Intelligent electricity utilization anomaly detection method for non-technical loss

Abstract

The invention discloses an intelligent electricity utilization anomaly detection method for non-technical loss, and belongs to the technical field of power load analysis. The method includes the steps that (1) original data are preprocessed; (2) feature extraction is conducted on sample data; (3) samples are divided into the initial training samples and the optimization samples; (4) real-time data are sampled, and the sample features are extracted to form a test sample; (5) parameter optimization is conducted through a GA to determine the optical ELM parameter value; (6) anomaly detection is conducted by substitution of the optical ELM parameter value, a training sample and a test sample; (7) if the test time is an integer multiple of 72 hours, classification accuracy and the anomaly error detection rate are counted; if the anomaly error detection rate exceeds the set limit value, the step (8) is executed, and if not, the step (4) is executed; (8) the training sample of a user is updated and the step (5) is executed. The intelligent electricity utilization anomaly detection method for non-technical loss is definite in physical conception, clear in thought, easy and convenient to analyze and calculate, and capable of effectively solving the problem of online detection of non-technical loss of arbitrary electricity utilization loads.

Description

technical field [0001] The invention belongs to the technical field of power load analysis, and in particular relates to an intelligent power consumption anomaly detection method aimed at non-technical losses. Background technique [0002] Nontechnical loss (Nontechnical Loss, NTL) is a concept proposed relative to technical loss, usually refers to the electricity that has been transmitted to the user side but has not been priced, and the user stealing electricity on the distribution network side and a series of fraudulent use. related to electrical behavior. Today, non-technical losses have become an important factor affecting the power company's earnings. [0003] For non-technical losses, many methods for NTL detection have been proposed abroad, and the detection methods are very rich, including statistical methods, decision trees, artificial neural networks, data mining, knowledge discovery, and optimal path trees, etc., but they are all offline detection . Its histor...

AI Technical Summary

Problems solved by technology

At the same time, existing research focuses more on classification accuracy. However, in the case of a large sample base, the problem of high false detection rate cannot be ignored.

On-site verification consumes manpower and material resources, and the waste caused by false detection will also cause great losses when the sample base is large.

At the same time, the abnormal false detection rate is high, and the algorithm with poor adaptability to the environment is not easy to promote

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