Non‐technical loss and power blackout detection under advanced metering infrastructure using a cooperative game based inference mechanism

Abstract

To efficiently detect non‐technical loss and power blackout in micro‐distribution systems, this study proposes using a cooperative game (CG) based inference mechanism under the advanced metering infrastructure technique. Fractional‐order Sprott system is designed to extract specific features between the profiled usages and the measurement usages in real time analysis. The fractional‐order dynamic errors are positive correlated with the changes in load usages, including normal conditions, electricity fraudulent events, and power blackout events. Then, multiple agents in a game and multiple CG based inference mechanisms are used to locate abnormalities in micro‐distribution systems. For energy management applications, the proposed inference mechanism can identify the 2.5–20% irregular usages during normal demand operations. In addition, it can also identify the large changes >20% in usages, while a micro‐distribution system is disconnected to operate in the islanded mode within a few hours. This function can address an outage occurrence and then quickly resume service using the service restoration strategy and distributed generations in a local grid. Using a medium‐scale micro‐distribution system, computer simulations are conducted to show the effectiveness of the proposed inference model.