Intelligent Fault Diagnosis in a Power Distribution Network

Babayomi, Oluleke; Oluseyi, P.O.

doi:10.1155/2016/8651630

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…Numerous research has also been conducted on the adoption of intelligent algorithms and machine learning methods for power fault detection and analysis. In [49], a proposal is made to detect faults using a Fuzzy Logic Controller (FLC) and to identify the fault location, Adaptive Neuro Fuzzy Inference System (ANFIS) is suggested. The study focused on how the distribution grid incidences can be detected, identified, and located.…”

Section: Figure 2 Main Components Of a Scada Systemmentioning

confidence: 99%

An Efficient LoRa-Enabled Smart Fault Detection and Monitoring Platform for the Power Distribution System Using Self-Powered IoT Devices

et al. 2022

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Transient stability and supply disturbances are common yet unwelcome phenomena in power distribution systems, particularly in sub-Saharan Africa. The growing demand for greater reliability and dependability in power delivery has aroused the interest of researchers and renewed the pursuit of advanced technological solutions for fault detection and location determination at medium and low-voltage levels. The length of the distribution network typically ranges from hundreds to thousands of kilometers. In this regard, the management of distribution networks, including the identification of faulty segments, is a significant recurrent challenge facing power-system operators. With the ever-expanding distribution network and regulatory demands for service reliability, the challenge for network operators is daunting. However, the deployment of IoT technologies in the energy distribution infrastructure would significantly accelerate the detection and location of faults, thus transforming the electricity delivery service into one that is responsive, communicative, attractive, and robust. This study proposes, designs, and implements a reasonably priced LoRaWAN-based IoT platform for monitoring distribution networks. The study was conducted in Nakuru County, Kenya on an actual and active distribution network owned and managed by Kenya Power Company. Experimental results showed that a trigger is generated at the network-monitoring center in about 100 msec of the occurrence of a fault on the distribution network, thus enabling quick, prompt, and immediate commencement of reparative action. Furthermore, practical evaluation has shown that this platform is well adapted for the context of developing countries where budgetary constraints and cost prohibitions hinder the upgrade of the legacy grid into fully-fledged smart entities.

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Section: Figure 2 Main Components Of a Scada Systemmentioning

confidence: 99%

An Efficient LoRa-Enabled Smart Fault Detection and Monitoring Platform for the Power Distribution System Using Self-Powered IoT Devices

et al. 2022

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“…Oluleke. babayomi et al [121] proposed and presented a fault identification and localization based on an adaptive neuro-fuzzy for the DSN. The fault data are collected from the network and are applied to train the fuzzy inference system to find out the location of the fault.…”

Section: Microgrids Fault Localization Challengesmentioning

confidence: 99%

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

et al. 2020

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The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed.

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