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BRB and LSTM model-based power big data electricity consumption anomaly detection method and device

An electrical anomaly detection and big data technology, applied in data processing applications, reasoning methods, character and pattern recognition, etc., can solve the problems of time-consuming, high cost, etc.

Pending Publication Date: 2021-06-08
WUHAN INSTITUTE OF TECHNOLOGY
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However, in practical applications, manual calibration is usually used for data calibration, which is time-consuming and expensive

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  • BRB and LSTM model-based power big data electricity consumption anomaly detection method and device
  • BRB and LSTM model-based power big data electricity consumption anomaly detection method and device
  • BRB and LSTM model-based power big data electricity consumption anomaly detection method and device

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[0047] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0048]In order to improve the efficiency of data calibration, the present invention firstly introduces the Belief rule-based (BRB) method into the anomaly detection of big electric power data, and proposes a method based on the fluctuation characteristics of user electricity consumption and the abnormality of electricity consumption curve. The confidence rule reasoning method of the feature is used to detect the abnormal power consumption of the user, and automatically and quickly obtain the positive and negative sample sets with high reliability and robustness. Then, based on this sample set, the ...

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Abstract

The invention discloses a BRB and LSTM model-based power big data electricity consumption anomaly detection method and device. The method specifically comprises the following steps: extracting user electricity consumption fluctuation features and user electricity consumption curve anomaly features from electricity consumption big data; establishing a confidence rule reasoning BRB system, and performing confidence conversion on the electric quantity fluctuation coefficient and the total burr width; according to a belief rule base in a belief rule reasoning BRB system, comparing the converted confidence coefficients by adopting an evidence reasoning ER algorithm to obtain the trust degree of each reference value in a user non-technical loss NTL anomaly output result; calibrating non-technical loss (NTL) abnormal power utilization of the user; on the basis of calibration data, building a long short term memory (LSTM) model, using the LSTM model to effectively extract and detect abnormal power consumption characteristics, and finally, diagnosing an NTL abnormal condition accurately. According to the invention, the abnormal power consumption condition can be effectively identified.

Description

technical field [0001] The invention relates to the technical field of metering data detection, and in particular to a method and device for detecting abnormality of power consumption in large electric power data based on BRB and LSTM models. Background technique [0002] In recent years, with the rise and popularization of "Smart Grid", more and more attention has been paid to the transmission and distribution losses during its operation, and the transmission and distribution losses can be roughly divided into technical losses (technical losses, TL) and non-technical losses. Loss (non-technical loss, NTL) two categories. Among them, the serious non-technical loss, that is, the abnormal electricity stealing behavior of users, has brought huge economic losses to the power grid industry. Compared with the proportion of national electricity consumption of NTL in countries such as India and Brazil, my country's NTL is relatively low, but my country's overall electricity demand ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N5/04G06Q50/06
CPCG06N5/04G06Q50/06G06N3/044G06F18/2433G06F18/214Y02D10/00
Inventor 刘威陈成卢涛万磊
Owner WUHAN INSTITUTE OF TECHNOLOGY