PN Inference Based Autonomous Sequential Restoration of Distribution System Under Natural Disaster

Abstract

Potential challenge during a disaster is the wide range of disturbance caused to the power system network. The article attempts to develop a systematic procedure to progressively restore the network by recognizing the alive network. During disaster there is manpower shortage and poor road connectivity around the affected area. Hence, an autonomous priority-based restoration policy is required for critical load restoration. The main idea of the proposed article is to develop an autonomous system, which is decision-based restoration system by identifying the network component still available after disaster followed by reconfiguration confirming the proposed load pickup procedure. Because of the inherent capability of implementing the condition-based state transition of switches and parallel processing, a petri net (PN) based methodology has been used in the article to integrate alive parts, which further assists in identifying the best switching schedule of circuit breakers for autonomous load restoration. The primary contribution of the article is to develop an encapsulated algorithm using PN as a modeling platform of components as well as empowering the same by introducing the game-theory-based concept in the formulation of different microgrids integrated with distributed generators during extreme weather events. The verification of effective autonomous restoration is then realized by using the available delivery capability and design verification of the framed microgrids, and the comparison with different restoration strategies adopted by other authors. The studies are carried out with the IEEE 34, 37, and 123-bus feeder test systems for implementation and validation of the proposed article along with the comparison with other published works.