Efficient Fault Diagnosis Method of Electric Power Information System based on Rough Set Theory Knowledge Representation System

The Electric Power Information System is an extremely important support for the smooth operation of the power grid. Accurately assessing the operating status of the information system and performing fault diagnosis in a timely manner has always been an important issue for the industry. In this paper, we propose an efficient diagnosis method for Electric Power Information System faults. First, establish knowledge representation systems and analytic hierarchy model for the evaluation indicators of the information system, calculate the relative importance of the evaluation indicators and arrange them in descending order, and then construct the attribute indicators set according to the importance of the attribute indicators. Reduce the evaluation index number by rough set theory, get the smallest index subset that can accurately judge whether the information system is faulty, so as to improve the efficiency and accuracy of information system fault diagnosis. Finally, according to the minimum index subset and the index weight, calculate the operation status of the information system quantitatively and make the diagnosis finally. The paper describes in detail the implementation steps of an efficient fault diagnosis method. The method is simulated and verified with an information system as a test object. The results indicated that the proposed of method measure the operating state of the system accurately, perform fault diagnosis quickly, improve the efficiency of troubleshooting and has better practical value.