Temporal Graph Convolutional Autoencoder based Fault Detection for Renewable Energy Applications - Arifeen, Murshedul; Petrovski, Andrei (IEEE International Conference on Industrial Cyber Physical Systems (Online), 2024)
Detecting faults in energy generation systems is a challenging task due to the complex nature of the system, measurement noise, and outliers. Recently, researchers have shown an increasing interest in using data-driven models that utilize sensor data for fault detection and diagnosis. However, the nonlinearities, spatial and temporal dependencies in timeseries sensor data make it difficult to develop an effective datadriven fault detection model. To address this issue, we propose an autoencoder model that uses a temporal graph convolutional layer to detect faults in the energy generation process. The proposed model has exceptional spatiotemporal feature learning capabilities, making it ideal for fault detection applications. In addition, we have included a data processing module to reduce noise and eliminate outliers from sensor data. We evaluated the model's performance using wind turbine blades and photovoltaic microgrid datasets. Experimental results have demonstrated that the proposed model outperforms other fault detection models based on graph convolutional autoencoders.