Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation
The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuatin...
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| Veröffentlicht in: | Sustainability 2022-11, Vol.14 (22), p.14928 |
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| creator | Hussien, Ahmed M Kim, Jonghoon Alkuhayli, Abdulaziz Alharbi, Mohammed Hasanien, Hany M Tostado-Véliz, Marcos Turky, Rania A Jurado, Francisco |
| description | The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions. |
| doi_str_mv | 10.3390/su142214928 |
| format | Article |
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To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su142214928</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Adaptive algorithms ; Algorithms ; Analysis ; Bayesian analysis ; Distributed generation ; Efficiency ; Electric power production ; Error signals ; Load ; Load fluctuation ; Optimization ; Optimization algorithms ; Optimization techniques ; Response surface methodology ; Systems stability ; Technology application</subject><ispartof>Sustainability, 2022-11, Vol.14 (22), p.14928</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. 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| subjects | Adaptive algorithms Algorithms Analysis Bayesian analysis Distributed generation Efficiency Electric power production Error signals Load Load fluctuation Optimization Optimization algorithms Optimization techniques Response surface methodology Systems stability Technology application |
| title | Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation |
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