Bayesian Learning of Causal Networks for Unsupervised Fault Diagnosis in Distributed Energy Systems

Distributed energy generation systems, key for producing electricity near usage points, are essential to meet the global electricity demand, leveraging diverse sources like renewables, traditional fuels, and industrial waste heat. Despite their high reliability, these systems are not immune to faults and failures. Such incidents can result in considerable downtime and reduced efficiency, underlining the need for effective fault detection and diagnosis techniques. Implementing these strategies is crucial not just for mitigating damage and preventing potential disasters, but also to maintain optimal performance levels. This paper introduces a novel methodology based on Bayesian graphical modeling for unsupervised fault diagnosis, focusing on organic Rankine cycle case study. It employs structural learning to discern unknown intervention points within a directed acyclic graph that models the power plant’s operations. By analyzing real-world data, the study demonstrates the effectiveness of this approach, pinpointing a subset of variables that could be implicated in specific faults.