Composite system well-being analysis using sequential Monte Carlo simulation and fuzzy algorithm

Health, Margin and Risk states which in recent years are known as well-being reliability indices, provide a comprehensive adequacy assessment of bulk power system reliability studies. Conventional reliability information about power system operation only considered health and risk states which were not often adequate criteria both in system planning and utilization. It seems margin state as the intermediate condition between health and risk must be considered. Well-being method which is a approach to power system generation adequacy evaluation incorporates deterministic criteria in a probabilistic framework and provides system operating information in addition to risk assessment and can be evaluated using analytical techniques. The most important part of this approach is the algorithm for calculating the probability of each state. Besides, all system components, their behavior and their operational conditions such as transmission lines overloads and voltage drops should be considered in the calculations. In this context, this paper proposes a method to calculate more precise well-being indices using Monte Carlo simulation procedure and Fuzzy Logic algorithm while AC load flow is utilized for contingency analysis. The proposed method is then demonstrated on the RBTS to demonstrate the instance results.