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Energies 2018, 11(2), 426; https://doi.org/10.3390/en11020426

Data Mining and Neural Networks Based Self-Adaptive Protection Strategies for Distribution Systems with DGs and FCLs

Department of Electrical Engineering, National Cheng Kung University, East Dist., Tainan City 701, Taiwan
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Received: 19 December 2017 / Revised: 23 January 2018 / Accepted: 5 February 2018 / Published: 13 February 2018
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Abstract

In light of the development of renewable energy and concerns over environmental protection, distributed generations (DGs) have become a trend in distribution systems. In addition, fault current limiters (FCLs) may be installed in such systems to prevent the short-circuit current from exceeding the capacity of the power apparatus. However, DGs and FCLs can lead to problems, the most critical of which is miscoordination in protection system. This paper proposes overcurrent protection strategies for distribution systems with DGs and FCLs. Through the proposed approach, relays with communication ability can determine their own operating states with the help of an operation setting decision tree and topology-adaptive neural network model based on data processed through continuous wavelet transform. The performance and effectiveness of the proposed protection strategies are verified by the simulation results obtained from various system topologies with or without DGs, FCLs, and load variations. View Full-Text
Keywords: adaptive protection strategy; protection coordination; distributed generation; fault current limiter adaptive protection strategy; protection coordination; distributed generation; fault current limiter
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Tang, W.-J.; Yang, H.-T. Data Mining and Neural Networks Based Self-Adaptive Protection Strategies for Distribution Systems with DGs and FCLs. Energies 2018, 11, 426.

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