Power Abnormity Detection Method Based on Power Big Data Applying BRB and LSTM Network

doi:10.12204/j.issn.1000-7229.2021.08.005

Abstract

Abstract:

In order to reduce the operating cost of power companies, for non-technical loss (NTL), this paper proposes a belief rule-based (BRB) and long short-term memory (LSTM) based method for diagnosing the user’s electricity theft behavior. This method firstly extracts the power fluctuation coefficient and the burr width of the power consumption curve from the electric power dataset, and then the input pre-attribute conversion method for BRB anomaly feature is established. The final confidence is output by evidential reasoning (ER) method, and the confidence-rule base for NTL anomaly detection is established, thus, the labeled positive and negative training data sets with high robustness can be obtained automatically. Then, on this basis, a multi-LSTM network detection model is proposed to realize the effective extraction and detection of abnormal electrical features. The experimental results show that, compared with existing mainstream fault detection networks, the proposed method can better accurately diagnose the abnormal electrical behavior of users from the power big data.

Key words: power big data, abnormal non-technical loss (NTL) electricity behavior, long short-term memory (LSTM), belief rule-based (BRB), evidential reasoning (ER) method

CLC Number:

TM73
TP277

WAN Lei, CHEN Cheng, HUANG Wenjie, LU Tao, LIU Wei. Power Abnormity Detection Method Based on Power Big Data Applying BRB and LSTM Network[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(8): 38-45.

Figures/Tables 11

Fig.1 Abnormal power consumption curve

Fig.2 Overall flow chart

Fig.3 Structure of LSTM

Fig.4 Overall flow chart of NTL abnormal power consumption detection based on BRB and LSTM network

Table 1 Training sample set of electric power consumptionkW·h

Table 2 Test sample set of electric power consumption

Fig.5 Monthly power fluctuation coefficient of test samples

Fig.6 Monthly burr width coefficient of test sample

Table 3 Confidence-rule base after training

Table 4 Accuracy of the proposed method compared with other methods

Table 5 Experimental results of abnormality detection of power consumption data of state grid users in the first half of 2016[4]

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