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Markov Chain–Based Reliability Modeling and Block Diagram Framework for Performance Evaluation of Electrical Power Generation Systems Under Uncertainty
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Keywords
Markov Chain modeling
Reliability Block Diagram
Power system reliability
Stochastic modeling
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Abstract
The increasing complexity of modern electrical power generation systems, driven by renewable integration, deregulation, and distributed energy resources, has intensified the need for robust reliability evaluation frameworks capable of capturing stochastic behavior under uncertainty. This study proposes a Markov Chain–based reliability modeling approach integrated with a Reliability Block Diagram (RBD) framework for comprehensive performance assessment of electrical power generation systems. The proposed methodology combines probabilistic state-transition modeling with structural system decomposition to evaluate system-level reliability indices such as availability, failure probability, and expected energy not supplied.
Traditional deterministic reliability approaches are insufficient for systems characterized by renewable intermittency, component aging, and dynamic load variability. Therefore, stochastic modeling techniques such as Markov processes provide a mathematically rigorous mechanism to represent state transitions between operational, degraded, and failed conditions. Additionally, RBD structures enhance interpretability by mapping system configurations into series-parallel reliability relationships. The integration of these two methods enables multi-level reliability assessment from component to system scale.
The study builds upon established reliability frameworks in power systems and hybrid energy networks, particularly highlighting recent advancements in probabilistic evaluation and microgrid reliability modeling (Adefarati and Bansal, 2019). The proposed framework is applicable to both centralized and distributed generation systems, including renewable-integrated grids and hybrid storage-supported configurations. Comparative analysis with classical approaches demonstrates improved accuracy in failure prediction and system resilience estimation.
Overall, the research contributes a unified analytical framework that enhances decision-making in power system planning, operation, and resilience optimization under uncertainty.
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