Method and system for identifying dominant harmonic source

Abstract

The invention belongs to the technical field of power quality of a power distribution network, and discloses a dominant harmonic source identification method and system.The dominant harmonic source identification method comprises the steps that on the basis of power grid phase-free measurement data collected in real time, the nonlinearity of each feeder line in a power grid is judged through nonlinear analysis, and the feeder lines containing harmonic sources are screened; according to the screened feeder lines containing the harmonic sources, utilizing a correlation analysis algorithm to solve a correlation index between the harmonic voltage of the common connection point and the harmonic current of each feeder line, and calculating a nonlinearity index of each feeder line containing the harmonic sources; and performing weighted summation on the correlation indexes and the nonlinearity indexes by using an objective weighting method to obtain fusion indexes of the feeder lines, and identifying the dominant harmonic source according to the size of the fusion index of each feeder line. According to the method, harmonic wave responsibility division can be efficiently and accurately realized, the identification result is not interfered by background harmonic wave fluctuation, and the method has good stability.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality of distribution network, and in particular to a method and system for identifying a dominant harmonic source. Background Art

[0002] At present, the world is actively promoting the development of new energy to achieve multiple goals such as environmental protection, energy security, economic growth and sustainable development, and effectively respond to the challenges brought by climate change. With the widespread access of a large number of nonlinear devices such as new energy power generation equipment to the distribution system, the harmonic content in the distribution system continues to increase, and harmonic pollution is becoming more and more serious. Excessive harmonic content will not only increase the thermal load of electrical equipment, causing premature aging or failure of equipment, but also cause voltage and current waveform distortion, seriously affecting the power supply quality; therefore, in order to accurately locate the dominant harmonic source, implement effective harmonic control measures, and ensure the stable, safe and efficient operation of the power system, it is necessary to identify the dominant harmonic source.

[0003] At present, the identification of dominant harmonic sources is mainly based on the harmonic responsibility division algorithm. Harmonic responsibility division is a method to measure the contribution of harmonic responsibility on the distribution network system side and the user side at the point of common coupling (PCC). The identification of dominant harmonic sources is achieved by judging the size of harmonic responsibility on the system side and the user side. However, due to the volatility and randomness of distributed photovoltaic grid connection, the fluctuation of background harmonics will lead to inaccurate calculation results; at the same time, most of the existing harmonic responsibility division algorithms rely on the harmonic voltage and current phasor data of the grid connection point, while the harmonic monitoring devices currently configured in the low-voltage distribution network are not comprehensive in terms of data monitoring functions, and there is a problem of missing phase angle of measurement data.

[0004] Therefore, how to provide a solution in which the identification result of the dominant harmonic source is not disturbed by the background harmonic fluctuation and has good stability has become a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present invention provide a dominant harmonic source identification method and system to solve the problems in the prior art that the fluctuation of background harmonics may lead to inaccurate calculation results due to the volatility and randomness of distributed photovoltaic grid connection, and the existing harmonic responsibility division algorithms mostly rely on the harmonic voltage and current phasor data of the grid connection point. However, the harmonic monitoring devices currently configured in the low-voltage distribution network are not comprehensive in data monitoring functions and there is a problem of missing phase angle of measurement data.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a method for identifying a dominant harmonic source is provided.

[0008] In one embodiment, the dominant harmonic source identification method includes:

[0009] Based on the real-time collected non-phase measurement data of the power grid, the nonlinearity of each feeder in the power grid is determined by nonlinear analysis, and the feeders containing harmonic sources are screened;

[0010] According to the screened feeders containing harmonic sources, the correlation index between the harmonic voltage at the common connection point and the harmonic current of each feeder is solved by using the correlation analysis algorithm, and the nonlinearity index of each feeder containing harmonic sources is calculated;

[0011] The objective weighting method is used to weight the correlation index and the nonlinearity index to obtain the fusion index of the feeder. The dominant harmonic source is identified according to the size of the fusion index of each feeder.

[0012] In one embodiment, the power grid includes a busbar, a feeder and a common connection point;

[0013] The non-phase measurement data of the power grid is collected through the measurement terminal, which includes the power quality meter and the intelligent fusion terminal;

[0014] The power grid non-phase measurement data includes the non-phase measurement data of the harmonic voltage and the non-phase measurement data of the harmonic current;

[0015] The non-phase measurement data of the harmonic voltage includes the harmonic voltage amplitude and the harmonic voltage phase difference, and the non-phase measurement data of the harmonic current includes the harmonic current amplitude and the harmonic current phase difference.

[0016] In one embodiment, based on the real-time collected non-phase measurement data of the power grid, the nonlinearity of each feeder in the power grid is determined by nonlinear analysis, and the feeders containing harmonic sources are screened, including:

[0017] A linear load model is established based on the non-phase measurement data of harmonic voltage and harmonic current, and the resistance and inductance values ​​of the linear load model are calculated using the least square method, and the result matrix is ​​output;

[0018] Based on the result matrix, the model error of each feeder is calculated and compared with the preset error threshold;

[0019] Based on the comparison result of the model error of each feeder with the preset error threshold, the feeder containing the harmonic source is selected.

[0020] In one embodiment, the expression of the result matrix is:

[0021] Ax=y

[0022]

[0023] Where x is the impedance matrix; A is the coefficient matrix; y is the voltage matrix; R is the resistance value in the linear load model; L is the inductance value in the linear load model; U[nT s ]、I[nT s ] are nT respectively s The voltage and current measured values ​​at time T s is the sampling interval; ΔI and Δt are the current change and time change respectively; T is the data time window length used in the least squares calculation, and N is the relative window length ratio;

[0024] The expression of the model error of each feeder is:

[0025]

[0026] Where e is the model error of each feeder, is the impedance estimate;

[0027] The expression of the preset error threshold is:

[0028] e thr =k rel e max

[0029] In the formula, e thr is the preset error threshold, k rel is the reliability coefficient, e max Maximum error when identifying nonlinear sources for a linear load model.

[0030] In one embodiment, based on the screened feeders containing harmonic sources, a correlation analysis algorithm is used to solve the correlation index between the harmonic voltage at the common connection point and the harmonic current of each feeder, and the nonlinearity index of each feeder containing the harmonic source is calculated, including:

[0031] A first index sequence is established based on the time series data of the harmonic voltage at the common connection point, and a second index sequence is established based on the time series data of the harmonic current of each feeder;

[0032] Using the Pearson correlation algorithm, calculate the correlation index between the first indicator sequence and the second indicator sequence;

[0033] According to the fluctuation degree of the resistance and inductance of the linear load model, the nonlinearity index of each feeder containing harmonic sources is calculated.

[0034] In one embodiment, according to the fluctuation degree of the resistance value and the inductance value of the linear load model, calculating the nonlinearity index of each feeder including the harmonic source includes:

[0035] Based on the resistance and inductance values ​​of the linear load model, the average values ​​of the resistance and inductance values ​​are calculated respectively, and the fluctuation degree of the resistance and inductance values ​​is analyzed;

[0036] According to the fluctuation degree of resistance and inductance, the nonlinearity of the feeder currently containing harmonic sources is analyzed to obtain the volatility index;

[0037] The volatility index is normalized and combined with the harmonic measured power of the feeder to calculate the nonlinearity index of the feeder containing the harmonic source.

[0038] In one embodiment, the expression of the correlation index is:

[0039]

[0040] In the formula, r 1j is the correlation index between the first index sequence and the second index sequence, n is the number of sampling points, x(n) is the first index sequence established based on the time series data of the harmonic voltage at the common connection point; y j (n) is a second indicator sequence established based on the time series data of the harmonic current of each feeder; μ represents the value of the first index sequence at time μ, represents the value of the second index sequence at time μ, represents the mean of the first indicator series, represents the mean of the second indicator series;

[0041] The expression of nonlinearity index is:

[0042]

[0043] In the formula, r Pj is the nonlinearity index, r 2j is the normalized volatility index of feeder j, P j is the harmonic measured power of feeder j.

[0044] In one embodiment, an objective weighting method is used to weight the correlation index and the nonlinearity index to obtain a fusion index of the feeder, and according to the size of the fusion index of each feeder, the dominant harmonic source is identified, including:

[0045] Standardize the correlation index and nonlinearity index, solve the comparison strength of the correlation index and the nonlinearity index after standardization, and calculate the conflict between the correlation index and the nonlinearity index after standardization;

[0046] According to the contrast intensity and conflict, the information carrying capacity is calculated, and the objective weight is solved based on the information carrying capacity;

[0047] Based on the objective weight, the weighted sum of the correlation index and the nonlinearity index is used to obtain the fusion index of the feeder, and the feeder with the largest fusion index is selected as the dominant harmonic source.

[0048] According to a second aspect of an embodiment of the present invention, a dominant harmonic source identification system is provided.

[0049] In one embodiment, the dominant harmonic source identification system includes:

[0050] A nonlinear analysis unit is used to determine the nonlinearity of each feeder in the power grid and screen the feeders containing harmonic sources based on the power grid non-phase measurement data collected in real time by nonlinear analysis. The power grid non-phase measurement data includes the non-phase measurement data of harmonic voltage and the non-phase measurement data of harmonic current.

[0051] An index calculation unit is used to solve the correlation index between the harmonic voltage of the common connection point and the harmonic current of each feeder according to the screened feeders containing harmonic sources by using a correlation analysis algorithm, and calculate the nonlinearity index of each feeder containing a harmonic source;

[0052] The index fusion unit is used to obtain the fusion index of the feeder by using the objective weighting method, weighted summation of the correlation index and the nonlinearity index, and identify the dominant harmonic source according to the size of the fusion index of each feeder.

[0053] In one embodiment, the power grid includes a busbar, a feeder and a common connection point;

[0054] The non-phase measurement data of the power grid is collected through the measurement terminal, which includes the power quality meter and the intelligent fusion terminal;

[0055] The non-phase measurement data of the harmonic voltage includes the harmonic voltage amplitude and the harmonic voltage phase difference, and the non-phase measurement data of the harmonic current includes the harmonic current amplitude and the harmonic current phase difference.

[0056] In one embodiment, the nonlinear analysis unit comprises:

[0057] A model calculation module, used to establish a linear load model based on the non-phase measurement data of the harmonic voltage and the non-phase measurement data of the harmonic current, calculate the resistance value and the inductance value of the linear load model by using the least square method, and output the result matrix;

[0058] A model error evaluation module, used to calculate the model error of each feeder based on the result matrix and compare it with a preset error threshold;

[0059] The harmonic source selection module is used to select a feeder containing a harmonic source according to the comparison result between the model error of each feeder and a preset error threshold.

[0060] In one embodiment, the expression of the result matrix is:

[0061] Ax=y

[0062]

[0063] Where x is the impedance matrix; A is the coefficient matrix; y is the voltage matrix; R is the resistance value in the linear load model; L is the inductance value in the linear load model; U[nT s ]、I[nT s ] are nT respectively s The voltage and current measured values ​​at time T s is the sampling interval; ΔI and Δt are the current change and time change respectively; T is the data time window length used in the least squares calculation, and N is the relative window length ratio;

[0064] The expression of the model error of each feeder is:

[0065]

[0066] Where e is the model error of each feeder, is the impedance estimate;

[0067] The expression of the preset error threshold is:

[0068] e thr =k rel e max

[0069] In the formula, e thr is the preset error threshold, k rel is the reliability coefficient, e max Maximum error when identifying nonlinear sources for a linear load model.

[0070] In one embodiment, the indicator calculation unit includes:

[0071] An indicator sequence building module, used to build a first indicator sequence based on the time series data of the harmonic voltage at the common connection point, and to build a second indicator sequence based on the time series data of the harmonic current of each feeder;

[0072] A correlation index calculation module, used to calculate the correlation index between the first index sequence and the second index sequence using a Pearson correlation algorithm;

[0073] The nonlinearity index evaluation module is used to calculate the nonlinearity index of each feeder including a harmonic source according to the fluctuation degree of the resistance value and the inductance value of the linear load model.

[0074] In one embodiment, according to the fluctuation degree of the resistance value and the inductance value of the linear load model, calculating the nonlinearity index of each feeder including the harmonic source includes:

[0075] Based on the resistance and inductance values ​​of the linear load model, the average values ​​of the resistance and inductance values ​​are calculated respectively, and the fluctuation degree of the resistance and inductance values ​​is analyzed;

[0076] According to the fluctuation degree of resistance and inductance, the nonlinearity of the feeder currently containing harmonic sources is analyzed to obtain the volatility index;

[0077] The volatility index is normalized and combined with the harmonic measured power of the feeder to calculate the nonlinearity index of the feeder containing the harmonic source.

[0078] In one embodiment, the expression of the correlation index is:

[0079]

[0080] In the formula, r 1j is the correlation index between the first index sequence and the second index sequence, n is the number of sampling points, x(n) is the first index sequence established based on the time series data of the harmonic voltage at the common connection point; y j (n) is a second indicator sequence established based on the time series data of the harmonic current of each feeder; μ represents the value of the first index sequence at time μ, represents the value of the second index sequence at time μ, represents the mean of the first indicator series, represents the mean of the second indicator series;

[0081] The expression of nonlinearity index is:

[0082]

[0083] In the formula, r Pj is the nonlinearity index, r′ 2j is the normalized volatility index of feeder j, P j is the harmonic measured power of feeder j.

[0084] In one embodiment, the indicator fusion unit includes:

[0085] The contrast strength and conflict module is used to standardize the correlation index and the nonlinearity index, solve the contrast strength of the correlation index and the nonlinearity index after the standardization, and calculate the conflict between the correlation index and the nonlinearity index after the standardization;

[0086] A weight assignment module is used to calculate the information carrying capacity according to the contrast intensity and conflict, and to solve the objective weight based on the information carrying capacity;

[0087] The fusion index module obtains the fusion index of the feeder based on the objective weight, weighted sum correlation index and nonlinearity index, and selects the feeder with the largest fusion index as the dominant harmonic source.

[0088] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0089] In one embodiment, the computer device includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above method when executing the computer program.

[0090] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0091] In one embodiment, the computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0092] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0093] (1) In view of the problem of dominant harmonic source identification in the prior art, the present invention proposes a dominant harmonic source identification method based on phase-free measurement data, which overcomes the dependence of traditional harmonic responsibility assessment technology on synchronous measurement phase data and realizes accurate harmonic source positioning through multi-index fusion analysis. It has the characteristics of strong practicality and wide adaptability.

[0094] (2) The present invention breaks through the limitation that conventional harmonic responsibility assessment technology needs to obtain synchronous measurement phase quantities of harmonic voltage and harmonic current at the common connection point. Since synchronous measurement phase quantities at the common connection point are difficult to obtain in actual engineering projects, it is often difficult to obtain accurate dominant harmonic source identification results based on traditional harmonic responsibility assessment technology. The present invention improves the engineering application value of the technical solution by using more easily accessible phase-free measurement data for harmonic source identification.

[0095] (3) The present invention uses the collected phase-free measurement data to judge the dominant harmonic source. First, the collected data is combined with a nonlinear analysis method, and the electrical quantity of each feeder is brought into the linear load model to judge the nonlinearity of each feeder and extract the feeders with low linearity; and the harmonic correlation index and the harmonic nonlinearity index of the selected feeders are calculated; finally, a weighted summation is performed based on an objective weighting method to obtain a fusion index of the two indexes, and the dominant harmonic source can be judged based on the relative size of the index; the method of the present invention is simple and easy to execute, suitable for phase-free measurement data, and is insensitive to background side harmonic voltage fluctuations, and has a wider range of applications.

[0096] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0098] Figure 1 is a flow chart of a method for identifying a dominant harmonic source according to an exemplary embodiment;

[0099] Figure 2 is a specific implementation diagram of a method for identifying a dominant harmonic source according to an exemplary embodiment;

[0100] Figure 3 is a schematic diagram of equivalent circuits on the system side and the user side in a method for identifying a dominant harmonic source according to an exemplary embodiment;

[0101] Figure 4 is a multi-harmonic source tracing topology diagram of a dominant harmonic source identification method according to an exemplary embodiment;

[0102] Figure 5 is a structural block diagram of a dominant harmonic source identification system according to an exemplary embodiment;

[0103] Figure 6 The diagram is a schematic structural diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0104] Figure 1-Figure 2 An embodiment of a dominant harmonic source identification method of the present invention is shown.

[0105] In this optional embodiment, the dominant harmonic source identification method includes:

[0106] Step S101: Based on the real-time collected non-phase measurement data of the power grid, the nonlinearity of each feeder in the power grid is determined by nonlinear analysis, and the feeders containing harmonic sources are screened;

[0107] It should be noted that the power grid includes busbars, feeders and public connection points, among which the busbar is the main conductor used to collect and distribute electric energy in the power system, the feeder is the line that distributes electric energy from the busbar to various load points, and the public connection point is the connection point where the user system accesses the public power grid. This point may be a branch on the busbar or an access point on the feeder. In short, the busbar is the center of power distribution in the power grid, the feeder is the connection line from the busbar to the load point, and the public connection point is the connection point where the user load accesses the power grid. Together, they constitute the distribution network of the power system. This is the existing technology and will not be elaborated here.

[0108] Step S103: according to the screened feeders containing harmonic sources, using a correlation analysis algorithm, solve the correlation index between the harmonic voltage of the common connection point and the harmonic current of each feeder, and calculate the nonlinearity index of each feeder containing the harmonic source;

[0109] Step S105: using an objective weighting method, weighted summing the correlation index and the nonlinearity index to obtain a fusion index of the feeder, and identifying the dominant harmonic source according to the size of the fusion index of each feeder.

[0110] Specifically, the present invention firstly performs nonlinear analysis on each feeder based on the harmonic voltage amplitude collected at the common connection point, the harmonic current amplitude of each feeder and the phase difference between them; screens out feeders with a large degree of nonlinearity, and considers that such feeders have harmonic sources; secondly, a correlation index that can reflect the correlation between the harmonic source and the common connection point (PCC) data and a harmonic nonlinearity index that can reflect the characteristics of the harmonic source itself are proposed, and the screened feeders are calculated based on the dual indicators; finally, the dual indicators are integrated based on the CRITIC objective weighting method to determine the dominant source of harmonics.

[0111] Specifically, the present invention comprises the following steps:

[0112] ① Perform nonlinear analysis on each feeder to screen out feeders that may contain harmonic sources;

[0113] ② Calculate the harmonic correlation index and harmonic nonlinearity index;

[0114] ③ Based on the CRITIC algorithm, the two indicators are integrated to determine the dominant source of harmonics.

[0115] In this optional embodiment, the power grid includes a bus, a feeder and a common connection point; the power grid phase-free measurement data is collected through a measurement terminal, and the measurement terminal includes a power quality meter and an intelligent fusion terminal; the power grid phase-free measurement data includes the phase-free measurement data of the harmonic voltage and the phase-free measurement data of the harmonic current; the phase-free measurement data of the harmonic voltage includes the harmonic voltage amplitude and the harmonic voltage phase difference, and the phase-free measurement data of the harmonic current includes the harmonic current amplitude and the harmonic current phase difference.

[0116] In this optional embodiment, based on the real-time collected grid non-phase measurement data, nonlinear analysis is used to determine the nonlinearity of each feeder in the grid, and feeders containing harmonic sources are screened, including:

[0117] Step S1011, establishing a linear load model based on the non-phase measurement data of the harmonic voltage and the non-phase measurement data of the harmonic current, calculating the resistance and inductance of the linear load model using the least square method, and outputting the result matrix;

[0118] Step S1013: Calculate the model error of each feeder based on the result matrix and compare it with a preset error threshold;

[0119] Step S1015: Select a feeder containing a harmonic source according to a comparison result between a model error of each feeder and a preset error threshold.

[0120] Specifically, in step ①, the nonlinear analysis method is used to determine the nonlinearity of each feeder to complete the feeder screening work.

[0121] Specifically, the monitoring equipment based on the intelligent fusion terminal in the substation monitors the harmonic voltage U at the public connection point pcc and harmonic current I pcc , establish as Figure 3 The Norton equivalent circuit is shown in the figure. S-h is the hth order background harmonic voltage vector value on the system side; U PCC-h ,I PCC-h are the hth harmonic voltage and current vector values ​​measured at the PCC; Z S-h is the equivalent harmonic impedance of the hth system side; Z Cj-h is the equivalent harmonic impedance of each user side at the hth order.

[0122] Specifically, by Figure 3 The Norton equivalent circuit diagram shown in Figure 1 and the monitoring position of the data can be obtained as follows Figure 4 The multi-harmonic tracing topology diagram is shown.

[0123] Specifically, according to the voltage and current values ​​measured at the feeder, the resistance value R and the inductance value L in the linear load model are calculated using the least square method.

[0124] Specifically, the linear load model is discretized:

[0125]

[0126] In the formula, U[nT s ]、I[nT s ] refers to nT s The voltage and current measured values ​​at time T s is the sampling interval, ΔI and ΔT refer to the change of current and time respectively.

[0127] Specifically, the discretized formula is organized into a matrix form, and the expression of the result matrix is:

[0128] Ax=y

[0129]

[0130] Wherein, x is the impedance matrix, which is a 2×1 matrix in this embodiment; A is the coefficient matrix, which is an N×2 matrix in this embodiment; y is the voltage matrix, which is an N×1 matrix in this embodiment; R is the resistance value in the linear load model; L is the inductance value in the linear load model; U[nT s ]、I[nT s ] are nT respectively s The voltage and current measured values ​​at time T s is the sampling interval; ΔI and Δt are the current change and time change respectively; T is the data time window length used in the least squares calculation; N is the relative window length ratio, which is equal to T and T s The ratio of

[0131] Specifically, the linear least squares method under non-negative conditions is used to solve x, and the expression is:

[0132]

[0133] All results are aggregated into a set of impedance estimates In the formula, R i , L i is the i-th resistance value and i-th inductance value of the calculated linear load model; obtained by calculation The error value is used to judge and screen the feeders that may have harmonic sources.

[0134] Specifically, the expression of the model error of each feeder is:

[0135]

[0136] Where e is the model error of each feeder, is the evaluation value;

[0137] Specifically, the expression of the preset error threshold is:

[0138] e thr =k rel e max

[0139] In the formula, e thr is the preset error threshold; k rel is the reliability coefficient, and its value needs to make e thr It is within the range of the maximum error and minimum error that may occur in the non-harmonic source analysis. In this embodiment, the reliability coefficient k rel Set to 2.5; e max It is the maximum error when identifying nonlinear sources for linear load models. This error belongs to the calculation error caused by replacing the derivative with the slope during the analysis process and decreases with the increase of sampling frequency.

[0140] Specifically, based on the comparison between the error calculation result e after each feeder is brought into the model and the preset error threshold, when the load on the calculated feeder is a harmonic source, the model error obtained after the feeder data is brought into the model for calculation is large, and the model error will exceed the set threshold; when the load on the calculated feeder is a non-harmonic source, the model error obtained after the feeder data is brought into the model for calculation is small, and the model error will not exceed the set threshold. Based on this, feeders with harmonic sources can be extracted, laying the foundation for the next step of work.

[0141] In this optional embodiment, according to the screened feeders containing harmonic sources, a correlation analysis algorithm is used to solve the correlation index between the harmonic voltage of the common connection point and the harmonic current of each feeder, and the nonlinearity index of each feeder containing the harmonic source is calculated, including:

[0142] Step S1031: establishing a first index sequence based on the time series data of the harmonic voltage at the common connection point, and establishing a second index sequence based on the time series data of the harmonic current of each feeder;

[0143] Step S1033, using the Pearson correlation algorithm, calculating the correlation index between the first indicator sequence and the second indicator sequence;

[0144] Step S1035: Calculate the nonlinearity index of each feeder including a harmonic source according to the fluctuation degree of the resistance value and the inductance value of the linear load model.

[0145] In this optional embodiment, according to the fluctuation degree of the resistance value and the inductance value of the linear load model, calculating the nonlinearity index of each feeder including the harmonic source includes:

[0146] Based on the resistance and inductance values ​​of the linear load model, the average values ​​of the resistance and inductance values ​​are calculated respectively, and the fluctuation degree of the resistance and inductance values ​​is analyzed;

[0147] According to the fluctuation degree of resistance and inductance, the nonlinearity of the feeder currently containing harmonic sources is analyzed to obtain the volatility index;

[0148] The volatility index is normalized and combined with the harmonic measured power of the feeder to calculate the nonlinearity index of the feeder containing the harmonic source.

[0149] Specifically, in step ②, the harmonic correlation index of the harmonic voltage at the common connection point and the harmonic current of each feeder is calculated based on the Pearson correlation algorithm, and the nonlinearity index of the harmonic source of each feeder is calculated based on the fluctuation degree of the R and L values ​​calculated by the linear model.

[0150] Specifically, correlation analysis is a quantitative analysis of data association. For real number sequences x(n) and y with n sampling points, j (n), the expression of the correlation index is:

[0151]

[0152] In the formula, r 1j is the correlation index between the first index sequence and the second index sequence. In the above embodiment, the value interval is [-1,1]. Since the purpose of the present invention is to determine the dominant harmonic source, when r 1j <0, the influence of the feeder on the common connection point is negatively correlated, and it is definitely not the dominant harmonic source; n is the number of sampling points, x(n) is the first indicator sequence established based on the time series data of the harmonic voltage at the common connection point; y j (n) is a second indicator sequence established based on the time series data of the harmonic current of each feeder; μ represents the value of the first index sequence at time μ, represents the value of the second index sequence at time μ, represents the mean of the first indicator series, represents the mean of the second indicator series;

[0153] Specifically, the correlation index r is obtained based on the Pearson correlation analysis method. 1j , when r 1j >0, the influence of the feeder on the common connection point is positively correlated and may be the dominant harmonic source; when r 1j <0, the influence of the feeder on the common connection point is negatively correlated, and it is definitely not the dominant harmonic source. 1j The larger the absolute value, the greater the impact of the feeder on the common connection point. 1j The larger the value, the more likely the feeder is to be the dominant harmonic source.

[0154] Specifically, the first indicator sequence x is used to represent the time series data of the harmonic voltage at the common connection point (PCC), and the second indicator sequence y is used to represent the time series data of the harmonic voltage at the common connection point (PCC). j Indicates the time series data of the harmonic current of each feeder. The harmonic correlation analysis results within the time period can be obtained from the data uploaded by the measuring device.

[0155] Specifically, since the correlation index reflects the influence of the feeder on the PCC point, it can only describe the correlation level between the data, but cannot describe the characteristics of the harmonic source itself. There is a certain error in identifying the dominant harmonic source using only the harmonic correlation index. Therefore, the present invention proposes a harmonic nonlinearity index to reflect the characteristics of the harmonic source itself.

[0156] Specifically, according to the current value measured at the feeder of interest and the harmonic voltage at the common connection point (PCC), the least squares method is used to calculate the R and L values ​​in the linear load model. The algorithm for calculating the R and L values ​​of this method is similar to the method of determining the nonlinearity of each feeder using the nonlinear analysis method in step ①, and will not be elaborated here.

[0157] Specifically, the nonlinearity of the feeder harmonic source is measured based on the fluctuation degree of the R and L values ​​calculated based on the linear load model, and the volatility index is obtained, thereby obtaining the nonlinearity index for judging each harmonic source.

[0158] Specifically, the expression of volatility index is:

[0159]

[0160] Where r2 is the volatility index; N s is the calculated resistance value R of the linear load model i Or inductance value L i The number of Rl 、r Ll R is calculated based on the measured voltage and current of the linear load. 1i and L 1i the degree of volatility; The calculated R i and L i The average value of .

[0161] Specifically, the calculated R i and L i The expression for the average value of is:

[0162]

[0163] In the formula, The calculated R i and L i The average value, Ns The calculated R i or L i The number of .

[0164] Specifically, the expression of the fluctuation degree of R and L values ​​calculated based on the linear load model is:

[0165]

[0166] In the formula, r Rl 、r Ll The calculated resistance values ​​of the linear load model are R 1i With the inductance value L 1i the degree of volatility; The calculated resistance values ​​of the linear load model are R i The average value and inductance value L i The average value of .

[0167] Specifically, the obtained volatility index r2 is normalized and the expression is:

[0168] r′2=arctan(r2)×(2 / π)

[0169] Since r2>0, the value range of the normalized volatility index r′2 is [0,1].

[0170] Specifically, the nonlinearity index of the harmonic source can be expressed by combining the measured feeder power and r′2:

[0171]

[0172] In the formula, r 2j is the volatility index of feeder j, P j is the measured harmonic power of feeder j, r Pj It is an indicator of nonlinearity.

[0173] Specifically, the expression of the nonlinearity index is:

[0174]

[0175] In the formula, r Pj is the nonlinearity index, r 2j is the normalized volatility index of feeder j, P j is the harmonic measured power of feeder j.

[0176] In this optional embodiment, an objective weighting method is used to weight the correlation index and the nonlinearity index to obtain a fusion index of the feeder, and according to the size of the fusion index of each feeder, the dominant harmonic source is identified, including:

[0177] Step S1051, normalizing the correlation index and the nonlinearity index, solving the comparison strength of the correlation index and the nonlinearity index after the normalization, and calculating the conflict between the correlation index and the nonlinearity index after the normalization;

[0178] Step S1053: Calculate the information carrying capacity according to the comparison strength and the conflict, and solve the objective weight based on the information carrying capacity;

[0179] Step S1055: Based on the objective weight, the correlation index and the nonlinearity index are weighted and summed to obtain the fusion index of the feeder, and the feeder with the largest fusion index is selected as the dominant harmonic source.

[0180] Specifically, in step ③, CRITIC is used to perform weighted calculation on the Pearson correlation index and the harmonic nonlinearity index, and finally a fusion index that can reflect the dominant source of feeder harmonics is obtained.

[0181] Specifically, the two indicators reflect the characteristics of the harmonic source in two aspects. The correlation indicator reflects the data correlation between the harmonic source and the PCC point, and the harmonic nonlinearity indicator reflects the characteristics of the harmonic source itself. When identifying the harmonic source, the two indicators need to be integrated to facilitate the identification of the dominant harmonic source.

[0182] It should be noted that the CRITIC objective weighting method is an objective weight assignment technology, the core of which is to use the contrast strength and the conflict between indicators to determine the indicator weights. The contrast strength, that is, the difference in indicator values, is usually expressed as a standard deviation, which reflects the degree of variation of the indicator. The conflict between indicators is measured based on the correlation between indicators. For example, if two indicators have a strong positive correlation, it indicates that their conflict is low. The main principle of the CRITIC objective weighting method is to combine the contrast strength and conflict to calculate the objective weight of each indicator in order to achieve a reasonable weight distribution of the evaluation scheme. This method is suitable for evaluating the stability of data and is suitable for analyzing indicators or factors with a certain correlation.

[0183] Specifically, before calculating the weights of the two indicators, the correlation indicator and the volatility indicator need to be standardized. The two indicators are combined into a data matrix X, where each indicator occupies 1 column of the matrix, and the element is x ij ; can form a data matrix X = (x ij ) m×n , let the element in the data matrix after standardization be x′ ij .

[0184] Specifically, each column in the data matrix is ​​standardized using the following formula:

[0185]

[0186] Specifically, based on the standardized data, the following formula is used to calculate the comparison intensity σ of the jth indicator: j :

[0187]

[0188] Where m is the number of correlation indicators and n is the number of volatility indicators.

[0189] Specifically, the conflict between indicators is calculated using the following formula: j :

[0190]

[0191] In the formula, r ij It refers to the correlation coefficient between the ith correlation index and the jth volatility index. In this embodiment, the Pearson correlation coefficient is used.

[0192] Specifically, the information carrying capacity C is calculated based on the comparison strength and conflict of the indicators. j :

[0193] C j =σ j f j

[0194] Specifically, the weight w of each indicator can be calculated by the information carrying capacity j :

[0195]

[0196] Specifically, based on the weight, the fusion index S that integrates the characteristics of the two indicators is obtained by weighted summation of the indicators:

[0197]

[0198] Specifically, based on the above analysis, the two indicators of each feeder are calculated to obtain the fusion indicator of each feeder, and the feeder with the largest fusion indicator is the dominant harmonic source.

[0199] Specifically, an objective weighting method is adopted to calculate the weights of the two indicators, and the weighted sum of the two indicators is used to establish a new fusion index S, which is used as the final output value of the method of the present invention. This can better combine the advantages of the harmonic correlation method and the load nonlinearity discrimination method, making the dominant harmonic source discrimination result more accurate.

[0200] Figure 5 An embodiment of a dominant harmonic source identification system of the present invention is shown.

[0201] In this optional embodiment, the dominant harmonic source identification system includes:

[0202] The nonlinear analysis unit 201 is used to determine the nonlinearity of each feeder in the power grid and screen the feeders containing harmonic sources based on the power grid non-phase measurement data collected in real time by nonlinear analysis, and the power grid non-phase measurement data includes the non-phase measurement data of harmonic voltage and the non-phase measurement data of harmonic current;

[0203] The index calculation unit 203 is used to solve the correlation index of the harmonic voltage of the common connection point and the harmonic current of each feeder according to the screened feeders containing harmonic sources, and calculate the nonlinearity index of the harmonic source of each feeder by using the correlation analysis algorithm;

[0204] The index fusion unit 205 is used to obtain the fusion index of the feeder by weighted summation of the correlation index and the nonlinearity index using an objective weighting method, and identify the dominant harmonic source according to the size of the fusion index of each feeder.

[0205] In this optional embodiment, the power grid includes a bus, a feeder and a common connection point; the power grid phase-free measurement data is collected through a measurement terminal, and the measurement terminal includes a power quality meter and an intelligent fusion terminal; the phase-free measurement data of the harmonic voltage includes the harmonic voltage amplitude and the harmonic voltage phase difference, and the phase-free measurement data of the harmonic current includes the harmonic current amplitude and the harmonic current phase difference.

[0206] In this optional embodiment, the nonlinear analysis unit 201 includes:

[0207] A model calculation module (not shown in the figure), used to establish a linear load model based on the non-phase measurement data of the harmonic voltage and the non-phase measurement data of the harmonic current, calculate the resistance value and the inductance value of the linear load model using the least square method, and output the result matrix;

[0208] A model error evaluation module (not shown in the figure) is used to calculate the model error of each feeder based on the result matrix and compare it with a preset error threshold;

[0209] The harmonic source selection module (not shown in the figure) is used to select a feeder containing a harmonic source according to the comparison result between the model error of each feeder and a preset error threshold.

[0210] In this alternative embodiment, the expression of the result matrix is:

[0211] Ax=y

[0212]

[0213] Where x is the impedance matrix; A is the coefficient matrix; y is the voltage matrix; R is the resistance value in the linear load model; L is the inductance value in the linear load model; U[nT s ]、I[nTs ] are nT respectively s The voltage and current measured values ​​at time T s is the sampling interval; ΔI and Δt are the current change and time change respectively; T is the data time window length used in the least squares calculation, and N is the relative window length ratio;

[0214] The expression of the model error of each feeder is:

[0215]

[0216] Where e is the model error of each feeder, is the impedance estimate;

[0217] The expression of the preset error threshold is:

[0218] e thr =k rel e max

[0219] In the formula, e thr is the preset error threshold, k rel is the reliability coefficient, e max Maximum error when identifying nonlinear sources for a linear load model.

[0220] In this optional embodiment, the indicator calculation unit 203 includes:

[0221] An indicator sequence building module (not shown in the figure) is used to build a first indicator sequence based on the time series data of the harmonic voltage at the common connection point, and to build a second indicator sequence based on the time series data of the harmonic current of each feeder;

[0222] A correlation index calculation module (not shown in the figure), used to calculate the correlation index between the first index sequence and the second index sequence using the Pearson correlation algorithm;

[0223] The nonlinearity index evaluation module (not shown in the figure) is used to calculate the nonlinearity index of each feeder harmonic source according to the fluctuation degree of the resistance value and the inductance value of the linear load model.

[0224] In this optional embodiment, according to the fluctuation degree of the resistance value and the inductance value of the linear load model, the nonlinearity index of each feeder harmonic source is calculated, which includes:

[0225] Based on the resistance and inductance values ​​of the linear load model, the average values ​​of the resistance and inductance values ​​are calculated respectively, and the fluctuation degree of the resistance and inductance values ​​is analyzed;

[0226] According to the fluctuation degree of resistance and inductance, the nonlinearity of the current feeder harmonic source is analyzed to obtain the volatility index;

[0227] The volatility index is normalized and combined with the harmonic measurement power of the feeder to calculate the nonlinearity index of the feeder harmonic source.

[0228] In this optional embodiment, the expression of the correlation index is:

[0229]

[0230] In the formula, r 1j is the correlation index between the first index sequence and the second index sequence, n is the number of sampling points, x(n) is the first index sequence established based on the time series data of the harmonic voltage at the common connection point; y j (n) is a second indicator sequence established based on the time series data of the harmonic current of each feeder; μ represents the value of the first index sequence at time μ, represents the value of the second index sequence at time μ, represents the mean of the first indicator series, represents the mean of the second indicator series;

[0231] The expression of nonlinearity index is:

[0232]

[0233] In the formula, r Pj is the nonlinearity index, r′ 2j is the normalized volatility index of feeder j, P j is the harmonic measured power of feeder j.

[0234] In this optional embodiment, the indicator fusion unit 205 includes:

[0235] A comparison strength and conflict calculation module (not shown in the figure) is used to standardize the correlation index and the nonlinearity index, solve the comparison strength of the correlation index and the nonlinearity index after the standardization, and calculate the conflict between the correlation index and the nonlinearity index after the standardization;

[0236] A weight assignment module (not shown in the figure) is used to calculate the information carrying capacity according to the comparison strength and conflict, and solve the objective weight based on the information carrying capacity;

[0237] The fusion index module (not shown in the figure) obtains the fusion index of the feeder based on the objective weight, weighted summation correlation index and nonlinearity index, and selects the feeder with the largest fusion index as the dominant harmonic source.

[0238] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0239] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0240] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0241] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0242] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention 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 or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0243] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for identifying a dominant harmonic source, characterized in that: The dominant harmonic source identification method includes: Based on the real-time collected non-phase measurement data of the power grid, nonlinear analysis is used to determine the nonlinearity of each feeder in the power grid and screen the feeders containing harmonic sources; According to the screened feeders containing harmonic sources, the correlation index between the harmonic voltage at the common connection point and the harmonic current of each feeder is solved by using the correlation analysis algorithm, and the nonlinearity index of each feeder containing harmonic sources is calculated; The objective weighting method is used to weight the correlation index and the nonlinearity index to obtain the fusion index of the feeder. The dominant harmonic source is identified according to the size of the fusion index of each feeder.

2. A method for identifying a dominant harmonic source according to claim 1, characterized in that: The power grid includes busbars, feeder lines and a public connection point; The grid non-phase measurement data is collected through a measurement terminal, and the measurement terminal includes a power quality meter and an intelligent fusion terminal; The power grid non-phase measurement data includes non-phase measurement data of harmonic voltage and non-phase measurement data of harmonic current; The non-phase measurement data of the harmonic voltage includes the harmonic voltage amplitude and the harmonic voltage phase difference, and the non-phase measurement data of the harmonic current includes the harmonic current amplitude and the harmonic current phase difference.

3. A method for identifying a dominant harmonic source according to claim 2, characterized in that: The method of using nonlinear analysis based on the real-time collected grid non-phase measurement data to determine the nonlinearity of each feeder in the grid and screening the feeders containing harmonic sources includes: A linear load model is established based on the non-phase measurement data of harmonic voltage and harmonic current, and the resistance and inductance values ​​of the linear load model are calculated using the least square method, and the result matrix is ​​output; Based on the result matrix, the model error of each feeder is calculated and compared with the preset error threshold; Based on the comparison result of the model error of each feeder with the preset error threshold, the feeder containing the harmonic source is selected.

4. A method for identifying a dominant harmonic source according to claim 3, characterized in that: The expression of the result matrix is: Ax=y Where x is the impedance matrix; A is the coefficient matrix; y is the voltage matrix; R is the resistance value in the linear load model; L is the inductance value in the linear load model; U[nT s ]、I[nT s ] are nT respectively s The voltage and current measured values ​​at time T s is the sampling interval; ΔI and Δt are the current change and time change respectively; T is the data time window length used in the least squares calculation, and N is the relative window length ratio; The expression of the model error of each feeder is: Where e is the model error of each feeder, is the impedance estimate; The expression of the preset error threshold is: yes thr =k rel yes max In the formula, e thr is the preset error threshold, k rel is the reliability coefficient, e max Maximum error when identifying nonlinear sources for a linear load model.

5. A method for identifying a dominant harmonic source according to claim 3, characterized in that: The method of solving the correlation index between the harmonic voltage at the common connection point and the harmonic current of each feeder using the correlation analysis algorithm based on the screened feeders containing harmonic sources, and calculating the nonlinearity index of each feeder containing harmonic sources includes: A first index sequence is established based on the time series data of the harmonic voltage at the common connection point, and a second index sequence is established based on the time series data of the harmonic current of each feeder; Using the Pearson correlation algorithm, calculate the correlation index between the first indicator sequence and the second indicator sequence; According to the fluctuation degree of the resistance and inductance of the linear load model, the nonlinearity index of each feeder containing harmonic sources is calculated.

6. A method for identifying a dominant harmonic source according to claim 5, characterized in that: The calculation of the nonlinearity index of each feeder including a harmonic source according to the fluctuation degree of the resistance value and the inductance value of the linear load model includes: Based on the resistance and inductance values ​​of the linear load model, the average values ​​of the resistance and inductance values ​​are calculated respectively, and the fluctuation degree of the resistance and inductance values ​​is analyzed; According to the fluctuation degree of resistance and inductance, the nonlinearity of the feeder currently containing harmonic sources is analyzed to obtain the volatility index; The volatility index is normalized and combined with the harmonic measured power of the feeder to calculate the nonlinearity index of the feeder containing the harmonic source.

7. A method for identifying a dominant harmonic source according to claim 6, characterized in that: The expression of the correlation index is: In the formula, r 1j is the correlation index between the first index sequence and the second index sequence, n is the number of sampling points, x(n) is the first index sequence established based on the time series data of the harmonic voltage at the common connection point; y j (n) is a second indicator sequence established based on the time series data of the harmonic current of each feeder; μ represents the value of the first index sequence at time μ, represents the value of the second index sequence at time μ, represents the mean of the first indicator series, represents the mean of the second indicator series; The expression of the nonlinearity index is: In the formula, r Pj is the nonlinearity index, r 2j is the normalized volatility index of feeder j, P j is the harmonic measured power of feeder j.

8. A method for identifying a dominant harmonic source according to claim 2, characterized in that: The objective weighting method is used to weight the correlation index and the nonlinearity index to obtain the feeder fusion index, and the dominant harmonic source is identified according to the size of the fusion index of each feeder, including: Standardize the correlation index and nonlinearity index, solve the comparison strength of the correlation index and the nonlinearity index after standardization, and calculate the conflict between the correlation index and the nonlinearity index after standardization; According to the contrast intensity and conflict, the information carrying capacity is calculated, and the objective weight is solved based on the information carrying capacity; Based on the objective weight, the weighted sum of the correlation index and the nonlinearity index is used to obtain the fusion index of the feeder, and the feeder with the largest fusion index is selected as the dominant harmonic source.

9. A dominant harmonic source identification system, characterized in that: The dominant harmonic source identification system comprises: A nonlinear analysis unit is used to determine the nonlinearity of each feeder in the power grid based on the real-time collected non-phase measurement data of the power grid, and to screen the feeders containing harmonic sources by using nonlinear analysis; An index calculation unit is used to solve the correlation index between the harmonic voltage of the common connection point and the harmonic current of each feeder according to the screened feeders containing harmonic sources by using a correlation analysis algorithm, and calculate the nonlinearity index of each feeder containing a harmonic source; The index fusion unit is used to obtain the fusion index of the feeder by using the objective weighting method, weighted summation of the correlation index and the nonlinearity index, and identify the dominant harmonic source according to the size of the fusion index of each feeder.

10. A dominant harmonic source identification system according to claim 9, characterized in that: The power grid includes busbars, feeder lines and a public connection point; The grid non-phase measurement data is collected through a measurement terminal, and the measurement terminal includes a power quality meter and an intelligent fusion terminal; The power grid non-phase measurement data includes non-phase measurement data of harmonic voltage and non-phase measurement data of harmonic current; The non-phase measurement data of the harmonic voltage includes the harmonic voltage amplitude and the harmonic voltage phase difference, and the non-phase measurement data of the harmonic current includes the harmonic current amplitude and the harmonic current phase difference.

11. A dominant harmonic source identification system according to claim 10, characterized in that: The nonlinear analysis unit comprises: A model calculation module, used to establish a linear load model based on the non-phase measurement data of the harmonic voltage and the non-phase measurement data of the harmonic current, calculate the resistance value and the inductance value of the linear load model by using the least square method, and output the result matrix; A model error evaluation module, used to calculate the model error of each feeder based on the result matrix and compare it with a preset error threshold; The harmonic source selection module is used to select a feeder containing a harmonic source according to the comparison result between the model error of each feeder and a preset error threshold.

12. A dominant harmonic source identification system according to claim 11, characterized in that: The expression of the result matrix is: Ax=y Where x is the impedance matrix; A is the coefficient matrix; y is the voltage matrix; R is the resistance value in the linear load model; L is the inductance value in the linear load model; U[nT s ]、I[nT s ] are nT respectively s The voltage and current measured values ​​at time T s is the sampling interval; ΔI and Δt are the current change and time change respectively; T is the data time window length used in the least squares calculation, and N is the relative window length ratio; The expression of the model error of each feeder is: Where e is the model error of each feeder, is the impedance estimate; The expression of the preset error threshold is: yes thr =k rel yes max In the formula, e thr is the preset error threshold, k rel is the reliability coefficient, e max Maximum error when identifying nonlinear sources for a linear load model.

13. A dominant harmonic source identification system according to claim 11, characterized in that: The indicator calculation unit comprises: An indicator sequence building module, used to build a first indicator sequence based on the time series data of the harmonic voltage at the common connection point, and to build a second indicator sequence based on the time series data of the harmonic current of each feeder; A correlation index calculation module, used to calculate the correlation index between the first index sequence and the second index sequence using a Pearson correlation algorithm; The nonlinearity index evaluation module is used to calculate the nonlinearity index of each feeder including a harmonic source according to the fluctuation degree of the resistance value and the inductance value of the linear load model.

14. A dominant harmonic source identification system according to claim 13, characterized in that: The calculation of the nonlinearity index of each feeder including a harmonic source according to the fluctuation degree of the resistance value and the inductance value of the linear load model includes: Based on the resistance and inductance values ​​of the linear load model, the average values ​​of the resistance and inductance values ​​are calculated respectively, and the fluctuation degree of the resistance and inductance values ​​is analyzed; According to the fluctuation degree of resistance and inductance, the nonlinearity of the feeder currently containing harmonic sources is analyzed to obtain the volatility index; The volatility index is normalized and combined with the harmonic measured power of the feeder to calculate the nonlinearity index of the feeder containing the harmonic source.

15. A dominant harmonic source identification system according to claim 14, characterized in that: The expression of the correlation index is: In the formula, r 1j is the correlation index between the first index sequence and the second index sequence, n is the number of sampling points, x(n) is the first index sequence established based on the time series data of the harmonic voltage at the common connection point; y j (n) is a second indicator sequence established based on the time series data of the harmonic current of each feeder; μ represents the value of the first index sequence at time μ, represents the value of the second index sequence at time μ, represents the mean of the first indicator series, represents the mean of the second indicator series; The expression of the nonlinearity index is: In the formula, r Pj is the nonlinearity index, r′ 2j is the normalized volatility index of feeder j, P j is the harmonic measured power of feeder j.

16. A dominant harmonic source identification system according to claim 10, characterized in that: The indicator fusion unit comprises: The contrast strength and conflict module is used to standardize the correlation index and the nonlinearity index, solve the contrast strength of the correlation index and the nonlinearity index after the standardization, and calculate the conflict between the correlation index and the nonlinearity index after the standardization; A weight assignment module is used to calculate the information carrying capacity according to the contrast intensity and conflict, and to solve the objective weight based on the information carrying capacity; The fusion index module obtains the fusion index of the feeder based on the objective weight, weighted sum correlation index and nonlinearity index, and selects the feeder with the largest fusion index as the dominant harmonic source.

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