Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images

Sukanya Patra, Nicolas Sournac, Souhaib Ben Taieb

Concentrated Solar Power (CSP) plants store energy by heating a storage medium with an array of mirrors that focus sunlight onto solar receivers atop a central tower. Operating at extreme temperatures exposes solar receivers to risks such as freezing, deformation, and corrosion. These problems can cause operational failures, leading to downtime or power generation interruptions, and potentially extensive equipment damage if not promptly identified, resulting in high costs. We study the problem of anomaly detection (AD) in sequences of thermal images collected over a span of one year from an operational CSP plant. These images are captured at irregular intervals ranging from one to five minutes throughout the day by infrared cameras mounted on solar receivers. Our goal is to develop an AD method to extract useful representations from high-dimensional thermal images, that is also robust to the temporal features of the data. This includes managing irregular intervals with temporal dependence between images, as well as accommodating non-stationarity due to a strong daily seasonal pattern. An additional challenge includes the coexistence of low-temperature anomalies resembling low-temperature normal images from the start and the end of the operational cycle alongside high-temperature anomalies. We first evaluate state-of-the-art deep anomaly detection methods for their performance in deriving meaningful image representations. Then, we introduce a forecasting-based AD method that predicts future thermal images from past sequences and timestamps via a deep sequence model. This method effectively captures specific temporal data features and distinguishes between difficult-to-detect temperature-based anomalies. Our experiments demonstrate the effectiveness of our approach compared to multiple SOTA baselines across multiple evaluation metrics. We have also successfully deployed our solution on six months of unseen data, providing critical insights to our industry partner for the maintenance of the CSP plant. As our dataset is confidential, we will release a simulated dataset and code.