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Journal ArticleDOI

Fault location and detection techniques in power distribution systems with distributed generation: A review

TLDR
In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper.
Abstract
Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.

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Citations
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Journal ArticleDOI

Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction

TL;DR: An integrated framework combining fault classification and location is proposed by applying an innovative machine-learning algorithm: the summation-wavelet extreme learning machine (SW-ELM) that integrates feature extraction in the learning process and is successfully applied to transmission line fault diagnosis.
Journal ArticleDOI

Dynamic protection of power systems with high penetration of renewables: A review of the traveling wave based fault location techniques

TL;DR: A critical review of the latest developments in travelling wave-based fault location techniques and their potential for future protection applications in smart grid scenarios viz. microgrids and active distribution systems.
Journal ArticleDOI

Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine

TL;DR: In this paper, a deep neural network with a modified early stopping algorithm and least square support vector machine were developed to predict SOx and NOx emissions associated with coal conversion in energy production.
Journal ArticleDOI

A set of efficient heuristics and metaheuristics to solve a two-stage stochastic bi-level decision-making model for the distribution network problem

TL;DR: A two-stage stochastic bi-level decision-making model to simulate the behavior of a distribution network, more efficiently and a set of quick heuristics along with two new hybrid metaheuristics based on the benefits of recent and traditional algorithms to solve the developed model in large-scale networks.
Journal ArticleDOI

Fault detection and location in a microgrid using mathematical morphology and recursive least square methods

TL;DR: Fault detection and location in a microgrid using mathematical morphology (MM) and recursive least-square (RLS) methods and simulation results depict that the proposed method provides faster fault detection and accurate fault location.
References
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Journal ArticleDOI

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Development of a New Type Fault Locator Using the One-Terminal Voltage and Current Data

TL;DR: In this article, a new type fault locator has been developed, that calculates the reactance of a faulty line, with a micro-processor, using the one-terminal voltage and current data of the transmission line.
Journal ArticleDOI

Automated fault location and diagnosis on electric power distribution feeders

TL;DR: In this paper, a fault location and diagnosis scheme is proposed to accurately identify the location of a fault upon its occurrence, based on the integration of information available from disturbance recording devices with knowledge contained in a distribution feeder database.
Journal ArticleDOI

Comparison of impedance based fault location methods for power distribution systems

TL;DR: In this article, performance of 10 fault location methods for power distribution systems has been compared using only measurements of voltage and current at the substation, and the results for several scenarios defined by significant values of the fault location and impedance.
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