Fault Diagnosis for Power Grid Systems Based on Rough Set and Bayesian Network
Baoyi Wang, Chongchong Liu, Shaomin Zhang · Advances in computer science research · 2015
In terms of the uncertainties and incompleteness of alarm information in power grid fault diagnosis, this paper proposes a fault diagnosis method based on rough set combined with Bayesian network.Using the ability of rough set to reduce knowledge and process indeterminate information and mine fault information hierarchically, using the attribute reducing method based on cognizable matrix and information entropy, the optimal attribute reduction combination is extracted.Finally, by means of the reduction decision table formed by optimal attribute reduction combination, the Bayesian network model is built for parallel reasoning of each region, and the nodal probability is trained to achieve fault diagnosis.The experiment proves that this method can diagnose the fault rapidly and accurately, and has strong fault tolerance and adaptability.