical model; and sparse representation is carried out on the multiple initial harmonic currents, and the harmonic impedance of the system side is determined according to the initial harmonic currents after sparse representation and the complex independent component method mathematical model. According to the harmonic impedance determination method provided by the invention, the accuracy of the determined harmonic impedance of the system side can be improved.

Description

Technical Field

[0001] The present application relates to the field of power systems, and in particular to a method, apparatus, computer equipment, and storage medium for determining harmonic impedance. Background Art

[0002] In modern power systems, with the integration of new energy sources and a large number of power electronic devices, the frequency and content of harmonics in the power system have increased, leading to an increasingly serious problem of harmonic pollution. Harmonic pollution distorts the voltage and current waveforms of the power grid, causes insulation degradation in electronic equipment, leads to varying degrees of losses for users, and even jeopardizes the safe and stable operation of the power grid. Therefore, harmonic control is an urgent issue that needs to be addressed in the power system.

[0003] Quantifying harmonic contributions on both the system and user sides at the point of common connection is both a prerequisite and a challenge for harmonic management. The key lies in accurately determining the harmonic impedance on the system side. Traditionally, independent component analysis (ICA) has been used to estimate the harmonic impedance on the system side.

[0004] However, the harmonic impedance on the system side determined by the independent component analysis-based method is not accurate. Summary of the Invention

[0005] Based on this, it is necessary to provide a harmonic impedance determination method, apparatus, computer equipment and storage medium that can provide the accuracy of the harmonic impedance determined on the system side in order to address the above technical problems.

[0006] In a first aspect, the present application provides a method for determining harmonic impedance, the method comprising:

[0007] Obtain harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups;

[0008] Establish the Norton equivalent circuit model corresponding to the power system, and establish the complex independent component method mathematical model based on the Norton equivalent circuit model;

[0009] For each cluster of data groups, multiple initial harmonic currents on the system side are determined based on the data group and the complex independent component method mathematical model;

[0010] A plurality of initial harmonic currents are sparsely represented, and the harmonic impedance on the system side is determined based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model.

[0011] In one embodiment, a sparse representation is performed on a plurality of initial harmonic currents, and the harmonic impedance on the system side is determined based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model, including:

[0012] Dividing the multiple initial harmonic currents into multiple groups of local harmonic currents according to a preset data length;

[0013] Based on the singular value decomposition method and each group of local harmonic currents, a sparse dictionary corresponding to each group of local harmonic currents is determined;

[0014] The harmonic impedance on the system side is determined based on the sparse dictionary corresponding to each group of local harmonic currents and the complex independent component method mathematical model.

[0015] In one embodiment, determining the harmonic impedance on the system side according to a sparse dictionary corresponding to each group of local harmonic currents and a complex independent component method mathematical model includes:

[0016] Determine the mathematical model of each group of local harmonic currents based on the complex independent component method mathematical model;

[0017] Applying the sparse dictionary corresponding to each group of local harmonic currents to the mathematical model of the corresponding local harmonic currents to determine the initial harmonic impedance corresponding to the local harmonic currents;

[0018] The harmonic impedance on the system side is determined according to the average of the initial harmonic impedances corresponding to the multiple groups of local harmonic currents.

[0019] In one embodiment, cluster analysis is performed on multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups, including:

[0020] Get the minimum number and maximum distance;

[0021] Selecting target data from a plurality of harmonic data, and determining harmonic data and the target data that meet preset conditions in the plurality of harmonic data, excluding the target data, as a cluster of data groups; the preset conditions include that the distance between the harmonic data and the target data is less than a maximum distance, and the number of harmonic data whose distance to the target data is less than the maximum distance is greater than a minimum number;

[0022] The remaining harmonic data in the multiple harmonic data except the harmonic data in a cluster data group are determined as new multiple harmonic data, and the step of selecting target data from the multiple harmonic data and determining the harmonic data and the target data in the multiple harmonic data that meet the preset conditions as a cluster data group is returned to. This is until all the multiple harmonic data are clustered and analyzed to obtain multiple cluster data groups.

[0023] In one embodiment, the method further comprises:

[0024] The harmonic responsibility on the system side is determined based on the harmonic impedance on the system side and the harmonic data of the common connection point.

[0025] In one embodiment, determining the harmonic responsibility of the system side based on the harmonic impedance of the system side and the harmonic data of the common connection point includes:

[0026] Determine the harmonic voltage on the system side based on the harmonic impedance on the system side and the harmonic data of the common connection point;

[0027] The harmonic responsibility on the system side is determined based on the harmonic voltage on the system side and the harmonic voltage at the common connection point.

[0028] In a second aspect, an embodiment of the present application provides a harmonic impedance determination device, the device comprising:

[0029] A clustering module is used to obtain harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups;

[0030] Establish a module for establishing a Norton equivalent circuit model corresponding to the power system, and establish a complex independent component method mathematical model based on the Norton equivalent circuit model;

[0031] A first determination module is configured to determine, for each cluster of data groups, a plurality of initial harmonic currents on the system side according to the data group and a complex independent component method mathematical model;

[0032] The second determination module is used to perform sparse representation on multiple initial harmonic currents and determine the harmonic impedance on the system side based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model.

[0033] In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method provided in the first aspect are implemented.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method provided in the first aspect above when the computer program is executed by a processor.

[0035] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising a computer program, which implements the steps of the method provided in the first aspect above when executed by a processor.

[0036] The above-mentioned harmonic impedance determination method, apparatus, computer device, and storage medium obtain harmonic data from multiple common connection points in a power system and perform cluster analysis on the harmonic data based on a density clustering algorithm to determine multiple cluster data groups; establish a Norton equivalent current model corresponding to the power system, and establish a complex independent component method mathematical model based on the Norton equivalent current model; for each cluster data group, determine multiple initial harmonic currents on the system side based on the data group and the complex independent component method mathematical model; sparsely represent the multiple initial harmonic currents, and determine the harmonic impedance on the system side based on the sparsely represented initial harmonic data and the complex independent component method mathematical model. In this embodiment, clustering the harmonic data based on the density clustering algorithm to determine multiple cluster data groups can reduce errors in the determined harmonic impedance caused by changes in the system-side harmonic impedance. Furthermore, determining the system-side harmonic impedance using the sparsely represented data and the complex independent component method mathematical model can improve the accuracy of the determined system-side harmonic impedance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic structural diagram of a computer device in one embodiment;

[0038] Figure 2 1. A schematic flow chart of the steps of a method for determining harmonic impedance in one embodiment;

[0039] Figure 3 A schematic flow chart of the steps of a method for determining harmonic impedance in another embodiment;

[0040] Figure 4 is a schematic flow chart of steps of a method for determining harmonic impedance in another embodiment;

[0041] Figure 5 is a schematic flow chart of steps of a method for determining harmonic impedance in another embodiment;

[0042] Figure 6 is a schematic flow chart of steps of a method for determining harmonic impedance in another embodiment;

[0043] Figure 7 is a schematic flow chart of steps of a method for determining harmonic impedance in another embodiment;

[0044] Figure 8 is a schematic diagram of a multi-cluster data set in one embodiment;

[0045] Figure 9 FIG. 1 is a schematic structural diagram of a harmonic impedance determination device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0047] The serial numbers assigned to the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0048] Before specifically introducing the technical solutions of the disclosed embodiments of this application, we will first introduce the background technology or technological evolution context underlying the embodiments of this application. In modern power systems, with the integration of new energy sources and a large number of power electronic devices, the frequency and content of harmonics in power systems have increased, leading to an increasingly serious problem of harmonic pollution in power systems. Harmonic pollution distorts the voltage and current waveforms of the power grid, causes insulation degradation in electronic equipment, leads to varying degrees of losses for users, and even endangers the safe and stable operation of the power grid. Therefore, harmonic control is an urgent issue that needs to be addressed in power systems. Quantifying the harmonic contribution of both the system and user sides at the point of common connection in the power system is both a prerequisite and a challenge for harmonic control. The key lies in accurately determining the harmonic impedance on the system side. Traditionally, the system-side harmonic impedance estimation method is typically based on independent component analysis (ICA). This method primarily uses the harmonic currents and voltages measured at the point of common connection as observation signals, and the harmonic currents on the user and system sides as source signals. The source signals and mixing matrices are reconstructed from the observation signals to estimate the harmonic impedance on the system side. However, conventional techniques treat the system-side harmonic impedance as a constant value. However, changes in the power system's operating mode and network topology can cause the system-side harmonic impedance to change. Furthermore, the power system's background harmonic voltage may also experience significant fluctuations. To address this issue, the present application provides a method for determining harmonic impedance.

[0049] The harmonic impedance determination method provided in the embodiment of the present application can be applied to Figure 1The computer device shown in FIG. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication. Wireless communication can be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for determining harmonic impedance. The display screen of the computer device can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch screen covering the display screen, buttons, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0050] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0051] The following describes in detail the technical solution of the present application and how the technical solution of the present application solves the technical problem with specific embodiments.

[0052] In one embodiment, Figure 2 As shown, a method for determining harmonic impedance is provided. This embodiment uses the method applied to a computer device as an example. In this embodiment, the method includes the following steps:

[0053] Step 200: Acquire harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups.

[0054] A point of common connection (PCC) in a power system is the physical connection point between the power system and the user. There are multiple PCCs in a power system. Harmonic data for each PCC includes the harmonic current and harmonic voltage at the PCC. Harmonic data for multiple PCCs in a power system can be pre-calculated or measured and stored in a computer.

[0055] After acquiring harmonic data from multiple points of common connection in the power system, the computer device performs cluster analysis on the harmonic data from the multiple points of common connection based on a density clustering algorithm. The computer device can then group the harmonic data from the multiple points of common connection into multiple clusters, where the multiple points of common connection in each cluster have the same system-side harmonic impedance value. This embodiment does not limit the specific method for performing cluster analysis on the harmonic data from the multiple points of common connection based on the density clustering algorithm, as long as the functionality is achieved.

[0056] Step 210: Establish a Norton equivalent circuit model corresponding to the power system, and establish a complex independent component method mathematical model based on the Norton equivalent circuit model.

[0057] The core idea of ​​the Norton equivalent circuit model is to equate a source-containing linear network to a circuit consisting of a current source and an impedance in parallel. For the power system, on the system side, it can be equivalent to a current source on the system side and a resistor on the system side. On the user side, it can also be equivalent to a current source on the user side and a resistor on the user side. The Norton equivalent circuit model corresponding to the power system is as follows: Figure 3 shown. Figure 3 The current source and resistor on the left are on the system side, and the current source and resistor on the right are on the user side. The common connection point is between the system side and the user side. The relationship between the harmonic voltage and harmonic current at the common connection point in the power system, as well as the harmonic current and harmonic impedance on the user side and the harmonic current and harmonic impedance on the system side can be expressed as: ,in, represents the harmonic impedance on the system side, Indicates the harmonic impedance on the user side, represents the harmonic voltage at the common connection point, represents the harmonic current at the common connection point, Indicates the harmonic current on the system side, Indicates the harmonic current on the user side.

[0058] After determining the Norton equivalent circuit model, the computer device establishes a complex independent component method mathematical model based on the Norton equivalent circuit model. The complex independent component method mathematical model can be expressed as: , where X can be expressed as , A can be expressed as , S can be expressed as .

[0059] Step 220: For each cluster of data groups, determine multiple initial harmonic currents on the system side based on the data group and the complex independent component method mathematical model.

[0060] For each cluster of data groups, the computer device substitutes each harmonic data in the data group into the complex independent component method mathematical model, and the initial harmonic current on the system side corresponding to each harmonic data can be obtained, that is, multiple initial harmonic currents on the system side. The number of initial harmonic currents on the system side is the same as the number of harmonic data in the data group. In other words, the number of harmonic currents of the common connection points included in the data group is the same as the number of initial harmonic currents on the system side. When each harmonic data in the data group is substituted into the complex independent component method mathematical model, multiple initial harmonic currents on the user side can be obtained at the same time. An initial harmonic current on the system side and an initial harmonic current on the user side can be expressed as: , H represents the conjugate transpose.

[0061] In an optional embodiment, the computer device may perform pre-processing by centralizing and whitening the harmonic data in the data set before substituting the harmonic data in the data set into the complex independent component method mathematical model. Centralizing and whitening the harmonic data in the data set can reduce the impact of bias on the harmonic data, thereby improving the accuracy of the initial harmonic current on the system side subsequently determined based on the harmonic data.

[0062] Step 230: Perform sparse representation on the multiple initial harmonic currents, and determine the harmonic impedance on the system side based on the sparsely represented initial harmonic currents and the complex independent component method mathematical model.

[0063] After obtaining multiple system-side initial harmonic currents, the computer device sparsely represents the multiple initial harmonic currents to obtain the sparsely represented initial harmonic currents. After obtaining the sparsely represented initial harmonic currents, the computer device determines the system-side harmonic impedance based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model. The system-side harmonic impedance can be determined for each cluster of data, thereby obtaining multiple system-side harmonic impedances.

[0064] The harmonic impedance determination method provided in an embodiment of the present application obtains harmonic data from multiple common connection points in a power system and performs cluster analysis on the harmonic data based on a density clustering algorithm to determine multiple cluster data groups; establishes a Norton equivalent current model corresponding to the power system, and establishes a complex independent component method mathematical model based on the Norton equivalent current model; for each cluster data group, determines multiple initial harmonic currents on the system side based on the data group and the complex independent component method mathematical model; sparsely represents the multiple initial harmonic currents, and determines the harmonic impedance on the system side based on the sparsely represented initial harmonic currents and the complex independent component method mathematical model. In this embodiment, by performing cluster analysis on the harmonic data based on a density clustering algorithm to determine multiple cluster data groups, the error in the determined harmonic impedance caused by changes in the harmonic impedance on the system side can be reduced. Furthermore, determining the harmonic impedance on the system side using the sparsely represented data and the complex independent component method mathematical model can improve the accuracy of the determined harmonic impedance on the system side.

[0065] In one embodiment, Figure 4 As shown, a method for implementing sparse representation of multiple initial harmonic currents and determining the harmonic impedance on the system side based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model is provided. The steps of the implementation method include:

[0066] Step 400: Divide a plurality of initial harmonic currents into a plurality of groups of local harmonic currents according to a preset data length.

[0067] The preset data length may be pre-set by the staff based on actual experience. After obtaining the initial harmonic currents from the system side, the computer device divides the multiple initial harmonic currents into multiple groups of local harmonic currents according to the preset data length.

[0068] In an optional embodiment, assuming that the number of obtained initial harmonic currents is 6 and the preset data length is 3, the 6 initial harmonic currents can be divided into 2 groups of local harmonic currents according to the preset data length, each group of local harmonic currents includes three harmonic currents. assuming that the number of obtained initial harmonic currents is 7 and the preset data length is 3, the 7 initial harmonic currents can be divided into 2 groups of local harmonic currents according to the preset data length, each group of local harmonic currents includes three harmonic currents, and the remaining initial harmonic current is discarded.

[0069] Step 410 : Determine a sparse dictionary corresponding to each group of local harmonics based on a singular value decomposition method and each group of local harmonic currents.

[0070] After determining multiple groups of local harmonic currents, the computer device uses a singular value decomposition method and the local harmonic currents for each group of local harmonic currents to determine a sparse dictionary and a sparse source signal corresponding to the local harmonic current.

[0071] The relationship between the sparse dictionary corresponding to the local harmonic current and the local harmonic current can be expressed as: ,in, represents the local harmonic current of group k, represents the sparse dictionary corresponding to the local harmonic current of group k, Represents the sparse source signal corresponding to the local harmonic current of the kth group.

[0072] In an optional embodiment, before determining the sparse dictionary corresponding to the local harmonic current, the sparse dictionary is randomly initialized. The dimension of the initialized sparse dictionary is the same as the number of harmonic currents in the local harmonic current. Assume that a group of local harmonic currents includes 3 harmonic currents, and the dimension of the initial sparse dictionary is .

[0073] Step 420: Determine the harmonic impedance on the system side according to the sparse dictionary corresponding to each group of local harmonic currents and the complex independent component method mathematical model.

[0074] After determining the sparse dictionary corresponding to each group of local harmonic currents, the computer equipment can determine the harmonic barriers corresponding to each group of local harmonic currents based on the sparse dictionary and the complex independent component method mathematical model, and can determine the harmonic impedance on the system side based on the harmonic impedances corresponding to all local harmonic currents.

[0075] In this embodiment, multiple initial harmonic currents are divided into multiple groups of local harmonic currents according to a preset data length. A sparse dictionary corresponding to each group of local harmonic currents is determined based on the singular value decomposition method and the local harmonic currents. The system-side harmonic impedance is determined based on the sparse dictionary corresponding to each group of local harmonic currents and the complex independent component method model. This sparse representation of multiple initial harmonic currents according to the preset data length allows data that matches the system-side harmonic current signal to be screened out, thereby improving the accuracy of the determined system-side harmonic impedance.

[0076] In one embodiment, Figure 5 As shown, an implementation method for determining the harmonic impedance on the system side based on a sparse dictionary corresponding to each group of local harmonic currents and a complex independent component method mathematical model is provided. The steps of the implementation method include:

[0077] Step 500: Determine a mathematical model of each group of local harmonic currents according to a complex independent component method mathematical model.

[0078] After determining each group of local harmonic currents, the computer device can determine the mathematical model of each group of local harmonic currents according to the complex independent component method mathematical model.

[0079] The mathematical model corresponding to the kth group of local harmonic currents can be expressed as: , the formula can be expanded to .

[0080] Step 510: Apply the sparse dictionary corresponding to each group of local harmonic currents to the mathematical model of the corresponding local harmonic currents to determine the initial harmonic impedance corresponding to the local harmonic currents.

[0081] The sparse source signal obtained by singular value decomposition can be expressed as In the mathematical model of local harmonic current, both the left and right sides of the equation are multiplied by , then the mathematical model of local harmonic current can be converted into ,in, From the formula, we can see that sparse representation does not change A. and Match, Can be Sparse representation, the Y cluster is a straight line passing through the origin, and the slope value is the initial harmonic impedance corresponding to the local harmonic current, that is, by calculating and The initial harmonic impedance can be obtained by calculating the ratio of

[0082] That is, after determining the sparse dictionary corresponding to each group of local harmonic currents, the computer device inverts the sparse dictionary and applies it to the mathematical model of the local harmonic currents to determine the initial harmonic impedance corresponding to the local harmonic currents.

[0083] Step 520: Determine the harmonic impedance on the system side according to the average of the initial harmonic impedances corresponding to the multiple groups of local harmonic currents.

[0084] After obtaining the initial harmonic impedance corresponding to each group of local harmonic currents, the computer device calculates the average of the initial harmonic impedances corresponding to all groups of local harmonic currents to obtain the harmonic impedance on the system side.

[0085] In this embodiment, the mathematical model of each group of local harmonic currents is determined based on the mathematical model of the complex independent component method, and the sparse dictionary corresponding to each group of local harmonic currents is applied to the corresponding mathematical model of the local harmonic currents to determine the initial harmonic impedance corresponding to the local harmonic currents; the harmonic impedance on the system side is determined based on the average of the initial harmonic impedances corresponding to multiple groups of local harmonic currents. This method of determining the harmonic impedance on the system side is quick and easy to implement.

[0086] In one embodiment, Figure 6 As shown, it involves performing cluster analysis on multiple harmonic data based on a density clustering algorithm to determine a multi-cluster data group. The steps of the implementation method include:

[0087] Step 600: Obtain the minimum number and maximum distance.

[0088] The minimum number indicates the minimum number of harmonic data points that can be included in each data cluster. The maximum distance indicates the maximum distance between harmonic data points in each data cluster. The minimum number and maximum distance can be preset by staff based on practical experience and stored in the computer. The computer directly obtains the minimum data points and maximum distance.

[0089] Step 610: Select target data from multiple harmonic data, and determine the harmonic data and the target data that meet preset conditions in the multiple harmonic data as a cluster data group; the preset conditions include that the distance between the target data and the target data is less than the maximum distance, and the number of harmonic data whose distance between the target data and the target data is less than the maximum distance is greater than the minimum number.

[0090] The computer device arbitrarily selects a target data from a plurality of harmonic data, determines harmonic data that meets a preset condition among the harmonic data other than the target data in the plurality of harmonic data, and determines the harmonic data that meets the preset condition and the target data as a cluster of data groups. Specifically, the harmonic data of a common connection point includes the harmonic current and the harmonic current of the common connection point, and the harmonic current and the harmonic current are combined into a vector ( , ), the computer device calculates the distance (Euclidean distance) between the target data and other harmonic data except the target data in the multiple harmonic data, and determines the harmonic data whose distance to the target data is less than the maximum distance and the number of other harmonic data less than the maximum distance is greater than the minimum number as the harmonic data that meets the preset conditions.

[0091] Step 620: Determine the remaining harmonic data in the multiple harmonic data except the harmonic data in a cluster data group as a new multiple harmonic data, return to execute the step of selecting target data from the multiple harmonic data, and determine the harmonic data in the multiple harmonic data except the target data that meet the preset conditions and the target data as a cluster data group, until the multiple harmonic data are all clustered and analyzed, and multiple cluster data groups are determined.

[0092] After determining a cluster of data groups, the computer device removes the harmonic data included in the cluster of data groups from the multiple harmonic data, determines the remaining harmonic data as new multiple harmonic data, and returns to execute step 610 until the multiple harmonic data are clustered and analyzed to obtain multiple cluster data groups.

[0093] In an optional embodiment, there may be harmonic data in the plurality of harmonic data that does not belong to any cluster data group, and the harmonic data may be directly discarded.

[0094] In this embodiment, by obtaining the minimum data and maximum distance, target data is selected from multiple harmonic data sets. Harmonic data other than the target data in the multiple harmonic data sets that meet preset conditions and the target data are determined as a cluster data group; the preset conditions include that the distance from the target data is less than the maximum distance, and the number of harmonic data with a distance from the target data less than the maximum distance is greater than the minimum number; the remaining harmonic data in the multiple harmonic data sets other than the harmonic data in the cluster data group is determined as new multiple harmonic data sets, and the above steps are repeated until all the multiple harmonic data sets are clustered and analyzed, resulting in multiple cluster data groups. This method of clustering and analyzing multiple harmonic data sets can more accurately cluster the multiple harmonic data sets, thereby improving the accuracy of subsequent determination of system-side harmonic impedance based on the multiple cluster data groups.

[0095] In one embodiment, when determining the harmonic impedance of the system side, the computer device may determine the harmonic responsibility of the system side based on the harmonic impedance of the system side. In this regard, the method further includes:

[0096] The harmonic responsibility on the system side is determined based on the harmonic impedance on the system side and the harmonic data of the common connection point.

[0097] After determining the harmonic impedance on the system side, the computer device can determine the harmonic responsibility on the system side based on the harmonic impedance and the harmonic data of the common connection point, that is, the harmonic voltage at the common connection point.

[0098] In one embodiment, Figure 7 As shown, an implementation method for determining the harmonic responsibility of the system side based on the harmonic impedance of the system side and the harmonic data of the common connection point is provided, and the steps of the implementation method include:

[0099] Step 700: Determine the harmonic voltage on the system side based on the harmonic impedance on the system side and the harmonic data of the common connection point.

[0100] The harmonic data of the common connection point includes the harmonic voltage and harmonic current of the common connection point. After the computer equipment determines the harmonic impedance of the system side, it can be calculated according to the formula Determine the harmonic current on the system side. Based on the harmonic impedance and harmonic current on the system side, use the formula Determine the harmonic voltage on the system side.

[0101] Step 710: Determine the harmonic responsibility of the system side based on the harmonic voltage of the system side and the harmonic voltage of the common connection point.

[0102] After determining the harmonic voltage on the system side, the computer device can determine the harmonic liability on the system side based on the harmonic voltage on the system side and the harmonic voltage at the common connection point.

[0103] Specifically, according to the formula , determine the harmonic responsibility on the system side. Among them, It indicates the contribution of the system side to the harmonic voltage of the common connection point, that is, the harmonic responsibility of the system side. N indicates the number of harmonic currents collected on the system side. for and The angle between them.

[0104] In an optional embodiment, after determining the harmonic voltage on the system side, the computer device can determine the harmonic voltage on the user side based on the harmonic voltage on the system side and the harmonic current at the common connection point. The harmonic liability on the user side can be determined based on the harmonic voltage on the user side and the harmonic voltage at the common connection point. The harmonic voltage on the user side can be expressed as The harmonic responsibility on the user side can be expressed as: ,in, for and The angle between them.

[0105] In this embodiment, the system-side harmonic voltage is determined based on the system-side harmonic impedance and the system-side harmonic current; the system-side harmonic responsibility is determined based on the system-side harmonic voltage and the harmonic voltage at the common connection point. This method for determining the system-side harmonic responsibility is quick and easy to implement. Furthermore, the user-side harmonic responsibility can be determined based on the determined system-side harmonic responsibility. This facilitates harmonic control based on the system-side harmonic responsibility and the user-side harmonic responsibility, thereby improving the practicality of the harmonic impedance determination method.

[0106] In an optional embodiment, it is assumed that the amplitude of the harmonic current on the user side is 10A, the phase angle is -30 degrees, the amplitude of the harmonic current on the system side is 0.8 times the amplitude of the harmonic current on the user side, the initial value of the phase angle is 30 degrees, the amplitude of the harmonic current on the user side and the amplitude of the harmonic current on the system side have a sinusoidal fluctuation of 10% and a random fluctuation of plus or minus 5% relative to the initial value during the entire time period, the harmonic impedance on the user side is always (10+j20)Ω, the harmonic impedance Zs on the system side is (3+j2)Ω at time t1, changes to (5+j3)Ω at time t2, and changes to (1+j2)Ω at time t3. On this basis, the harmonic data of 1200 common connection points are randomly generated, among which the sample points corresponding to time t1 are the harmonic data of common connection points 1-400, the sample points corresponding to time t2 are the harmonic data of common connection points 401-800, and the sample points corresponding to time t3 are the harmonic data of common connection points 801-1200. Figure 8 As shown, based on the density clustering algorithm, the harmonic data of multiple common connection points can be divided into three clusters of data groups, namely data group one, data group two and data group three.

[0107] According to the three data sets, the amplitude and phase angle errors of the harmonic impedance on the system side calculated based on the wave quantity method (method 1), the least squares method (method 2), the complex independent component analysis method (method 3) and the method provided in the embodiment of the present application (method 4) are shown in the following table:

[0108]

[0109] It can be seen from the above table that the errors in the amplitude and phase angle of the harmonic impedance determined by the method provided in the embodiment of the present application are small.

[0110] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0111] Based on the same inventive concept, embodiments of the present application also provide a harmonic impedance determination device for implementing the aforementioned harmonic impedance determination method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the harmonic impedance determination device provided below can be found in the above-described limitations of the harmonic impedance determination method and are not further elaborated here.

[0112] In one embodiment, Figure 9 As shown, a harmonic impedance determination device is provided, comprising: a clustering module 11, an establishment module 12, a first determination module 13 and a second determination module 14, wherein:

[0113] A clustering module 11 is used to obtain harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups;

[0114] Establishing module 12, for establishing a Norton equivalent circuit model corresponding to the power system, and establishing a complex independent component method mathematical model based on the Norton equivalent circuit model;

[0115] A first determination module 13 is configured to determine, for each cluster of data groups, a plurality of initial harmonic currents on the system side according to the data group and a complex independent component method mathematical model;

[0116] The second determination module 14 is configured to perform sparse representation on the multiple initial harmonic currents and determine the harmonic impedance on the system side based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model.

[0117] In one embodiment, the second determination module 14 includes a division unit, a first determination unit, and a second determination unit. The division unit is configured to divide the multiple initial harmonic currents into multiple groups of local harmonic currents according to a preset data length; the first determination unit is configured to determine a sparse dictionary corresponding to each group of local harmonic currents based on a singular value decomposition method and each group of local harmonic currents; and the second determination unit is configured to determine the harmonic impedance on the system side based on the sparse dictionary corresponding to each group of local harmonic currents and a complex independent component method mathematical model.

[0118] In one embodiment, the second determination unit is specifically used to determine the mathematical model of each group of local harmonic currents based on the complex independent component method mathematical model; apply the sparse dictionary corresponding to each group of local harmonic currents to the corresponding mathematical model of the local harmonic currents to determine the initial harmonic impedance corresponding to the local harmonic current; determine the harmonic impedance on the system side based on the average of the initial harmonic impedances corresponding to multiple groups of local harmonic currents.

[0119] In one embodiment, the clustering module is specifically used to obtain the minimum number and maximum distance; select target data from multiple harmonic data, and determine the harmonic data and the target data in the multiple harmonic data that meet preset conditions except the target data as a cluster data group; the preset conditions include that the distance between the target data and the target data is less than the maximum distance, and the number of harmonic data whose distance between the target data and the target data is less than the maximum distance is greater than the minimum number; determine the remaining harmonic data in the multiple harmonic data except the harmonic data in a cluster data group as new multiple harmonic data, return to execute the step of selecting target data from multiple harmonic data, and determining the harmonic data and the target data in the multiple harmonic data that meet the preset conditions as a cluster data group, until the multiple harmonic data are all clustered and analyzed to obtain multiple cluster data groups.

[0120] In one embodiment, the harmonic impedance determination device further includes a third determination module configured to determine the harmonic responsibility of the system side based on the harmonic impedance of the system side and the harmonic data of the common connection point.

[0121] In one embodiment, the third determination module is specifically used to determine the harmonic voltage on the system side based on the harmonic impedance on the system side and the harmonic data of the common connection point; and determine the harmonic responsibility on the system side based on the harmonic voltage on the system side and the harmonic voltage of the common connection point.

[0122] Each module in the harmonic impedance determination device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0123] In one embodiment, a computer device is provided, whose internal structure diagram can be as follows: Figure 1 In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0124] Obtain harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups;

[0125] Establish the Norton equivalent circuit model corresponding to the power system, and establish the complex independent component method mathematical model based on the Norton equivalent circuit model;

[0126] For each cluster of data groups, multiple initial harmonic currents on the system side are determined based on the data group and the complex independent component method mathematical model;

[0127] A plurality of initial harmonic currents are sparsely represented, and the harmonic impedance on the system side is determined based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model.

[0128] In one embodiment, when the processor executes the computer program, it further implements the following steps: dividing multiple initial harmonic currents into multiple groups of local harmonic currents according to a preset data length; determining a sparse dictionary corresponding to each group of local harmonic currents based on a singular value decomposition method and each group of local harmonic currents; and determining the harmonic impedance on the system side based on the sparse dictionary corresponding to each group of local harmonic currents and a complex independent component method mathematical model.

[0129] In one embodiment, when the processor executes the computer program, it further implements the following steps: determining a mathematical model for each group of local harmonic currents based on a complex independent component method mathematical model; applying a sparse dictionary corresponding to each group of local harmonic currents to the corresponding mathematical model of the local harmonic currents to determine an initial harmonic impedance corresponding to the local harmonic current; and determining a harmonic impedance on the system side based on an average of the initial harmonic impedances corresponding to multiple groups of local harmonic currents.

[0130] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining a minimum number and a maximum distance; selecting target data from a plurality of harmonic data, and determining the harmonic data in the plurality of harmonic data that meet preset conditions and the target data except the target data as a cluster data group; the preset conditions include that the distance between the harmonic data and the target data is less than the maximum distance, and the number of harmonic data whose distance between the harmonic data and the target data is less than the maximum distance is greater than the minimum number; determining the remaining harmonic data in the plurality of harmonic data except the harmonic data in a cluster data group as new plurality of harmonic data, returning to execute the step of selecting target data from a plurality of harmonic data, and determining the harmonic data in the plurality of harmonic data that meet the preset conditions and the target data as a cluster data group, until the plurality of harmonic data are all clustered and analyzed to obtain a plurality of cluster data groups.

[0131] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: determining the harmonic responsibility of the system side according to the harmonic impedance of the system side and the harmonic data of the common connection point.

[0132] In one embodiment, when the processor executes the computer program, it further implements the following steps: determining the harmonic voltage on the system side based on the harmonic impedance on the system side and the harmonic data of the common connection point; and determining the harmonic responsibility on the system side based on the harmonic voltage on the system side and the harmonic voltage at the common connection point.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0134] Obtain harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups;

[0135] Establish the Norton equivalent circuit model corresponding to the power system, and establish the complex independent component method mathematical model based on the Norton equivalent circuit model;

[0136] For each cluster of data groups, multiple initial harmonic currents on the system side are determined based on the data group and the complex independent component method mathematical model;

[0137] A plurality of initial harmonic currents are sparsely represented, and the harmonic impedance on the system side is determined based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model.

[0138] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: dividing multiple initial harmonic currents into multiple groups of local harmonic currents according to a preset data length; determining a sparse dictionary corresponding to each group of local harmonic currents based on a singular value decomposition method and each group of local harmonic currents; and determining the harmonic impedance on the system side based on the sparse dictionary corresponding to each group of local harmonic currents and a complex independent component method mathematical model.

[0139] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining a mathematical model of each group of local harmonic currents based on a complex independent component method mathematical model; applying a sparse dictionary corresponding to each group of local harmonic currents to the corresponding mathematical model of the local harmonic currents to determine an initial harmonic impedance corresponding to the local harmonic current; and determining the harmonic impedance on the system side based on an average of the initial harmonic impedances corresponding to multiple groups of local harmonic currents.

[0140] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining the minimum number and the maximum distance; selecting target data from multiple harmonic data, and determining the harmonic data and the target data in the multiple harmonic data that meet preset conditions except the target data as a cluster data group; the preset conditions include that the distance between the multiple harmonic data and the target data is less than the maximum distance, and the number of harmonic data whose distance between the multiple harmonic data and the target data is less than the maximum distance is greater than the minimum number; determining the remaining harmonic data in the multiple harmonic data except the harmonic data in the cluster data group as new multiple harmonic data, returning to execute the step of selecting target data from multiple harmonic data, and determining the harmonic data and the target data in the multiple harmonic data that meet the preset conditions as a cluster data group, until the multiple harmonic data are all clustered and analyzed to obtain multiple cluster data groups.

[0141] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining the harmonic responsibility of the system side according to the harmonic impedance of the system side and the harmonic data of the common connection point.

[0142] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining the harmonic voltage on the system side based on the harmonic impedance on the system side and the harmonic data of the common connection point; and determining the harmonic responsibility on the system side based on the harmonic voltage on the system side and the harmonic voltage at the common connection point.

[0143] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0144] Obtain harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups;

[0145] Establish the Norton equivalent circuit model corresponding to the power system, and establish the complex independent component method mathematical model based on the Norton equivalent circuit model;

[0146] For each cluster of data groups, multiple initial harmonic currents on the system side are determined based on the data group and the complex independent component method mathematical model;

[0147] A plurality of initial harmonic currents are sparsely represented, and the harmonic impedance on the system side is determined based on the sparsely represented initial harmonic currents and a complex independent component method mathematical model.

[0148] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: dividing multiple initial harmonic currents into multiple groups of local harmonic currents according to a preset data length; determining a sparse dictionary corresponding to each group of local harmonic currents based on a singular value decomposition method and each group of local harmonic currents; and determining the harmonic impedance on the system side based on the sparse dictionary corresponding to each group of local harmonic currents and a complex independent component method mathematical model.

[0149] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining a mathematical model of each group of local harmonic currents based on a complex independent component method mathematical model; applying a sparse dictionary corresponding to each group of local harmonic currents to the corresponding mathematical model of the local harmonic currents to determine an initial harmonic impedance corresponding to the local harmonic current; and determining the harmonic impedance on the system side based on an average of the initial harmonic impedances corresponding to multiple groups of local harmonic currents.

[0150] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining the minimum number and the maximum distance; selecting target data from multiple harmonic data, and determining the harmonic data and the target data in the multiple harmonic data that meet preset conditions except the target data as a cluster data group; the preset conditions include that the distance between the multiple harmonic data and the target data is less than the maximum distance, and the number of harmonic data whose distance between the multiple harmonic data and the target data is less than the maximum distance is greater than the minimum number; determining the remaining harmonic data in the multiple harmonic data except the harmonic data in the cluster data group as new multiple harmonic data, returning to execute the step of selecting target data from multiple harmonic data, and determining the harmonic data and the target data in the multiple harmonic data that meet the preset conditions as a cluster data group, until the multiple harmonic data are all clustered and analyzed to obtain multiple cluster data groups.

[0151] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining the harmonic responsibility of the system side according to the harmonic impedance of the system side and the harmonic data of the common connection point.

[0152] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining the harmonic voltage on the system side based on the harmonic impedance on the system side and the harmonic data of the common connection point; and determining the harmonic responsibility on the system side based on the harmonic voltage on the system side and the harmonic voltage at the common connection point.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0154] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0155] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0156] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining harmonic impedance, characterized in that: The method comprises: Acquire harmonic data of a plurality of common connection points in the power system, and perform cluster analysis on the plurality of harmonic data based on a density clustering algorithm to determine a plurality of cluster data groups; Establishing a Norton equivalent circuit model corresponding to the power system, and establishing a complex independent component method mathematical model based on the Norton equivalent circuit model; For each cluster of the data group, determining a plurality of initial harmonic currents on the system side according to the data group and the complex independent component method mathematical model; The multiple initial harmonic currents are sparsely represented, and the harmonic impedance on the system side is determined based on the sparsely represented initial harmonic currents and the complex independent component method mathematical model.

2. The method according to claim 1, characterized in that The sparsely representing the multiple initial harmonic currents and determining the harmonic impedance of the system side according to the sparsely represented initial harmonic currents and the complex independent component method mathematical model includes: Dividing the multiple initial harmonic currents into multiple groups of local harmonic currents according to a preset data length; Determine a sparse dictionary corresponding to each group of local harmonic currents based on a singular value decomposition method and each group of local harmonic currents; The harmonic impedance on the system side is determined according to the sparse dictionary corresponding to each group of local harmonic currents and the complex independent component method mathematical model.

3. The method according to claim 2, characterized in that The determining of the harmonic impedance of the system side according to the sparse dictionary corresponding to each group of the local harmonic currents and the complex independent component method mathematical model includes: Determining a mathematical model of each group of local harmonic currents according to the complex independent component method mathematical model; Applying the sparse dictionary corresponding to each group of the local harmonic currents to the corresponding mathematical model of the local harmonic currents to determine the initial harmonic impedance corresponding to the local harmonic currents; The harmonic impedance on the system side is determined according to an average value of the initial harmonic impedances corresponding to the multiple groups of local harmonic currents.

4. The method according to claim 1, wherein The density-based clustering algorithm is used to perform cluster analysis on the plurality of harmonic data to determine multiple cluster data groups, including: Get the minimum number and maximum distance; Selecting target data from the plurality of harmonic data, and determining harmonic data that meets preset conditions in the plurality of harmonic data, excluding the target data, and the target data as a cluster data group; the preset conditions include that the distance between the harmonic data and the target data is less than the maximum distance, and the number of harmonic data that have a distance from the target data less than the maximum distance is greater than the minimum number; The remaining harmonic data in the plurality of harmonic data except the harmonic data in the cluster data group are determined as a new plurality of harmonic data, and the step of selecting target data from the plurality of harmonic data and determining the harmonic data in the plurality of harmonic data that meet preset conditions and the target data as a cluster data group is returned to, until the plurality of harmonic data are all clustered and analyzed to obtain the multi-cluster data group.

5. The method according to claim 1, wherein The method further comprises: The harmonic responsibility of the system side is determined according to the harmonic impedance of the system side and the harmonic data of the common connection point.

6. The method according to claim 5, characterized in that The determining of the harmonic responsibility of the system side according to the harmonic impedance of the system side and the harmonic data of the common connection point includes: determining the harmonic voltage on the system side according to the harmonic impedance on the system side and the harmonic data of the common connection point; The harmonic liability of the system side is determined according to the harmonic voltage of the system side and the harmonic voltage of the common connection point.

7. A harmonic impedance determination device, characterized in that: The device comprises: A clustering module is used to obtain harmonic data of multiple common connection points in the power system, and perform cluster analysis on the multiple harmonic data based on a density clustering algorithm to determine multiple cluster data groups; Establishing a module for establishing a Norton equivalent circuit model corresponding to the power system, and establishing a complex independent component method mathematical model based on the Norton equivalent circuit model; A first determining module is configured to determine, for each cluster of the data groups, a plurality of initial harmonic currents on the system side according to the data groups and the complex independent component method mathematical model; The second determination module is used to perform sparse representation on the multiple initial harmonic currents, and determine the harmonic impedance on the system side according to the sparsely represented initial harmonic currents and the complex independent component method mathematical model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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