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Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location. [ABSTRACT FROM AUTHOR]

Locating the faulty section of a high-impedance fault (HIF) is quite challenging for the underground distribution network of a power system. The complexity of the distribution network, such as branches, nonhomogenous lines, and HIF, contributes to the difficulties in locating the faulty section. In this paper, the shortest distance (SD) technique and a database approach have been proposed to determine the faulty section. A multiresolution analysis based on discrete wavelet transforms is chosen to extract the unique features from voltage signals during the HIF event. The output coefficients from the decomposition process is stored in a database and used as the input data for the SD algorithm. The first, second, and third level of detailed coefficients of the post-disturbance voltage signal were utilized for the identification of the faulty section using the proposed method. A ranking analysis was created to provide a number of possibilities of faulty section. In this paper, a 38-node underground distribution network system in a national grid in Malaysia was modeled using the PSCAD software. The proposed method was able to successfully determine the faulty section. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This study provides a thorough investigation of partial discharge (PD) activities in nanofluid insulation material consisting of different types of nanoparticles, which are conductive and semiconductive when subjected to high voltage stress is presented. Nanofluids have become a topic of interest because they can be an alternative to liquid insulation in electrical apparatus due to their promising dielectric strength and cooling ability. However, during in-service operation, PDs can occur between conductors in the insulation system. Therefore, this study presents the behavior of PDs within nanofluid dielectric materials consisting of conductive and semiconductive nanoparticles. The results show that there is an improvement in the PD resistance and a reduction in the tan delta of nanofluids at power frequency after the incorporation of conductive or semiconductive nanoparticles in the nanofluid oil. However, the most suitable concentration of conductive and semiconductive nanoparticles in the base fluid was found to be, respectively, 0.01 g/L and 1.0 g/L at PD inception and PD steady-state conditions. The clustering of nanoparticles in a nanofluid suspension due to PD activities is also discussed in this study. [ABSTRACT FROM AUTHOR]

In zinc oxide (ZnO) surge arresters, leakage current usually flows across the arrester under normal operating condition. Leakage current is one of the factors which contribute towards degradation of surge arresters and therefore, it is very important to monitor the condition of surge arrester. In this work, the behaviour of leakage current in a ZnO surge arrester during normal operation, under different voltage amplitudes, wetness and pollution conditions was analysed. An 11 kV surge arrester model in three-dimensional space was subjected to finite element analysis (FEA) to determine the leakage current under different conditions. The results from the FEA model were compared with the measurement results to validate the model that has been developed. From comparison between the measurement and simulation results, physical parameters of a surge arrester that influence the leakage current under different conditions of the surge arrester were identified from the model. Through this work, a better understanding of leakage current behaviour can be attained, which may help in condition monitoring analysis on surge arrester in electrical utilities. [ABSTRACT FROM AUTHOR]

Modeling of the partial discharge (PD) process allows a better understanding of the phenomena. In this paper, a simulation model for spherical cavities within a homogeneous dielectric material has been developed. The model is implemented using Finite Element Analysis (FEA) software in parallel with a mathematical package. This method provides many advantages over previous PD models because discharge events can be simulated dynamically and the electric field in the cavity can be calculated numerically. The model has been used to study the effect of different amplitudes and frequencies of the applied voltage and simulation results have been compared with experimental measurement results. It is found that certain model parameters are dependent on the applied stress and parameters that clearly affect PD activity can be readily identified, these parameters include; the electron detrapping time constant, the cavity surface conductivity, the initial electron generation rate and the extinction voltage. The influence of surface charge decay through conduction along the cavity wall on PD activity has also been studied. [ABSTRACT FROM AUTHOR]