Distribution Feeder Reconfiguration for Load Balancing and Service Restoration by Using G-Nets Inference Mechanism

Abstract

In this paper, the customer information in customer information systems (CIS) and information of customer and distribution transformers in outage management information systems (OMIS) in Taiwan Power Company (Taipower), are used to determine the daily load patterns of service areas, sectionalizing switches, distribution feeders, and main transformers. During system normal operation conditions, the feeder reconfiguration for load balancing among distribution feeders is obtained by the G-Nets inference mechanism to enhance the operation performance of distribution systems. For distribution contingencies, such as feeder overloading and/or short-circuit fault, the G-Nets inference mechanism with operation rules is applied to derive the optimal switching operation decision for service restoration to perform the optimal load transfer among distribution feeders after the fault has been identified and isolated. To determine the effectiveness of the proposed methodology, a practical Taiwan power distribution system with daily load patterns derived by load survey study is selected to perform the computer simulation. It is found that the G-Nets inference mechanism approach can enhance the solution process of fault restoration with proper load transfer and improve feeder load balancing for distribution systems by considering the load characteristics of the service customers.