Rule-expert knowledge-based petri network approach for distribution system temperature adaptive feeder reconfiguration switching operation decision reasoning

This paper refers to decisions about transferring loads among distribution feeders in switching operations in order to enhance operating performance by combining the daily load characteristics and the variations among customers due to temperature increases using a switching operation decision inference mechanism, based on a rule-expert knowledge-based Petri network (RKBPN) method. Typical customer load patterns obtained in a load survey study are considered to determine the daily load profiles of each distribution feeder, to represent the load behavior more accurately. The current flows of the line switches and distribution feeders are determined by load flow analysis over a single day. A practical distribution system with seven feeders and daily load profiles with temperature sensitivity is selected for performing computer simulations, to demonstrate the effectiveness of the presented methodology. The proposed inference mechanism for reconfiguring the feeder based on the RKBPN approach enhances feeder reconfiguration to promote system contingency and improves load balance by considering the load characteristics of the customers served