Assessment of Distribution System Feeder and its Reconfiguration using fuzzy adaptive evolutionary computing

Distribution system automation (DSA) is being carried out very seriously world over to enhance the reliability of the system and to minimize the huge losses that are occurring in the distribution system. With the fast-paced changing technologies in the distribution sector, the automation of distribution system is unavoidable. Feeder reconfiguration (FR) is one of the vital operations to be carried out in successful implementation of the DSA. FR can be varied so that the load is supplied at the cost of possible minimal line losses, increased system security and enhanced power quality. Several attempts have been made in the past to obtain an optimal feeder configuration for minimizing losses in distribution systems. Feeder reconfiguration (FR) is an important sub-problem of the overall distribution system automation process. To get the optimized system configuration, first step is to detect whether the system is abnormal enough to have any other configuration from present case. To serve this purpose, S-difference criterion is proposed to detect whether the system is making transition from normal to abnormal state of operation. After detecting the abnormality, the situation arises to find whether the line through which the power is going to be rerouted can sustain the increment. This is identified by calculating the availability transfer capability (ATC) of the lines in the system. Linear and modified ATC has been proposed in this paper to serve the required purpose. After assessing the state of the system, its reconfiguration is done by fuzzy adaptation of evolutionary computing, to get the optimized system performance. Fuzzy adaptation has been introduced to take into account the grey area between various parameters considered and the obtained result is supposed to satisfy all the constraints to the optimum degree of fuzzy membership function. Vellore 75 bus real distribution system has been used as a test system for simulation purposes.