A MODULAR METHODOLOGY FOR FAST FAULT-DETECTION AND CLASSIFICATION IN POWER-SYSTEMS

This paper presents a modular yet integrated approach to the problem o f fast fault detection and classification. Although the specific appli cation example studied here is a power system, the method would be app licable to arbitrary dynamic systems. The approach is quite flexible i n the sense that it can be model-based or model-free. In the model-fre e case, we emphasize the use of concepts from signal processing and wa velet theory to create fast and sensitive fault indicators. If a model is available then conventionally generated residuals can serve as fau lt indicators, The indicators can then be analyzed by standard statist ical hypothesis testing or by artificial neural networks to create int elligent decision rules. After a detection, the fault indicator is pro cessed by a Kohonen network to classify the fault. The approach descri bed here is expected to be of wide applicability. Results of computer experiments with simulated faulty transmission lines are included.