Knowledge-based neural network for voltage contingency selection and ranking

Abstract

Voltage contingency screening and ranking is performed to choose the contingencies that cause the worst voltage problems. A knowledge-based conceptual neural network (KBCNN) is developed for fast voltage contingency selection and ranking. A recognised shortcoming of the ANN based approach is that the problem solving knowledge of ANN is represented in the connection weights, and hence is difficult for a human user to comprehend. One way to provide an understanding of the behaviour of neural networks is to extract rules that can be provided to the users. The rules extracted are used to build a knowledge-based connectionist network for learning and revision of rules. The knowledge-based neural network is applied for voltage contingency selection and ranking in an IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the KBCNN gives accurate selection and ranking for unknown patterns. At the same time, the system user is able to validate the output of the ANN under all possible input conditions. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.