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Selecting proper features to identify the root cause is a critical step in distribution fault diagnosis. Power engineers usually select features based on experience. However, engineers cannot be familiar with every local system, especially in fast growing regions. With the advancing information technologies and more powerful sensors, utilities can collect much more data on their systems than before. The phenomenon will be even more substantial for the anticipating Smart Grid environments. To help power engineers select features based on the massive data collected, this paper reviews two popular feature selection methods: 1) hypothesis test, 2) stepwise regression, and introduces another two: 3) stepwise selection by Akaike's Information Criterion, and 4) LASSO/ALASSO. These four methods are compared in terms of their model requirements, data assumptions, and computational cost. With real-world datasets from Progress Energy Carolinas, this paper also evaluates these methods and compares fault diagnosis performance by accuracy, probability of detection and false alarm ratio. This paper discusses the advantages and limitations of each method for distribution fault diagnosis as well. [ABSTRACT FROM AUTHOR]

Power distribution systems play an important role in modern society. When distribution system outages occur, fast and proper restorations are crucial to improve the quality of services and customer satisfaction. Proper usages of outage root cause identification tools are often essential for effective outage restorations. This paper reports on the investigation and results of two popular classification methods: logistic regression (LR) and artificial neural network (ANN) applied on power distribution fault cause identification. LR is seldom used in power distribution fault diagnosis, while ANN has been extensively used in power system reliability researches. This paper discusses the practical application problems, including data insufficiency, imbalanced data constitution, and threshold setting that are often faced in power distribution fault cause identification problems. Two major distribution fault types, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques. [ABSTRACT FROM AUTHOR]

Power distribution systems have been significantly affected by many fault causing events. Effective outage cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real- world data often degrades the outage cause identification performance. In this paper, artificial immune recognition system (AIRS), an immune-inspired algorithm for supervised classification task is applied to the Duke Energy outage data for outage cause identification using three major causes (tree, animal, and lightning) as prototypes. The performance of AIRS on these real-world imbalanced data is compared with an artificial neural network (ANN). The results show that AIRS can greatly improve the performance by as much as 163% when the data are imbalanced and achieve comparable performance with ANN for relatively balanced data. [ABSTRACT FROM AUTHOR]