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Power distribution network line loss abnormality diagnosis method and system based on K-means clustering algorithm

A k-means clustering and diagnostic method technology, applied in computing, complex mathematical operations, computer components and other directions, can solve the problems of lack of systematic research, lack of in-depth research on processing large-scale data sets and computational efficiency, and reduce Operating costs, improved processing power, and the effect of improved accuracy

Pending Publication Date: 2020-11-27
CHINA ELECTRIC POWER RES INST +3
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] At present, there is a lack of systematic research on the feature extraction and feature processing of load sequences with different time scales in the research on the detection of abnormal power consumption patterns at home and abroad, focusing on the accuracy of model prediction, and lack of in-depth calculation of the processing efficiency of large-scale data sets. Research

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  • Power distribution network line loss abnormality diagnosis method and system based on K-means clustering algorithm
  • Power distribution network line loss abnormality diagnosis method and system based on K-means clustering algorithm
  • Power distribution network line loss abnormality diagnosis method and system based on K-means clustering algorithm

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Embodiment 1

[0064] The application principle of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0065] like figure 1 As shown, the abnormal diagnosis method of distribution network line loss based on the K-means clustering algorithm in the embodiment of the present invention includes:

[0066] Step 1: Obtain multiple distribution network data based on the influencing factors that lead to abnormal line loss, and calculate the characteristic data corresponding to each influencing factor of each distribution network;

[0067] Step 2: Use the silhouette coefficient as the evaluation standard to determine the optimal number of cluster centers;

[0068] Step 3: Based on the optimal number of cluster centers, cluster the feature data using the K-means clustering algorithm;

[0069] Step 4: Select the feature data whose distance from the cluster center is greater than the preset threshold from all the feature data a...

Embodiment 2

[0113] Based on the same inventive concept, the present invention also provides a method system for abnormal diagnosis of distribution network line loss based on K-means clustering algorithm. The abnormal diagnosis methods are similar, and the repetitions will not be repeated here.

[0114] The basic structure of the system is as Figure 4 As shown, it includes: feature data module, optimal clustering center number module, clustering module and line loss judgment module;

[0115] Among them, the characteristic data module is used to obtain multiple distribution network data based on the influencing factors that cause the abnormal line loss, and calculate the characteristic data corresponding to each influencing factor of each distribution network;

[0116] The optimal cluster center number module is used to determine the optimal cluster center number by using the silhouette coefficient as an evaluation criterion;

[0117] A clustering module, configured to perform clustering...

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Abstract

The invention discloses a power distribution network line loss abnormality diagnosis method and system based on a K-means clustering algorithm. The method comprises the steps of obtaining multiple pieces of power distribution network data based on influence factors causing line loss abnormity, and calculating feature data, power factors, power supply electric quantity, a line loss rate average value, a line loss rate change coefficient and ammeter uncovering records corresponding to the influence factors of each power distribution network respectively; determining an optimal clustering centernumber by taking a contour coefficient as an evaluation standard; clustering the feature data by adopting the K-means clustering algorithm on the basis of the optimal clustering center number; and selecting the feature data of which the distance from the clustering center is greater than a preset threshold value from all the feature data as a line loss abnormal point. According to the method, thecharacteristic that the K-means clustering algorithm is unsupervised is utilized, the method for massively processing the line loss abnormal data is designed, and the calculation efficiency of processing a large-scale data set is improved.

Description

technical field [0001] The invention belongs to the technical field of electric power system automation, and in particular relates to a method and system for diagnosing abnormal line loss of a distribution network based on a K-means clustering algorithm. Background technique [0002] With the continuous improvement of the power system informatization and the rapid growth of the data volume of power distribution, researching algorithms suitable for data mining of power distribution and establishing an effective knowledge discovery model will play a significant role in the innovation of power distribution business models and the development of smart grids. is of great significance. However, so far, "massive data, lack of information" is still an important problem faced by power companies. The connotation of electric power big data is to reshape the core value of electric power and transform the development mode of electric power. Through the excavation of the individual mark...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/18G06F17/11G06F16/903G06Q50/06
CPCG06F17/11G06F17/18G06Q50/06G06F16/90335G06F18/23213
Inventor 刘科研贾东梨孟晓丽盛万兴何开元刁赢龙李国栋王峥满玉岩詹惠瑜张怀天
Owner CHINA ELECTRIC POWER RES INST