Non-invasive household electric equipment online monitoring system and fault identification method

Abstract

The invention relates to the technical field of power data analysis. The invention discloses a non-invasive household electric equipment online monitoring system and a fault identification method. A non-invasive electrical signal acquisition device and a real-time power utilization information multivariate feature extraction system complete acquisition of waveform signals generated by household electric equipment and extraction of multivariate power utilization features. An autoregressive moving average model ARMA, a multi-objective optimization model and an LSTM classification system analyzeand process the multivariate power utilization features to obtain an abnormal probability and a normal probability of each currently running electric equipment or a line where the electric equipment is located under each multivariate time sequence power utilization feature vector, and finally, whether a fault occurs or not is judged by a joint judgment model according to a joint probability: whenthe joint abnormal probability is greater than the joint normal probability, the current running electric equipment or the line where the current running electric equipment is located has a fault. According to the invention, the technical problem that fault identification is difficult to carry out on household electric equipment according to signals containing various electric appliance componentsis solved, the fault identification cost is reduced, and the identification accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of power data analysis, in particular to a non-invasive online monitoring system and fault identification method for household electrical equipment. Background technique [0002] Statistics show that more than 70% of household fire accidents are caused by electrical causes, and the painful accident lessons have become a pain point for electric power practitioners. The failures caused by the deterioration of electrical performance will mostly cause device damage, short circuit, partial discharge, etc., resulting in abnormal load characteristics. If the household electricity load can be monitored in real time and the fault condition can be effectively identified, it can respond in time at the initial stage of the fault, cut off the power supply, give early warning, and avoid the occurrence or expansion of the accident. Traditional load monitoring needs to install a signal sampling device at the signal output ...

AI Technical Summary

Problems solved by technology

[0004] Household electrical equipment includes lighting appliances, cooking appliances, air conditioners, washing machines, etc. Each appliance has its own unique operating characteristics. Only the same electrical appliance operates, so the signal detected by the non-intrusive equipment often contains components of multiple electrical appliances, which is also one of the difficulties in identifying faults of electrical equipment

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