Overview

The real world is complex; complexity in the world generally arises from uncertainty in the form of ambiguity. Electric power systems are large, complex, geographically widely distributed systems and influenced by unexpected events. These facts make it difficult to effectively deal with many power system problems through strict mathematical approaches. Therefore, intelligent techniques such as expert systems, artificial neural networks, genetic algorithms and fuzzy logic have emerged in recent years in power systems as a complement to mathematical approaches and have proved to be effective when properly coupled. As the real world power system problems may neither fit the assumptions of a single technique nor be effectively solved by the strengths and capabilities of a single technique, it is now becoming apparent that the integration of various intelligent techniques is a very important way forward in the next generation of intelligent systems.

Traditional logic uses variables that have precise values, called crisp values. Fuzzy logic, on the other hand, attempts to model the impreciseness of human reasoning by representing uncertainty for the variables that are used by assignment of a set of values to the variable. Each value has a degree of membership of the set which represents the probability of the variable having that value [1]. A membership function identifies the degree of membership over the range of possible values, known as the universe of discourse . This function can be defined to represent an adjective, known as a linguistic value or fuzzy set , which...