A fault diagnosis method of Smart Grid based on rough sets combined with genetic algorithm and tabu search
Most of the existing methods of fault diagnosis in Smart Grid focus primarily on the information of the protective relays and switches. There is still some incomplete or uncertain information in the process of receiving data. Usually, relying only on the information may obtain wrong conclusions. Large numbers of methods have been applied in the fault diagnosis of power system. An improving accuracy of fault diagnosis method in Smart Grid is put forward in this paper using rough sets combined with genetic algorithm (GA) and Tabu search (TS). The reduction in continuous attributes and their value reduction are the major application of rough sets. The proposed algorithm can combine the parallel global searching capability of genetic algorithm with the local searching ability of Tabu search and significantly improve the efficiency of execution and ensure the optimal result. The effectiveness of the proposed algorithm has been demonstrated using Changchun south substation and its distribution grid. To validate the proposed approach adequately, simulation studies have also been carried out on the simulated Smart Grid model. A series of tests are conducted toward three fault categories: the single faults, the multiple faults, and the loss information faults. All the results demonstrate that the proposed method in this paper is better than the preceding algorithms.
fault diagnosis smart grid rough sets genetic algorithm tabu search