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

Cunyu Long; Jieqiong Han; Wenjing Fan; Bin Ma; Yan Yang

doi:10.1117/12.3029111

22 May 2024 Abnormal time sequence identification algorithm for power measurement devices based on variational autoencoder and support vector machine

Cunyu Long, Jieqiong Han, Wenjing Fan, Bin Ma, Yan Yang

Author Affiliations +

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131761Y (2024) https://doi.org/10.1117/12.3029111
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cunyu Long, Jieqiong Han, Wenjing Fan, Bin Ma, and Yan Yang "Abnormal time sequence identification algorithm for power measurement devices based on variational autoencoder and support vector machine", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131761Y (22 May 2024); https://doi.org/10.1117/12.3029111

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KEYWORDS

Data modeling

Reconstruction algorithms

Support vector machines

Instrument modeling

Evolutionary algorithms

Education and training

Measurement devices

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