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Method for fault classification of smart electric meter based on cluster analysis and cloud model

A smart meter and fault classification technology, applied in the direction of measuring electrical variables, measuring devices, instruments, etc., can solve the problem of large and complex fault data, and achieve the effect of easy fault judgment, detailed classification, and easy fault division

Inactive Publication Date: 2016-08-17
SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
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Problems solved by technology

[0002] The rapid development of computer and network technology has made the means of information acquisition and analysis increasingly advanced. Data mining has become an important tool for enterprises and institutions to study data laws. Data mining platforms are mainly divided into storage computing platforms and data mining algorithms. The characteristics of meter data, the current smart energy meter fault data is huge and complex, and there is no qualitative analysis method that can separate it according to the fault type, and there is an urgent need for a fault analysis and classification method from quantitative to qualitative

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  • Method for fault classification of smart electric meter based on cluster analysis and cloud model
  • Method for fault classification of smart electric meter based on cluster analysis and cloud model
  • Method for fault classification of smart electric meter based on cluster analysis and cloud model

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Embodiment

[0044] K-means algorithm is one of the most widely used clustering algorithms. The algorithm takes the minimum standard measure function as the classification principle, and divides N electric energy meter fault data sample points into K clusters. The clustering results make the fault data sample points of electric energy meters in the same cluster have high similarity, but the similarity of fault data sample points of electric energy meters among different clusters is low. The specific classification steps of the K-means algorithm are as follows:

[0045] (1) Randomly select K power meter fault data sample points as the initial clustering center;

[0046] (2) For each remaining energy meter fault data sample point, assign it to the nearest cluster according to its distance from each cluster center;

[0047] (3) Calculate the sample mean of each cluster, and calculate the standard measure function, namely:

[0048] m i = ...

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Abstract

The invention relates to a method for fault classification of a smart electric meter based on cluster analysis and a cloud model. The method comprises a step 1) of obtaining historical smart electric meter fault data sample points, and adopting a K-means algorithm to divide historical smart electric meter fault data sample points into K large fault classes and central values corresponding to each large class; a step 2) of taking the central values corresponding to each large class as sample means, taking smart electric meter fault data sample points contained in each large fault class as data points, and generating a corresponding K-class electric meter fault cloud model; a step 3) of adopting a reverse normal cloud generator to calculate the electric meter fault cloud model, and obtaining qualitative cloud characteristics of the electric meter fault cloud model; and a step 4) of subdividing the K large fault classes into a plurality of small fault classes according to the qualitative cloud characteristics. Compared with the prior art, the method has the advantages of qualitative analysis and fine classification.

Description

technical field [0001] The invention relates to a smart meter fault classification method, in particular to a smart meter fault classification method based on cluster analysis and cloud model. Background technique [0002] The rapid development of computer and network technology has made the means of information acquisition and analysis increasingly advanced. Data mining has become an important tool for enterprises and institutions to study data laws. Data mining platforms are mainly divided into storage computing platforms and data mining algorithms. According to the characteristics of meter data, the fault data of smart electric energy meters is huge and complex, and there is no qualitative analysis method that can separate them according to fault types. There is an urgent need for a fault analysis and classification method from quantitative to qualitative. Contents of the invention [0003] The object of the present invention is to provide a smart meter fault classifica...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R35/04
CPCG01R35/04
Inventor 江剑峰朱彬若张垠朱铮王新刚顾臻翁素婷陈金涛盛青
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO
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