Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme
Controllers; Electric control equipment; Electric load management; Fuzzy inference; Hydroelectric generators; Hydroelectric power; Press load control; Stream flow; Fuzzy tuning; Load frequency controllers; Mini-hydropower; Proportional-derivative controllers; Secondary loads; Electric frequency cont...
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2023
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my.uniten.dspace-235222023-05-29T14:50:04Z Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme Basir Khan M.R. Pasupuleti J. Jidin R. 55812128900 11340187300 6508169028 Controllers; Electric control equipment; Electric load management; Fuzzy inference; Hydroelectric generators; Hydroelectric power; Press load control; Stream flow; Fuzzy tuning; Load frequency controllers; Mini-hydropower; Proportional-derivative controllers; Secondary loads; Electric frequency control; consumption behavior; demand elasticity; diesel; employment; fuzzy mathematics; hydroelectric power; power generation This paper presents a new method for load frequency control (LFC) based on self-tuning fuzzy Proportional-Derivative method. Under dynamic frequency variations, the proportional-derivative (PD) gains will automatically tuned by the fuzzy rules in order to maintain frequency at nominal value. In this paper, the proposed controller was used to controls the frequency of a high-penetration mini-hydropower system. In the microgrid, both diesel generator and mini-hydropower system were designed to meet the demand at low stream flow. However, the diesel generator will be shut down if hydropower generation surpasses the consumer demand. The frequency was controlled by absorbing excess generation from hydropower system through secondary load bank. The system performance with the new control scheme was assessed by simulation under dynamic frequency variations. The simulation result shows the employment of the proposed load frequency controller demonstrates robust and high-performance frequency control compared to conventional fixed-gain PD controller. � 2018 Elsevier Ltd Final 2023-05-29T06:50:04Z 2023-05-29T06:50:04Z 2018 Article 10.1016/j.seta.2018.10.013 2-s2.0-85055322336 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055322336&doi=10.1016%2fj.seta.2018.10.013&partnerID=40&md5=935b745b84bd9e64e450b29d9aefb8ce https://irepository.uniten.edu.my/handle/123456789/23522 30 253 262 Elsevier Ltd Scopus |
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Controllers; Electric control equipment; Electric load management; Fuzzy inference; Hydroelectric generators; Hydroelectric power; Press load control; Stream flow; Fuzzy tuning; Load frequency controllers; Mini-hydropower; Proportional-derivative controllers; Secondary loads; Electric frequency control; consumption behavior; demand elasticity; diesel; employment; fuzzy mathematics; hydroelectric power; power generation |
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55812128900 Basir Khan M.R. Pasupuleti J. Jidin R. |
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Article |
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Basir Khan M.R. Pasupuleti J. Jidin R. |
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Basir Khan M.R. Pasupuleti J. Jidin R. Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme |
| author_sort |
Basir Khan M.R. |
| title |
Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme |
| title_short |
Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme |
| title_full |
Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme |
| title_fullStr |
Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme |
| title_full_unstemmed |
Load frequency control for mini-hydropower system: A new approach based on self-tuning fuzzy proportional-derivative scheme |
| title_sort |
load frequency control for mini-hydropower system: a new approach based on self-tuning fuzzy proportional-derivative scheme |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806426070984949760 |