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Hybrid genetic algorithm fuzzy-based control schemes for small power system with high-penetration wind farms Lotfy, M, Senjyu, T, Farahat, M, Abdel-Gawad, A, Lei, L and Datta, M 2018, 'Hybrid genetic algorithm fuzzy-based control schemes for small power system with high-penetration wind farms', Applied Sciences, vol. 8, no. 3, pp. 1-20. Document type: Journal Article Collection: Journal Articles Title Hybrid genetic algorithm fuzzy-based control schemes for small power system with high-penetration wind farms Author(s) Lotfy, M Senjyu, T Farahat, M Abdel-Gawad, A Lei, L Datta, M Year 2018 Journal name Applied Sciences Volume number 8 Issue number 3 Start page 1 End page 20 Total pages 20 Publisher M D P I AG Abstract Wind is a clean, abundant, and inexhaustible source of energy. However, wind power is not constant, as windmill output is proportional to the cube of wind speed. As a result, the generated power of wind turbine generators (WTGs) fluctuates significantly. Power fluctuation leads to frequency deviation and voltage flicker inside the system. This paper presents a new methodology for controlling system frequency and power. Two decentralized fuzzy logic-based control schemes with a high-penetration non-storage wind-diesel system are studied. First, one is implemented in the governor of conventional generators to damp frequency oscillation, while the other is applied to control the pitch angle system of wind turbines to smooth wind output power fluctuations and enhance the power system performance. A genetic algorithm (GA) is employed to tune and optimize the membership function parameters of the fuzzy logic controllers to obtain optimal performance. The effectiveness of the suggested controllers is validated by time domain simulation for the standard IEEE nine-bus three-generator test system, including three wind farms. The robustness of the power system is checked under normal and faulty operating conditions. © 2018 by the authors. Subject Industrial Electronics Renewable Power and Energy Systems Engineering (excl. Solar Cells) Keyword(s) Frequency control Fuzzy control Genetic algorithm Pitch angle control Power system stability Wind power generation DOI - identifier 10.3390/app8030373 Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). ISSN 2076-3417 Related Links Link Description https://dx.doi.org/10.3390/app8030373 Link to Published Version   Versions Version Filter Type Mon, 10 Dec 2018, 14:04:35 EST Filtered Full Citation counts:   Cited 7 times in Thomson Reuters Web of Science Article | Citations Cited 0 times in Scopus Article Altmetric details: Access Statistics: 26 Abstract Views  -  Detailed Statistics Created: Thu, 06 Dec 2018, 10:39:00 EST by Catalyst Administrator