Nontechnical Loss and Outage Detection Using Fractional-Order Self-Synchronization Error-Based Fuzzy Petri Nets in Micro-Distribution Systems

Load management is a challenging issue in micro-distribution systems dealing with power utilities. To efficiently detect fraudulent and abnormal consumption, this paper proposes the use of fractional-order self-synchronization error-based Fuzzy Petri nets (FPNs) to detect nontechnical losses and outage events. Under the advanced metering infrastructure technique, the Sprott system is a feature extractor, which tracks the differences between profiled usages and irregular usages, such as illegal and fault events. Thus, fraudulent consumption, outages, and service restoration activities can be pointed out, randomly initiated, and terminated in a real-time application. Multiple FPNs-based making-decision systems are used to locate abnormalities. Computer simulations are conducted using an IEEE 30-bus power system and medium-scale micro-distribution systems to show the effectiveness of the proposed method.