Unsupervised Anomaly Detection of Shipboard Electrical Power Consumption Through GRU Autoencoder Model

Anomaly detection (AD) in modern electrical microgrids is critical for ensuring reliable and safe operation as well as in improving the operational and maintenance efficiency of the infrastructure. This is particularly true for on-board microgrids, given their inherent electrical weakness and predominantly isolated operation. This paper proposes a machine learning-based approach for performing anomaly detection (AD) in electrical power consumption of a large passenger ship. The developed AD method relies on a gate recurrent unit (GRU) autoencoder (AE) model trained and validated with a multivariate electrical power time series collected from a real-world vessel. The proposed GRU AE approach is compared with different AE models, training and testing all models on an unsupervised context, and their performance is evaluated using various metrics suitable for such an unsupervised context.