CN112381130A - Cluster analysis-based power distribution room multivariate data anomaly detection method - Google Patents
Cluster analysis-based power distribution room multivariate data anomaly detection method Download PDFInfo
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Abstract
The invention discloses a clustering analysis-based power distribution room multivariate data anomaly detection method, which comprises the following steps of: step 1, collecting normal monitoring data samples of a power distribution room, and standardizing the data samples; step 2, generating a normal clustering cluster for the standardized data by adopting a clustering algorithm; step 3, collecting monitoring data of the power distribution room in real time, and applying a time sliding window model to the real-time monitoring data; step 4, obtaining a candidate abnormal data set D corresponding to each time sliding window model; step 5, judging a data point D in the candidate abnormal data set D to judge whether the data point D belongs to a normal cluster; step 6.1, if the data belongs to the data, judging the next data point d + 1; step 6.21, if the data does not belong to the abnormal data set, obtaining an abnormal data point a, and adding the data point a into an abnormal data set Q; and 6.22, judging the abnormal mode of the abnormal data point a. The invention can monitor the abnormal state of the power equipment according to the multivariate data collected on line.
Description
Technical Field
The invention relates to a clustering analysis-based power distribution room multivariate data anomaly detection method used in the field of automatic monitoring of smart power grids.
Background
Monitoring of the operating state of the electrical equipment is critical to ensure safe operation of the power grid. At present, a power grid company mainly adopts a manual inspection and live detection mode to inspect power equipment. However, this approach has two problems: 1) the manual inspection has large workload, low efficiency and high cost, which leads to heavy workload of inspection personnel and large inspection pressure, and the contradiction between the rapid increase of the power grid scale and the configuration of equipment operation and maintenance personnel is increasingly prominent 2) the manual inspection has inspection blind areas, and the operation and maintenance personnel can not realize all-weather, all-time and all-round inspection. In recent years, in order to solve the problems of operation inspection of a power distribution room, an online monitoring system and an inspection robot are researched and applied more, but multivariate data obtained by online monitoring and collection are difficult to be effectively utilized, only unrelated data sets can be formed, and the online monitoring system and the inspection robot cannot be practically applied to state monitoring and state prediction of power equipment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a clustering analysis-based power distribution room multivariate data anomaly detection method which can monitor the state anomaly of power equipment according to online collected multivariate data.
One technical scheme for achieving the above purpose is as follows: a power distribution room multivariate data anomaly detection method based on cluster analysis comprises the following steps:
step 1, collecting normal monitoring data samples of a power distribution room, and standardizing the data samples;
step 2, generating a normal clustering cluster for the standardized data by adopting a clustering algorithm;
step 3, collecting monitoring data of the power distribution room in real time, and applying a time sliding window model to the real-time monitoring data;
step 4, obtaining a candidate abnormal data set D corresponding to each time sliding window model;
step 5, judging a data point D in the candidate abnormal data set D to judge whether the data point D belongs to a normal cluster;
step 6.1, if the data belongs to the data, judging the next data point d + 1;
step 6.21, if the data does not belong to the abnormal data set, obtaining an abnormal data point a, and adding the data point a into an abnormal data set Q;
and 6.22, judging the abnormal mode of the abnormal data point a.
Furthermore, the data types of the power distribution room normal monitoring data samples are partial discharge signals, infrared signals, temperature data and humidity data respectively.
Further, the method for judging the abnormal pattern of the abnormal data point a in step 6.22 is manual judgment or machine learning judgment by introducing an expert database.
The invention relates to a power distribution room multivariate data anomaly detection method based on cluster analysis, which mainly utilizes a time sequence sliding window to establish a data set for multidimensional online monitoring data flow, establishes an anomaly detection model of multivariate characteristic quantity data points based on an unsupervised k-means clustering method to judge whether anomaly occurs or not, realizes anomaly detection of multidimensional state quantity, and identifies an anomaly mode and occurrence time of the multidimensional state quantity. The integrated sensing terminal is implanted into the algorithm, so that not only can the abnormal changes of the level transition and trend of the state parameters such as the temperature, the partial discharge and the sound of the equipment be detected, but also the abnormal changes of the correlation among the state quantities such as the ambient temperature, the humidity, the temperature, the sound and the partial discharge of the equipment can be detected.
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Fig. 1 is a flowchart of a power distribution room multivariate data anomaly detection method based on cluster analysis according to the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made by specific examples:
referring to fig. 1, a power distribution room multivariate data anomaly detection method based on cluster analysis is characterized by comprising the following steps:
step 1, collecting normal monitoring data samples of a power distribution room, and standardizing the data samples;
step 2, generating a normal clustering cluster for the standardized data by adopting a clustering algorithm;
step 3, collecting monitoring data of the power distribution room in real time, and applying a time sliding window model to the real-time monitoring data;
step 4, obtaining a candidate abnormal data set D corresponding to each time sliding window model;
step 5, judging a data point D in the candidate abnormal data set D to judge whether the data point D belongs to a normal cluster;
step 6.1, if the data belongs to the data, judging the next data point d + 1;
step 6.21, if the data does not belong to the abnormal data set, obtaining an abnormal data point a, and adding the data point a into an abnormal data set Q;
and 6.22, judging the abnormal mode of the abnormal data point a. The method for judging the abnormal mode of the abnormal data point a is manual judgment or machine learning judgment by introducing an expert database.
The data types of the normal monitoring data samples of the power distribution room are partial discharge signals, infrared signals, temperature data and humidity data.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (3)
1. A power distribution room multivariate data anomaly detection method based on cluster analysis is characterized by comprising the following steps:
step 1, collecting normal monitoring data samples of a power distribution room, and standardizing the data samples;
step 2, generating a normal clustering cluster for the standardized data by adopting a clustering algorithm;
step 3, collecting monitoring data of the power distribution room in real time, and applying a time sliding window model to the real-time monitoring data;
step 4, obtaining a candidate abnormal data set D corresponding to each time sliding window model;
step 5, judging a data point D in the candidate abnormal data set D to judge whether the data point D belongs to a normal cluster;
step 6.1, if the data belongs to the data, judging the next data point d + 1;
step 6.21, if the data does not belong to the abnormal data set, obtaining an abnormal data point a, and adding the data point a into an abnormal data set Q;
and 6.22, judging the abnormal mode of the abnormal data point a.
2. The cluster analysis-based power distribution room multivariate data anomaly detection method according to claim 1, wherein the data types of the power distribution room normal monitoring data samples are partial discharge signals, infrared signals, temperature data and humidity data respectively.
3. The method for detecting the multivariate data abnormality of the power distribution room based on the cluster analysis as claimed in claim 1, wherein the method for judging the abnormal pattern of the abnormal data points a in step 6.22 is manual judgment or machine learning judgment with an expert database.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112990329A (en) * | 2021-03-26 | 2021-06-18 | 清华大学 | System abnormity diagnosis method and device |
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- 2020-11-10 CN CN202011246416.3A patent/CN112381130A/en active Pending
Patent Citations (4)
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| CN101908065A (en) * | 2010-07-27 | 2010-12-08 | 浙江大学 | On-line attribute abnormal point detecting method for supporting dynamic update |
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