Search

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]

In high-voltage equipment insulation, multiple partial discharge (PD) sources may exist at the same time. Therefore, it is important to identify PDs from different PD sources under noisy condition in insulations, with the highest accuracy. Although many studies on classifying different PD types in insulation have been performed, some signal processing methods have not been used in the past for this application. Thus, in this work, Cepstrum analysis on PD signals combined with artificial neural network (ANN) is proposed to classify the PD types from different PD sources simultaneously under noisy condition. Measurement data from different sources of artificial PD signals were recorded from insulation materials. Feature extractions were performed on the recorded signals, including Cepstrum analysis, discrete wavelet transform, discrete Fourier transform, and wavelet packet transform for comparison between the different methods. The features extracted were used to train the ANN. To investigate the classification accuracy under noisy signals, the remaining data were corrupted with artificial noise. The noisy data were classified using the ANN, which had been trained by noise-free PD signals. It is found that Cepstrum-ANN yields the highest classification accuracy for noisy PD signals than the other methods tested. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [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]

An enhanced fault location method based on voltage sag profiles for distribution networks is presented in this paper. In other previous methods, only a single measurement has been used. The proposed method can be used with any number of measurements in the network, making it more general. A new ranking approach that addresses multiple possibilities of the faulted section is proposed. Different case studies with various numbers of measurements are performed on a large 11-kV network with the main feeder consisting of 42 buses to validate the method. The test results show that there is an improvement in terms of accuracy in detecting the faulted section in the first attempt for each additional measurement. Therefore, by utilizing the average value of each measurement, a better accuracy of fault location can be achieved. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]