Abnormal time sequence identification algorithm for power measurement devices based on variational autoencoder and support vector machine

Abstract

When identifying abnormal timing of power metering devices, the identification effect is poor due to the diverse attributes of the original power metering device state data. Therefore, a research on abnormal timing identification algorithm of power metering devices based on variational autoencoder and support vector machine is proposed. A VAE-LSTM-DTW model was constructed with a variational autoencoder as the core, which can be mainly divided into two parts. The reconstruction model is composed of a VAE network improved by LSTM, which is responsible for generating the time series reconstruction data of the power metering device corresponding to the input data. The evaluation model is responsible for comparing the reconstruction effect of the model on the input power metering device status data through DTW and algorithm, and performing anomaly detection accordingly. When identifying abnormal time series, support vector machines are used to match and identify the abnormal features of the operating state data of individual power metering devices. In the test results, the identification accuracy of the design algorithm is stable at above 0.85, the recall rate is stable at above 0.80, and the F1 score is stable at above 0.90.