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Ferroresonance Overvoltage Mitigation Using Surge Arrester for Grid-Connected Wind Farm

Nehmdoh A. Sabiha*, Hend I. Alkhammash

Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

* Corresponding Author: Nehmdoh A. Sabiha. Email: email

Intelligent Automation & Soft Computing 2022, 31(2), 1107-1118. https://doi.org/10.32604/iasc.2022.020070

Abstract

Ferroresonance occurrence represents a very dangerous phenomenon to electric power systems. Concerning the recent trend of the applications of grid-connected wind farms, this phenomenon can lead to undesired overvoltages stressing the wind farm components. In this paper, the ferroresonance overvoltages are studied and mitigated for the grid-connected wind farm. Single-pole switching of the breaker is considered, where it is the most famous reason behind the ferroresonance transient events in the electric power systems. During the ferroresonance period, the transient voltage of the network is increased to more than three times the voltage level and associated with harmonics. Surge arrester is suggested to mitigate the ferroresonance transient overvoltages, in which the arrester coordination setting of its residual voltage is selected to be 1.2 of the nominal voltage value. Evaluation of the dynamic interaction of the installed surge arrester with the ferroresonance overvoltages is investigated, and the mitigation is attained, successfully. Furthermore, the magnitudes of the harmonics created due to ferroresonance are reduced by installing the surge arresters, but they are not diminished completely. However, the arresters are found under stress if their dissipating energies exceed the withstand capability. The simulations are carried out using the alternative transient program/electromagnetic transient program (ATP/EMTP), and the waveforms are digitally processed using MATLAB software.

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Cite This Article

N. A. Sabiha and H. I. Alkhammash, "Ferroresonance overvoltage mitigation using surge arrester for grid-connected wind farm," Intelligent Automation & Soft Computing, vol. 31, no.2, pp. 1107–1118, 2022. https://doi.org/10.32604/iasc.2022.020070



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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