This dissertation describes an on-line, non-intrusive, classification system for identifying and reporting normal and abnormal power system events occurring on a distribution feeder based on their underlying cause, using signals acquired at the distribution substation. The event classification system extracts features from acquired signals using signal processing and shape analysis techniques. It then analyzes features and classifies events based on their cause using a fuzzy logic expert system based classifier. The classification system also extracts and reports parameters to assist utilities in locating faulty components. A detailed illustration of the classifier design process is presented.
Power distribution system event classification problem is shown to be a large scale classification problem. The reasoning behind the choice of a fuzzy logic based hierarchical expert system classifier to solve this problem is explained in detail. The fuzzy logic based expert system classifier uses generic features, shape based features and event specific features extracted from acquired signals. The design of feature extractors for each of these feature categories is explained. A new, fuzzy logic based, modified Dynamic Time Warping (DTW) algorithm was developed for extracting shape based features. Design of event specific feature extractors for capacitor problems, arcing and overcurrent events are discussed in detail. The fuzzy logic based hierarchical expert system classifier required a new fuzzy inference engine that could efficiently handle a large number of rules and rule chaining. A new fuzzy inference engine was designed for this purpose and the design process is explained in detail. To avoid information overload, an intelligent reporting framework that processes raw classification information generated by the fuzzy classifier and reports events of interest in a timely and user friendly manner was developed.
Finally, performance studies were carried out to validate the performance of the designed fuzzy logic based expert system classifier and the intelligent reporting system. The data needed to design and validate the classification system were obtained through the Distribution Fault Anticipation (DFA) data collection plat- form developed by Power System Automation Laboratory (PSAL) at Texas A&M University, sponsored by the Electric Power Research Institute (EPRI) and multiple partner utilities
