PATTERN-ANALYSIS IN POWER-SYSTEM STATE ESTIMATION

In recent years, interest in the application of artificial intelligence technologies to power system operation, planning and design has grown rapidly. The application of non-symbolic techniques, particularly Artificial Neural Networks (ANNs), is a new area of research in this field. In this paper, intelligent systems for solving power system state estimation problems are investigated. A new framework for the solution of the topology determination, observability analysis and bad data processing tasks is proposed. Pattern analysis techniques have been developed to deal with noisy environments. An ANN for topology determination and a supervised learning algorithm for very large training sets, the Optimal Estimate Training 2 (OET2), are introduced. OET2 overcomes the major shortcomings of the back-propagation learning rule and can also he very useful for other problems. Power system network decomposition techniques are used to decrease the computational burden of the topology classifier training session. Tests using the IEEE 24- and 118-bus systems illustrate situations in which the existent tools for data processing fail.