Power Net Work Fault Diagnosis Based On Information Of SCADA And RMS

With the development of economic and the progress in electric power market the need for stable supply of power increases considerably. The automation of fault section diagnosis and restoration is also required in power systems operation so as to diminish the outage time and ensure stable supply of electric power for the customers. To identify fault sections in power systems by using information on operation of relays and circuit breakers has been an urgent problem that power enterprises must tackle. This paper is dedicated to solve this problem.The basic principle of power system relaying is to isolate the fault part from the whole, and the effective information that contributes to judging certain element's state is implicated in its adjacent area only. This is the fundamental principle of fault diagnosis. This paper first states the feasibility and key idea of fault diagnosis, then used some appropriate methods to represent the power system in computer. Having acquired each equipment's adjacent information, we use Artificial Intelligence methods to accomplish the diagnosis task.The frameworks of employing Expert System Artificial Neural Network and Genetic Algorithm three typical Artificial Intelligence methods to realize power network fault diagnosis are discussed in detail, the author also demonstrated these exercises in such aspects as knowledge-acquisition knowledge-storage and knowledge-exertion, examples of each methods are included. Having analyzed the characteristics of three typical AI methods, this paper proposes an integrate approach to solve the problem. The key idea of this method is to use three methods reasonably seeing to the condition of fault information. When pollution occurs we use different AI methods to infer in both directions to deal with uncertainties imposed on fault section diagnosis of power systems due to noises and loss of the information. Experimental studies for real power systems reveal usefulness of the proposed technique to diagnose fault that have uncertainty.