Operational optimization concept with integrated fault information for thermal energy systems

Kurzfassung

Fault Detection and Diagnosis (FDD) has received more research attention as scheme for identifying anomalies, assisting decision making and enhancing operational efficiency, safety, and reliability. While current research focuses on development of fault detection algorithms, limited attention has been given to how such information can be utilized for operational and economic decision-making during the presence of faults. Traditional decision-making approaches heavily rely on human intervention, introducing risks of misinterpretation, personal judgements, and delays. Therefore, this paper proposes a conceptual framework that integrates FDD into a constrained optimization problem to support optimal operation under faulty states. First, synthetic experiments are conducted using Modelica-based models focusing on thermal energy system applications, where failure modes such as fouling, working fluid leakage, and control malfunction are studied. Fault classification is performed to determine the fault type from dynamic data. Subsequently, root cause analysis is conducted to identify the underlying mechanisms, and the corresponding mitigation actions are derived and incorporated as additional constraints within the optimization problem. This work outlines a structured approach for fault management by bridging FDD scheme with constrained optimization.