CN112528762A - Harmonic source identification method based on data correlation analysis - Google Patents
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Abstract
The invention relates to a harmonic source identification method based on data correlation analysis, which comprises the following steps: acquiring harmonic voltage of a PCC (point of common coupling) and harmonic current data of each feeder line within a period of time, generating waveforms, and performing primary positioning on a harmonic source; carrying out sectional clustering analysis on the harmonic voltage and the harmonic current respectively, and outputting clustering analysis results; comparing the harmonic voltage with each section clustering result of each group of harmonic current, judging the similarity degree of each feeder line harmonic current and the harmonic voltage on the whole, and selecting the highest similarity degree as a judged harmonic source; and comparing whether the determined harmonic source is consistent with the preliminarily positioned harmonic source, and if so, successfully positioning the harmonic source. The method does not depend on the power grid parameters and models, is not influenced by other conditions, has simpler calculation process, less calculation amount and higher speed, and can quickly identify the harmonic source.
Description
Technical Field
The invention relates to the technical field of harmonic source positioning, in particular to a harmonic source identification method based on data correlation analysis.
Background
How to accurately position and identify harmonic sources is a key problem in the process of researching the quality of electric energy. The influence of the harmonic problem of the power system is increasingly large, if the position of each harmonic source cannot be correctly judged and the harmonic responsibility cannot be specifically divided, the pollution source and the victim of the harmonic can not be clearly distinguished, so that the reward and punishment mechanism of the harmonic can not be realized, and the fairness and the effectiveness are not to mention. However, the conventional harmonic source identification method has certain limitations due to various factors, and cannot quickly and accurately identify and position harmonic sources in a complex power grid, so that the research on a new harmonic source identification method has extremely important theoretical significance, economic benefits and technical values. Power data analytics, which have emerged with the large number of applications of various new power devices, have evolved into a new direction in the field of power quality research. The power data contains a large amount of historical information, various state information of the system can be calculated according to the dynamic data, and a solid data base can be provided for harmonic source identification and responsibility division. The invention provides a method for identifying and positioning harmonic sources by comparing and analyzing the similarity between harmonic voltage and current by using an improved K-means clustering method under the background of data correlation analysis. Compared and analyzed by a large amount of actual data, the similarity between the harmonic voltage of the common point and the harmonic current of the user is analyzed by utilizing a K-means clustering method, the harmonic source can be correctly identified and positioned, the method has the advantages of simpler process, less calculation amount and higher speed, and the harmonic source can be quickly and accurately identified.
The traditional harmonic source identification methods at home and abroad are roughly divided into two categories:
(1) power method. Such as: an active power direction method, a reactive power direction method, a synchronous measurement discrimination method, a critical impedance method and a reactive power variation method. The method is greatly influenced by phase difference between harmonic sources, and an active power direction method can generate certain errors in the process of identifying the harmonic sources, so that the method is only used for the condition of a single harmonic source; the reactive power direction method is not only influenced by the amplitude of the voltage source, but also related to harmonic impedance, so that the accuracy is not high; although the synchronous measurement discrimination method is not influenced by harmonic impedance, the accurate value of the power angle must be known, and the power angle is difficult to accurately measure due to factors such as time delay of a measurement system, so the application of the method is not wide; because the measurement error is large, the accuracy of the critical impedance identification method is not high; although the reactive power variation method does not need to know the harmonic impedance, the method can only qualitatively analyze the direction of the harmonic source and cannot quantitatively analyze the direction.
(2) Harmonic impedance method. The method comprises the following steps: differential equation method, least square method, fluctuation amount method, and linear regression method. The differential equation method is difficult to be practically applied because only harmonic sources of local branches can be identified in a complex network; the least square method can well distinguish linear and nonlinear loads, but cannot solve the problem of responsibility distinction under the condition of multiple harmonic sources; the fluctuation method and the linear regression method cannot determine the specific position of a harmonic source when the harmonic source exists on the load side. Generally, harmonic impedance measurement is performed in the case of disturbance, but in practice, the disturbance is highly random, and thus it is difficult to estimate the harmonic impedance. The first method is mainly qualitative analysis, and the second method is mainly quantitative analysis. Among them, the power method is relatively simpler and more intuitive, so the application is wider.
Since then, many new methods have emerged, which are more sophisticated than the traditional methods, such as: sensitivity methods, artificial neural network based methods, current vector methods, reference impedance methods, kalman filter based methods, Phasor Measurement Unit (PMU) based methods, and Singular Value Decomposition (SVD) based methods. The method based on the artificial neural network has the capability of simultaneously processing large-scale data and the capability of autonomous learning, but is a new harmonic source positioning method, and the technical realization needs to be perfected; the current vector method needs known harmonic impedance and does not consider the change of the harmonic impedance, so the practical application is difficult; the error in the result of the reference impedance method may be large.
Today, more efficient methods have emerged: a harmonic source identification method based on mutual information, composite criterion and data correlation analysis. Compared with the traditional method, the new methods have the advantages of clear thought, more accurate result and simpler calculation, not only make up for the defects of the traditional method, but also be suitable for a complex network, and have stronger credibility of the judgment result. Among them, the method based on data correlation analysis is gradually exposing the corners, and is popular and more efficient. Although the research data for identifying and positioning harmonic sources and dividing harmonic responsibilities from the perspective of analyzing the similarity of harmonic data is not abundant at present, it cannot be denied that the method based on data association analysis has a very broad prospect and is the mainstream direction in the field of current and future harmonic governance.
The disadvantages of the prior art are as follows:
1. in the prior art, the influence of factors such as phase difference between harmonic sources, amplitude of a voltage source, harmonic impedance, accuracy of a power factor angle and the like is large, so that certain errors can be generated in the process of identifying the harmonic sources, the accuracy of results is low, and the application is not wide.
2. The prior art needs to know detailed network parameters and an accurate topological structure, but in actual engineering, due to the approximation of a system model and the lack of the network parameters, a large error is generated in an estimation result.
3. The prior art can not well realize the effect under a complex network, can not solve the problem of responsibility distinction under the condition of a multi-harmonic source, can not judge the specific position of the harmonic source and can not eliminate the influence caused by random disturbance.
4. In the prior art, the calculation is complex, the calculation process is complicated, the calculation amount is large, the matrix solving is difficult, and the measurement investment cost is high; the method has the advantages of more intermediate parameters, large storage space requirement, increased calculation time, reduced calculation speed and incapability of quickly realizing the identification and positioning of the harmonic source.
Disclosure of Invention
In view of the above, the invention aims to provide a harmonic source identification method based on data association analysis, which is independent of power grid parameters and models, is not influenced by other conditions, is simpler in calculation process, less in calculation amount and higher in speed, and can quickly identify a harmonic source.
The invention is realized by adopting the following scheme: a harmonic source identification method based on data correlation analysis specifically comprises the following steps:
acquiring harmonic voltage of a PCC (point of common coupling) and harmonic current data of each feeder line within a period of time, generating waveforms, and performing primary positioning on a harmonic source;
carrying out sectional clustering analysis on the harmonic voltage and the harmonic current respectively, and outputting clustering analysis results;
comparing the harmonic voltage with each section clustering result of each group of harmonic current, judging the similarity degree of each feeder line harmonic current and the harmonic voltage on the whole, and selecting the highest similarity degree as a judged harmonic source;
and comparing whether the determined harmonic source is consistent with the preliminarily positioned harmonic source, and if so, successfully positioning the harmonic source.
Further, the preliminary positioning of the harmonic source specifically includes:
and comparing the similarity between the harmonic voltage waveform of the PCC and the harmonic current waveforms of all the feeders, and selecting the feeder with the highest similarity as a preliminarily positioned harmonic source.
Further, the step of performing segmented clustering analysis on the harmonic voltage and the harmonic current respectively and outputting a clustering analysis result specifically comprises:
respectively segmenting harmonic voltage and harmonic current; and performing improved clustering analysis on each segment of data, and outputting the clustering center of each segment and the percentage of each type of data.
Further, the performing of the improved clustering analysis on each segment of data specifically includes:
taking each section of data as an input sample set, and setting the number of clustering clusters;
normalizing the sample data, initializing k clustering centers, and sequencing the clustering centers;
calculating the distance from each data to each cluster center, and distributing each data to the class closest to the data;
and recalculating centers of various types, outputting clustering results if the centers are converged, and otherwise recalculating the distance from each data to each clustering center.
Further, the step of comparing the harmonic voltage with the clustering results of each segment of each group of harmonic currents, integrally determining the similarity between the harmonic current and the harmonic voltage of each feeder line, and specifically selecting the harmonic source with the highest similarity as the determined harmonic source comprises:
for each group of harmonic current, comparing the similarity of the value of the clustering center of each section with the clustering center of each section of the PCC harmonic voltage, and comparing the percentage of various data quantity of each section with the percentage of various data quantity of each section of the PCC harmonic voltage; and selecting the feeder corresponding to the group of harmonic currents with the highest similarity as a judged harmonic source.
The invention provides a harmonic source identification system based on data correlation analysis, comprising a memory, a processor and computer program instructions stored on the memory and executable by the processor, which when executed by the processor, are capable of implementing the method steps as described above.
The present invention also provides a computer readable storage medium having stored thereon computer program instructions executable by a processor, the computer program instructions when executed by the processor being capable of performing the method steps as described above.
Compared with the prior art, the invention has the following beneficial effects:
1. the method only aims at the change rule of the harmonic data without being influenced by other factors, does not need to know detailed network parameter data and topological structure, overcomes the defect of larger error, and provides a new path for identifying the harmonic source.
2. The method only needs to compare two parameters of the clustering center and the percentage when the similarity between the data is compared, and simultaneously, no intermediate parameter is formed, so that the method has the advantages of simple calculation, simplified process, small calculation amount, high convergence speed and capability of quickly finishing the calculation of the harmonic related data.
3. The invention provides a method for identifying and positioning a harmonic source by analyzing the similarity between harmonic voltage and harmonic current by adopting a K-means clustering method, belongs to the field of harmonic analysis and data association analysis of a power system, and aims at historical data of power system harmonics.
4. Aiming at the condition that the parameter result output by the common K-means clustering method is not suitable for similarity comparison, the invention improves the parameter result in three aspects of normalization, ordering and percentage calculation. The improved K-means clustering method has two parts of output result parameters, wherein one part is an ordered clustering center which is located between 0 and 1, and the other part is the data volume of each cluster and the percentage of the data volume occupied by the data volume. The output result of the improved K-means clustering method can ideally and intuitively illustrate the similarity between data and is more suitable for data association analysis.
5. Aiming at the contradiction between the one-dimensional property of the common K-means clustering method and the two-dimensional property of the harmonic data, the data are segmented in time, the overall similarity is judged by comparing the similarity between the data in a segmented and one-to-one mode, namely, the data of the clustering method is artificially added with the timeliness, and the problems of low error and low reliability caused by the dimension conflict between the common clustering method and the harmonic data are solved.
Drawings
FIG. 1 is a schematic diagram of the method of the embodiment of the present invention.
Fig. 2 is a schematic flow chart of an improved K-meas clustering method according to an embodiment of the present invention.
FIG. 3 is a 4 month, 2 day, 5 harmonic voltage waveform for a PCC point according to an embodiment of the present invention.
Fig. 4 is a 4-month, 2-day, 5-harmonic current waveform of the litz wire according to an embodiment of the present invention.
Fig. 5 is a 4 month, 2 day, 5 th harmonic current waveform of the lissajous line according to an embodiment of the present invention.
Fig. 6 is a 4-month, 2-day, 5-harmonic current waveform of the lee-stone line according to an embodiment of the present invention.
Fig. 7 shows the waveform of the current of 5 th harmonic in 4 months, 2 days, in the littleneck line according to the embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides a method for identifying a harmonic source based on data association analysis, which specifically includes the following steps: acquiring harmonic voltage of a PCC (point of common coupling) and harmonic current data of each feeder line within a period of time, generating waveforms, and performing primary positioning on a harmonic source; carrying out sectional clustering analysis on the harmonic voltage and the harmonic current respectively, and outputting clustering analysis results; comparing the harmonic voltage with each section clustering result of each group of harmonic current, judging the similarity degree of each feeder line harmonic current and the harmonic voltage on the whole, and selecting the highest similarity degree as a judged harmonic source; and comparing whether the determined harmonic source is consistent with the preliminarily positioned harmonic source, and if so, successfully positioning the harmonic source.
Compared and analyzed by a large amount of actual data, the similarity between the harmonic voltage of the common point and the harmonic current of the user is analyzed by utilizing a K-means clustering method, and a positioning harmonic source can be correctly identified; the method only aims at the change rule of harmonic data, does not depend on power grid parameters and models, is not influenced by other conditions, is simpler in calculation process, less in calculated amount and higher in speed, can quickly identify the harmonic source, and overcomes the defects in the prior art.
In this embodiment, the preliminary positioning of the harmonic source specifically includes: and comparing the similarity between the harmonic voltage waveform of the PCC and the harmonic current waveforms of all the feeders, and selecting the feeder with the highest similarity as a preliminarily positioned harmonic source.
In the embodiment, a clustering method is mainly adopted to judge the similarity between the harmonic current of each feeder line and the harmonic voltage of the PCC points. As known from the norton equivalent circuit, the harmonic voltage is linearly related to the harmonic current of each feeder. A harmonic source identification method based on data correlation analysis provides a theory that: if the harmonic voltage data of the PCC point is very similar to the harmonic current data of a feeder, then it is assumed that a harmonic source is likely on that feeder. The clustering method is a method for classifying the study objects according to the characteristics of the study objects. The clustering analysis is simple and visual, and the result output by the clustering method is a cluster set which represents the characteristics of the group of data. Therefore, the harmonic voltage and the harmonic current can be respectively subjected to cluster analysis, and then the cluster output results of each group of data are compared to judge the similarity degree of the harmonic current and the harmonic voltage of each feeder line, so that possible harmonic sources can be positioned. In addition, the data adopted by the K-means clustering method is one-dimensional, so that the increase and decrease of the data along with time cannot be reflected, while the harmonic data is two-dimensional, the time is not negligible, otherwise, the compared similarity is meaningless and has no credibility; therefore, the embodiment locates possible harmonic sources by dividing the data time into a plurality of segments, then performing similarity comparison in segments, and finally comprehensively considering the similarity of all the time segments to determine whether the similarity exists between the whole group of data.
In this embodiment, the step of performing the segmented clustering analysis on the harmonic voltage and the harmonic current respectively and outputting the clustering analysis result specifically includes: respectively segmenting harmonic voltage and harmonic current; and performing improved clustering analysis on each segment of data, and outputting the clustering center of each segment and the percentage of each type of data.
In this embodiment, as shown in fig. 2, the performing the improved clustering analysis on each piece of data specifically includes:
taking each section of data as an input sample set, and setting the number of clustering clusters;
normalizing the sample data, initializing k clustering centers, and sequencing the clustering centers;
calculating the distance from each data to each cluster center, and distributing each data to the class closest to the data;
and recalculating centers of various types, outputting clustering results if the centers are converged, and otherwise recalculating the distance from each data to each clustering center.
Regarding the improved clustering algorithm, this embodiment proposes that, since the K-means clustering method randomly selects K samples when selecting the initial clustering center vector, that is, means that the K clustering centers are unordered, then the K clustering centers in the final clustering output result are still unordered. When the clustering output results of two groups of data are compared, whether the clustering centers of various types are similar or not needs to be compared, if the clustering centers are unordered, troubles and errors are undoubtedly brought to the comparison. In consideration of the point, after the initial cluster centers are randomly selected, the initial cluster centers are sorted, and the final cluster center result obtained by the method is ordered, so that the comparison of two groups of data is more convenient and reasonable. Meanwhile, because each group of data is different, voltage and current exist, and peak time and valley time exist, the amplitudes of the data are different, namely the voltage data as small as a few tenths and the active data as large as thousands. If the overall level difference of the data amplitude is large, the amplitude of the cluster center of the data amplitude is also large, so that when the output result is compared, the data amplitude is not comparable because the magnitude of the data amplitude is not on the same level. Therefore, after the sample set is input, all data are normalized to be between 0 and 1 by using the normalization method, so that all data amplitude values are on the same level, and the output result is also contrastable. In addition, as the result output by the common clustering method is the clustering center and the category to which each data belongs, the clustering center has comparability through the two improvements, but the data volumes of various types cannot be compared. Therefore, the total number of each type of data needs to be counted for comparison. However, it is considered that the total amount of each set of collected data is different, and thus the data amount of each set is also different for each type, and cannot be compared. Therefore, each type of data was counted and the percentage thereof was calculated. When the total amount is different, the amounts of the parts cannot be compared, but the percentages of the parts are comparable. In conclusion, the improved clustering algorithm is more suitable for comparing and analyzing the similarity of two groups of data, wherein the similarity is judged by comparing whether the output clustering center values are similar or not, and the similarity is judged by comparing whether the percentage of each type of data is similar or not. If the cluster centers and percentages are very similar, it means that the two sets of data are very similar. The improved flow chart of the complete K-means clustering method is shown in figure 2.
In this embodiment, the step of comparing the harmonic voltage with the clustering results of each segment of each group of harmonic currents, and overall determining the similarity between the harmonic current and the harmonic voltage of each feeder line, and the step of selecting the harmonic source with the highest similarity as the determined harmonic source specifically includes: for each group of harmonic current, comparing the similarity of the value of the clustering center of each section with the clustering center of each section of the PCC harmonic voltage, and comparing the percentage of various data quantity of each section with the percentage of various data quantity of each section of the PCC harmonic voltage; and selecting the feeder corresponding to the group of harmonic currents with the highest similarity as a judged harmonic source.
The present embodiment provides a harmonic source identification system based on data correlation analysis, comprising a memory, a processor and computer program instructions stored on the memory and executable by the processor, which when executed by the processor, enable the method steps as described above to be carried out.
The present embodiments also provide a computer readable storage medium having stored thereon computer program instructions executable by a processor, the computer program instructions, when executed by the processor, being capable of performing the method steps as described above.
Specifically, the present embodiment is described with reference to a specific example.
In the embodiment, the voltage data is the A-phase 95% probability value of the 5-order harmonic voltage content of the PCC points where the plum iron wire, the plum western wire, the plum rear wire and the plum stele wire are located, and the data in one day of 4 months and 2 days in 2019 years is obtained; the current data, whether position, type, or time, should be consistent with the voltage data, otherwise, the data comparison between the two is meaningless. Therefore, the current data is the A-phase 95% probability value of 5 harmonic current effective values of the four feeder lines including the litz wire, the litz rear wire and the litz stele wire, and similarly, the data of one day is 4 months and 2 days. The interval for each data point was 3 minutes. Based on the data of 4 months and 2 days, the waveforms of the harmonic voltages and currents of the respective groups are shown in fig. 3 to 7. As can be seen from the harmonic voltage current waveform diagrams of fig. 3 to 7, only one set of the litz wires among the harmonic currents of the feeder lines of each set is very similar to the harmonic voltage. From this, it can be preliminarily determined that there may be a harmonic source in the litz wire. After a large number of data calculations, k is 4, the best result is obtained. In this embodiment, each group of data is divided into 4 segments on average for cluster analysis, and the results are further verified and output as table 1.
TABLE 1 clustering output result of 4-month and 2-day harmonic voltage and current data
As can be seen from comparative analysis of a large amount of data in table 1, only the clustering output result of the harmonic current of the litz wire in the four feeder lines is similar to each section of the harmonic voltage of the PCC point in a one-to-one correspondence manner, that is, the overall is similar. That is, according to the result of the cluster analysis, only one possible harmonic source is located on the litz wire, and the result of the comparison with the waveform is consistent. Therefore, the harmonic source can be judged quickly and accurately by the embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (7)
1. A harmonic source identification method based on data correlation analysis is characterized by comprising the following steps:
acquiring harmonic voltage of a PCC (point of common coupling) and harmonic current data of each feeder line within a period of time, generating waveforms, and performing primary positioning on a harmonic source;
carrying out sectional clustering analysis on the harmonic voltage and the harmonic current respectively, and outputting clustering analysis results;
comparing the harmonic voltage with each section clustering result of each group of harmonic current, judging the similarity degree of each feeder line harmonic current and the harmonic voltage on the whole, and selecting the highest similarity degree as a judged harmonic source;
and comparing whether the determined harmonic source is consistent with the preliminarily positioned harmonic source, and if so, successfully positioning the harmonic source.
2. The method for identifying a harmonic source based on data association analysis according to claim 1, wherein the preliminary positioning of the harmonic source specifically comprises:
and comparing the similarity between the harmonic voltage waveform of the PCC and the harmonic current waveforms of all the feeders, and selecting the feeder with the highest similarity as a preliminarily positioned harmonic source.
3. The method for identifying harmonic sources based on data association analysis according to claim 1, wherein the step of performing segmented clustering analysis on the harmonic voltages and the harmonic currents respectively and outputting clustering analysis results specifically comprises:
respectively segmenting harmonic voltage and harmonic current; and performing improved clustering analysis on each segment of data, and outputting the clustering center of each segment and the percentage of each type of data.
4. The harmonic source identification method based on data association analysis according to claim 3, wherein the performing of the improved clustering analysis on each segment of data specifically comprises:
taking each section of data as an input sample set, and setting the number of clustering clusters;
normalizing the sample data, initializing k clustering centers, and sequencing the clustering centers;
calculating the distance from each data to each cluster center, and distributing each data to the class closest to the data;
and recalculating centers of various types, outputting clustering results if the centers are converged, and otherwise recalculating the distance from each data to each clustering center.
5. The method for identifying harmonic sources based on data correlation analysis according to claim 1, wherein the harmonic voltage is compared with the clustering results of each segment of each group of harmonic currents, the similarity between the harmonic current and the harmonic voltage of each feeder line is determined on the whole, and the harmonic source with the highest similarity is selected as the determined harmonic source, specifically:
for each group of harmonic current, comparing the similarity of the value of the clustering center of each section with the clustering center of each section of the PCC harmonic voltage, and comparing the percentage of various data quantity of each section with the percentage of various data quantity of each section of the PCC harmonic voltage; and selecting the feeder corresponding to the group of harmonic currents with the highest similarity as a judged harmonic source.
6. A harmonic source identification system based on data correlation analysis comprising a memory, a processor and computer program instructions stored on the memory and executable by the processor, the computer program instructions when executed by the processor being operable to implement the method steps of any of claims 1 to 5.
7. A computer-readable storage medium, having stored thereon computer program instructions executable by a processor, the computer program instructions, when executed by the processor, being capable of carrying out the method steps according to any one of claims 1 to 5.
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| CN113890018A (en) * | 2021-09-27 | 2022-01-04 | 广西电网有限责任公司桂林供电局 | Power distribution network weak point identification method based on data correlation analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109472231A (en) * | 2018-10-31 | 2019-03-15 | 南方电网科学研究院有限责任公司 | Substation's harmonic source location method and device, computer readable storage medium |
| US10330812B1 (en) * | 2015-03-16 | 2019-06-25 | Elintrix | Identifying schematic and topological properties of an electrical grid through analysis of directional relationships of electrical currents |
| CN110163304A (en) * | 2019-06-14 | 2019-08-23 | 福州大学 | A kind of harmonic source coupling parameter discrimination method clustered using linear relationship |
| CN110618316A (en) * | 2019-10-12 | 2019-12-27 | 南方电网科学研究院有限责任公司 | Harmonic source identification method and device, terminal equipment and storage medium |
| CN111898499A (en) * | 2020-07-17 | 2020-11-06 | 东南大学 | Multi-harmonic source harmonic responsibility division method based on mutual approximation entropy and clustering |
-
2020
- 2020-11-25 CN CN202011334378.7A patent/CN112528762B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10330812B1 (en) * | 2015-03-16 | 2019-06-25 | Elintrix | Identifying schematic and topological properties of an electrical grid through analysis of directional relationships of electrical currents |
| CN109472231A (en) * | 2018-10-31 | 2019-03-15 | 南方电网科学研究院有限责任公司 | Substation's harmonic source location method and device, computer readable storage medium |
| CN110163304A (en) * | 2019-06-14 | 2019-08-23 | 福州大学 | A kind of harmonic source coupling parameter discrimination method clustered using linear relationship |
| CN110618316A (en) * | 2019-10-12 | 2019-12-27 | 南方电网科学研究院有限责任公司 | Harmonic source identification method and device, terminal equipment and storage medium |
| CN111898499A (en) * | 2020-07-17 | 2020-11-06 | 东南大学 | Multi-harmonic source harmonic responsibility division method based on mutual approximation entropy and clustering |
Non-Patent Citations (3)
| Title |
|---|
| HAOYUAN SHA 等: "Multi-Harmonic Sources Harmonic Contribution Determination Based on Data Filtering and Cluster Analysis", 《IEEE ACCESS》, 19 June 2010 (2010-06-19) * |
| 于永军 等: "谐波源定位与谐波责任划分方法的探讨", 《方案设计》, 31 July 2017 (2017-07-31) * |
| 王攸然 等: "谐波责任划分研究现状及在分布式电源并网条件下的展望", 《电工电能新技术》, 31 January 2019 (2019-01-31) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113890018A (en) * | 2021-09-27 | 2022-01-04 | 广西电网有限责任公司桂林供电局 | Power distribution network weak point identification method based on data correlation analysis |
| CN113890018B (en) * | 2021-09-27 | 2023-11-17 | 广西电网有限责任公司桂林供电局 | Power distribution network weak point identification method based on data association analysis |
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