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Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100 % accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location.

A surge arrester on transmission line is used to deflect lightning surge to ground, avoiding power systems from destruction. During normal condition, there is a leakage current flow through surge arresters where it is one of the main causes of surge arrester humiliation. Thus, monitoring the surge arrester condition is very significant. In this work, the effect of several parameters in a 11kV zinc oxide surge arrester design based on leakage current distribution has been studied. A comparison is made between measurements and modelling in order to validate the model that has been developed using finite element analysis. This work found that the benefit of using FEA-based model where the design parameters that affect the leakage current can be identified. Through this study, a well knowledge of leakage current can be achieved and also help in zinc oxide surge arrester design.