Thukaram, D and Khincha, HP and Vijaynarasimha, HP (2005) Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems. In: IEEE Transactions on Power Delivery, 20 (2). pp. 710-721.
This paper presents an artificial neural network (ANN) and support vector machine (SVM) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of support vector classifiers (SVCs) and feed forward neural networks (FFNNs). A practical 52 bus distribution system with loads is considered for studies, and the results presented show that the proposed approach of fault location gives accurate results in terms of the estimated fault location. Practical situations in distribution systems, such as protective devices placed only at the substation, all types of faults, and a wide range of varying short circuit levels, are considered for studies. The results demonstrate the feasibility of applying the proposed method in practical distribution system fault diagnosis.
|Item Type:||Journal Article|
|Additional Information:||Copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.|
|Keywords:||Artificial neural network;distribution systems;fault location;support vector machines|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||25 Aug 2008|
|Last Modified:||19 Sep 2010 04:18|
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