Integrated Stochastic Approach for Instantaneous Energy Performance Analysis of Thermal Energy Systems

To ascertain energy availability and system performance, a comprehensive understanding of the systems’ degradation profile and impact on overall plant reliability is imperative. The current study presents an integrated Failure Mode and Effects Analysis (FMEA)–Markovian algorithm for reliability-based instantaneous energy performance prediction for thermal energy systems. The FMEA methodology is utilized to identify and categorize the various failure modes of the gas turbines, establishing a reliability pattern that informs overall system performance. Meanwhile, the Markovian algorithm discretizes the system into states based on its operational energy performance envelope. The algorithm predicts instantaneous energy performance according to upper and lower bounds criteria. This integrated methodology has been subjected to testing in three case studies, yielding results that demonstrate improved reliability and instantaneous energy performance prediction during system degradation. It was observed that after 14 years of operation, the likelihood of major failures increases to 79.6%, 88.7%, and 82.8%, with corresponding decreases in system performance reliability of 10.1%, 4.5%, and 7.8% for the Afam, Ibom, and Sapele gas turbine plants, respectively. Furthermore, the percentage of instantaneous mean power performance relative to the rated capacity is 37.9%, 35.1%, and 46.3% for the three gas turbine plants. These results indicate that the Sapele thermal power plant performs better relative to its rated capacity. Overall, this integrated methodology serves as a valuable tool for monitoring gas turbine engine health and predicting energy performance under varying operating conditions.