A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.

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Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

In this paper, a fuzzy-logic-based algorithm to identify the type of faults for digital distance protection system has been developed. The proposed technique is able to accurately identify the phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault resistances, inception angle, and loading levels. The proposed method needs only three line-current measurements available at the relay location and can perform the fault classification task in about a half-cycle period. Thus, the proposed technique is well suited for implementation in a digital distance protection scheme.

Fuzzy-logic-based fault classification scheme for digital distance protection

This paper presents a new approach to real-time fault classification in power transmission systems using fuzzy-logic-based multicriteria approach Only the three line currents are utilized to detect fault types such as LG, LL, and LLG, and then to define the faulty line An online wavelet-based preprocessor stage is used with data window of ten samples (based on 45-kHz sampling rate and 50-Hz power frequency) The multicriteria algorithm is developed based on fuzzy sets for the decision-making part of the scheme Computer simulation has been conducted using EMTP programs Results are shown and they indicate that this approach can be used as an effective tool for high-speed digital relaying, as the correct detection is achieved in less than half a cycle and that computational burden is much simpler than the recently postulated fault classification techniques

Combined fuzzy-logic wavelet-based fault classification technique for power system relaying

Series capacitor protected by metal-oxide varistor and air-gap arrangement imposes problems to line protection and other online decisions. Discrete wavelet transform integrated with a fuzzy logic system is designed for fault classification of a transmission line possessing a series capacitor at the midpoint. The approach uses information obtained from the wavelet decomposition of current signals for faulty phase selection and section identification. Two different FLSs are designed for the two classification objectives in this paper.

Wavelet fuzzy combined approach for fault classification of a series-compensated transmission line

This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios.

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Fuzzy ART neural network algorithm for classifying the power system faults

This paper presents a new approach to real-time fault detection and classification in power transmission systems by using fuzzy-neuro techniques. The integration with neural network technology enhances fuzzy logic systems on learning capabilities. The symmetrical components in combination with three line currents are utilized to detect fault types such as single line-to ground, line-to-line, double line-to-ground and three line-to-ground, and then to define the faulty line. Computer simulation results are shown in this paper and they indicate this approach can be used as an effective tool for high speed digital relaying, as the correct detection is achieved in less than 10 ms.

Huisheng Wang

Papers

Fuzzy-neuro approach to fault classification for transmission line protection