DETECTION OF HIGH IMPEDANCE FAULT USING A PROBABILISTIC NEURAL-NETWORK CLASSFIER

In this paper, a simple and efficient method for detection high impedance fault (HIF) on power distribution systems using an intelligent approach the probabilistic neural network (PNN) combined with wavelet transform technique is proposed. A high impedance fault has impedance enough high so that conventional overcurrent devices, like overcurrent relays and fuses, cannot detect it. While low impedance faults, which include comparatively large fault currents are easily detected by conventional overcurrent devices. Both frequency and time data are needed to get the exact information to classify and detect no fault from HIF. In the proposed method, DWT is used to extract feature of the no fault and HIF signals. The features extracted which comprise the energy of detail and approximate coefficients of the voltage, current and power signals calculated at a chosen level frequency are utilized to train and test the probabilistic neural network (PNN) for a precise classification of no fault from HIFs

Detection of high impedance faul on power distribution system using probabilistic neural network

High impedance fault (HIF) is abnormal event currents on electric power distribution feeder which does not draw sufficient fault current to be detected by conventional protective devices. The waveforms of normal and HIF current signals on electric power distribution feeders are investigated and analysis the characteristic of HIF. The purpose of this study is to use a new feature which indicates HIF faults. Fast Fourier Transformation (FFT) is used to extract the feature of the fault signal and other power system events, odd harmonics frequency components of the phase currents are analyzed. The effect of capacitor banks and other events on distribution feeder harmonics is discussed. The features extracted are using to train and test the probabilistic neural network (PNN) which is used as the classifier to detect HIF from other normal event in power distribution system

Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model

An irregular activity on electric power distribution feeder, which does not draw adequate fault current to be detected by general protective devices, is called as High impedance fault (HIF). This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. This paper proposes an intelligent algorithm using the Takagi Sugeno- Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. The Fast Fourier Transformation (FFT) is used to extract the feature of the faulted signals and other power system events. The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. The HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. It is evident from the outcomes that the proposed algorithm can effectively differentiate the HIFs from other events in power distribution feeder

Using Probabilistic Neural Network for Classification High Impedance Faults on Power Distribution Feeders

An intelligent approach probabilistic Neural Network (PNN) combined with advanced signalprocessing techniques such as Discrete Wavelet Transform (DWT) is presented for detection High impedance faults (HIFs) on power distribution networks. HIFs detection is usually very difficult using the common over current devices, both frequency and time data are needed to get the exact information to classify and detect no fault from HIF. In this proposed method, DWT is used to extract features of the no fault and HIF signals. The features extracted using DWT which comprises the energy, standard deviation, mean, root mean square and mean of energy of detail and approximate coefficients of the voltage, current and power signals are utilized to train and test the PNN for a precise classification of no fault from HIFs. The proposed method shows that it is more convenient for HIF detection in distribution systems with ample varying in operating cases

Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System

High impedance fault (HIF) is abnormal event on electric power distribution feeder which does not draw enough fault current to be detected by conventional protective devices. The algorithm for HIF detection based on the amplitude ratio of second and odd harmonics to fundamental is presented. This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. It is integrating the learning capabilities of neural network to the fuzzy logic system robustness in the sense that fuzzy logic concepts are embedded in the network structure. It also provides a natural framework for combining both numerical information in the form of input/output pairs and linguistic information in the form of IF– THEN rules in a uniform fashion. Fast Fourier Transformation (FFT) is used to extract the features of the fault signal and other power system events. The effect of capacitor banks switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. The results show that the proposed algorithm can distinguish successfully HIFs from other events in distribution power syste

High Impedance Fault Detection on Power Distribution Feeder

This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. High impedance fault (HIF) is abnormal event on electric power distribution feeder which does not draw enough fault current to be detected by conventional protective devices. The algorithm for HIF detection based on the amplitude ratio of second and (3rd, 5th, 7th, 9th, 11th) harmonics to fundamental is presented. Fast Fourier Transformation (FFT) is used to extract the feature of the fault signal and other power system events. The effect of capacitor banks switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. The results show that the proposed algorithm can distinguish successfully HIFs from other events in distribution power system

Broken Conductor Detection on Power Distribution Feeder

An irregular activity on electric power distribution feeder, which does not draw adequate fault current to be detected by general protective devices, is called as High impedance fault (HIF). This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. It proposes an intelligent algorithm using the Fuzzy Subtractive Clustering Model (FSCM) to detect the high impedance fault. The Fast Fourier Transformation (FFT) is used to extract the feature of the faulted signals and other power system events. The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. The HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. It is evident from the outcomes that the proposed algorithm can effectively differentiate the HIFs from other events in power distribution feeder

Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model 1

Abstract: An irregular activity on electric power distribution feeder, which does not draw adequate fault current to be detected by general protective devices, is called as High impedance fault (HIF). This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. This paper proposes an intelligent algorithm using the Takagi SugenoKang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. The Fast Fourier Transformation (FFT) is used to extract the feature of the faulted signals and other power system events. The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. The HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. It is evident from the outcomes that the proposed algorithm can effectively differentiate the HIFs from other events in power distribution feeder

Methods in single phase to ground faults on power distribution systems

This study highlights the main contributions for the single phase to ground faults on distribution networks field throughout a last three decades from classic methods to heuristic methods. The surveys about 76 papers that are published in the field, the quantity of existing methods for each method is determined and categorized. The study includes graphs and tables explaining the frequency of each single phase to ground faults methods and so that, researchers in the same field can be used this paper as a guideline for their research