# Fault Classification and Localization Scheme for Power Distribution Network
## 1 Introduction
An adaptive protection scheme based on ANN and WT for fault detection was proposed in [10]. The scheme produced good results; however, fault location was not considered. In [11], a technique based on stationary wavelet transform (SWT) and support vector machine (SVM) was proposed. The technique used SWT for signal decomposition and feature extraction, while the SVM scheme was used for fault classification and detection. Fault location is another important aspect to consider when designing a protection scheme. Fault location schemes give an indication of where the fault has occurred along the power distribution line, resulting in a much quicker restoration time. In [12], a technique based on ANN and a deterministic approach (DA) to locate the fault in a distribution line is proposed. However, it is reported in [12] that the estimation fault error is high. Fault location schemes were proposed in [13; 14]. These schemes use the ANN technique to estimate the fault position and WT to extract statistical features to train and test the ANN location scheme. In [15], the authors used DWT and wavelet fuzzy neural network (WFNN) to estimate the fault position. DWT is used to extract statistical features from the fault signal, and WFNN is used to determine the fault location. An intelligent fault location scheme based on wavelet packet transform (WPT) and back propagation neural network (BPNN) techniques was proposed [16]. The scheme in [16] used WPT as a feature extraction algorithm and BPNN as a fault estimator. A fault location scheme based on determinant function and support vector regression (SVR) is discussed in [17]. The scheme in [17] considered the measurements of both current and voltage at the terminal source. Furthermore, a filtering segment of the noise and DC offset is used in [17] to improve the scheme's efficiency. In [18], a hybrid protection scheme for fault location in a distribution radial network using WT and SVR is proposed. The scheme in [18] uses DWT for feature extraction and SVR to estimate the location of the fault. Although several schemes have been proposed to determine an optimal solution to the fault diagnostic problem. The limitations of these schemes cannot be ignored. The limitations range from signal analysis, parameter selection, and the time taken to respond to the fault. In our study, we propose a more reliable signal analysis tool. We further use optimization techniques to determine the parameters of the classifier to enhance the classification accuracy. Unlike other proposed schemes, our scheme uses half of a cycle of the post-fault signal to classify and estimate the fault position, thus improving the processing time. This paper proposes two hybrid methodologies for fault classification and locating in a distribution power line. The proposed method focuses on the analysis of a single cycle of the current signal, which is extracted from the sending end terminals of the distribution power line under consideration for fault classification and locating. Subsequently, the current samples are processed using the wavelet packet transform, and statistical features are extracted from them. Unlike other schemes discussed that use one or more cycles, our scheme uses half a cycle of the post-fault signal. The training data are generated through simulation, and conditions such as fault impedances, fault incipient angles, and fault distances are considered. The statistically selected features are then used to train and test the SVM classifier and SVR locator. The optimal performance of the SVM and SVR schemes is improved by using the particle swarm optimization technique to determine the optimal parameters. To minimize the complexity of the scheme, four support vector classifiers (SVC) are used to detect the fault in the corresponding phases and the ground. The sampling period is taken to be 12.5 kHz for the whole process. The simulation results depict that the proposed scheme can classify and locate power systems faults with high accuracy and minimal estimation error.