A high impedance fault (HIF) is characterized by a small, nonlinear, random, unstable, and widely varying fault current in a power distribution system. HIFs draw very low fault currents, and hence are not always effectively cleared by conventional overcurrent relays. Various schemes are proposed to detect such faults. This paper presents a method to detect HIFs using a tool based on mathematical morphology (MM). The method is implemented alongside the conventional overcurrent relay at the substation to improve the performance of this relay in detecting HIFs. It is rigorously tested on standard test systems using PSCAD/EMTDC® to generate test waveforms, and Matlab® to implement the method. Simulation results show that the proposed method is fast, secure, and dependable.

Detection of High Impedance Fault in Power Distribution Systems Using Mathematical Morphology

Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey

A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.

High impedance fault detection based on wavelet transform and statistical pattern recognition

High impedance fault detection: A review

A feedforward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents The training algorithm used was backpropagation, assuming initially a sigmoid transfer function for the network's processing units ("neurons") Once the network was trained the units' transfer function was changed to hard limiters with thresholds equal to the biases obtained for the sigmoids during training The off-line experimental results presented in this paper show that a FFNN may be considered as an alternative method to make the discrimination between inrush and fault currents in a digital relay implementation >

Training an artificial neural network to discriminate between magnetizing inrush and internal faults

The downed-wires problem, known as high impedance faults, is described. A high voltage laboratory setup was devised to investigate the phenomenon. The laboratory model results agreed with field test results, and previous research efforts. The arcing fault model was justified. The setup was used as a source of fault current signal. A simple approach was taken to design an arcing fault detector. The algorithm utilizes the random behavior of the fault current. It compares the positive and negative current peaks in one cycle to those in the next cycle to measure the flicker in the current signal. The asymmetry of the current is calculated by comparing the positive peak to the negative peak, for each cycle: the moving window length is half a cycle. Both values are used as a signature of arcing. The result is filtered and compared with a suitable detection threshold. The algorithm was tested by traces of normal load, and no-load current disturbed by currents of faults on dry and wet soil, arc welders, computers, and fluorescent light loads, as well as short circuit currents. The algorithm performed well under the test conditions, except for the arc welder load. This load is also a source of insecurity for other algorithms. The detection criterion will be integrated with another detection method to improve the security during arcing load events. >

Detecting arcing downed-wires using fault current flicker and half-cycle asymmetry

The authors present an arcing fault detector, motivated by the advances in neurocomputing in pattern recognition, that uses a simple preprocessing algorithm. A feedforward three-layer perceptron was trained by high-impedance fault, fault-like load, and normal load current patterns, using the backpropagation training algorithm. The neural network parameters were embodied in a high-impedance arcing fault detection algorithm, which used a simple preprocessing technique to prepare the information input to the network. The algorithm was tested by traces of normal load current disturbed by fault currents on dry and wet soil, an arc welder, computers, and fluorescent lights. The algorithm showed good performance in identifying faults disrupted by arc noise as well as good discrimination between faults and fault-like loads. >

Detection of high impedance arcing faults using a multi-layer perceptron

This paper describes a new positive sequence directional element for use in numerical distance relays. The element is incorporated in a numerical relay and the relay is tested on a RTDS (real time digital simulator). Results are also shown from on-line tests in two different locations in the Manitoba Hydro network. Finally an accurate software model of the relay, proved against recorded performance on the RTDS tests, is used in off-line tests on complex system configurations. >

A new directional element for numerical distance relays

This paper describes a double circuit line protection relay which has all 6 line currents and voltages available for processing. This allows several alterations to the standard 3-phase distance package which enhance the performance of the protection system. Results are presented for the relay fed with simulated waveforms from a Playback Simulator. The system used in the simulations is modelled on a 69 kV parallel line in the Winnipeg Hydro network.

Enhanced double circuit line protection

This paper describes a development in relaying hardware and philosophy which has been made possible by DSPs and industrial grade PCs. Details are given of a prototype design capable of being assembled and configured by a utility and results of tests on this prototype using the RTDS at the Manitoba HVDC Research Centre are presented. The project is a joint effort between the University of Manitoba and Manitoba Hydro. >

G.W. Swift

Papers

Detecting arcing downed-wires using fault current flicker and half-cycle asymmetry

Detection of high impedance arcing faults using a multi-layer perceptron

A new directional element for numerical distance relays

Enhanced double circuit line protection

"Open" systems relaying