Intelligent Anomaly Detection Method of Gateway Electrical Energy Metering Devices using Deep Learning

Accurate anomaly detection of gateway electrical energy metering device is important for maintenance and operations in the power systems. Traditionally, anomaly detection was typically performed manually through the analysis of the collected energy information. However, the manual process is time-consuming and labor-intensive. In this condition, this paper proposes a hybrid deep-learning model, which integrates Stacked Autoencoder (SAE) and Long Short-Term Memory (LSTM), for intelligently detecting the abnormal events of gateway electrical energy metering device. The proposed model named SAE-LSTM model, first uses SAE to extract deep latent features of three-phase voltage data collected from the gateway electrical energy metering device, and then adopts LSTM for separating the abnormal events based on the extracted deep latent features. The SAE-LSTM model, can effectively highlight the temporal information of the electrical data, thereby enhancing the accuracy of anomaly detection. The simulation experiments verify the advantages of the SAE-LSTM model in anomaly detection under different signal-to-noise ratios. The experimental results of real datasets demonstrate that it is suitable for anomaly detection of gateway electrical energy metering devices in practical scenarios.

@article{Zhang2023,
  title     = {Intelligent Anomaly Detection Method of Gateway Electrical Energy Metering Devices using Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Lihua Zhang and Xu Chen and Chao Zhang and Lingxuan Zhang and Binghang Zou},
  doi       = {10.14569/IJACSA.2023.0140793},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140793}
}