Including Uncertainty in Health Analysis of Composite Power System Using Fuzzy Set Theory

Usually probabilistic methods are used in the conventional reliability assessment of power system. From an applied point of view, dealing with uncertainties of some parameters such as mean time to failure or repair and forecasted load, using probabilistic methods would be difficult; because acquisition of adequate historical data related to different parts of system to extract an appropriate probability distribution is difficult. The large amount of uncertainty is implicit in the estimate of system reliability because of insufficient failure data, variation in environmental conditions, etc. In addition, limits imposed by the operators on power system parameters, like line flows, bus voltages are crisps when dealing the deterministic technique, but in real situation these limits are no longer of a crisp nature and are considered as soft constraints. The power system parameters such as failure and repair rates used in the probabilistic models basically come from historical operation records, which are necessarily limited and subject to errors. There is considerable data uncertainty that exists in these parameters. Another important issue in reliability evaluation using contingency enumeration approach is the large number of possible system contingency states for a practical system. To investigate the credible outages, the fuzzy logic approach is developed to combine the conventional outage ranking method with fuzzy reasoning. In this paper a framework to evaluate health analysis of the composite power system by incorporating the fuzzy approach is suggested. The proposed methodology is tested on composite power systems to demonstrate the fuzzy logic based ranking of outages and impact of outages on power system health.