Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment
This paper proposes a novel controller optimization of boost converter by tunning two controllers of voltage and current in PV (Photovoltaic) boost converters: Sliding Mode Control (SMC) or Sliding Mode plus Proportional-Integrative. Genetic Algorithm (GA) optimization is applied in a Internet of Th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Results in Control and Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720724000195 |
| _version_ | 1827316322964340736 |
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| author | Roberto Inomoto Alfeu J. Sguarezi Filho José Roberto Monteiro Eduardo C. Marques da Costa |
| author_facet | Roberto Inomoto Alfeu J. Sguarezi Filho José Roberto Monteiro Eduardo C. Marques da Costa |
| author_sort | Roberto Inomoto |
| collection | DOAJ |
| description | This paper proposes a novel controller optimization of boost converter by tunning two controllers of voltage and current in PV (Photovoltaic) boost converters: Sliding Mode Control (SMC) or Sliding Mode plus Proportional-Integrative. Genetic Algorithm (GA) optimization is applied in a Internet of Things (IoT) context, in which the server side consists of running the GA and thereafter used to tune the SMC and SMPIC of the PV plant boost converter. Communication between the IoT (PV plant) and cloud server comprises to the acquired currents and voltages from PV to the server and controllers parameters from server to IoT. Data from the IoT is applied to calculate the fitness function for a given solution, which learns the solar plant (machine learning). Experimental results using hardware are considered, in order to evaluate the performance, and results are compared between heuristic and deterministic parameters from SMC or SMPIC, proving the reduction of overshoot and settling time. |
| first_indexed | 2024-03-08T03:35:16Z |
| format | Article |
| id | doaj.art-6b3b28f9a32d48ada9208960db467b35 |
| institution | Directory Open Access Journal |
| issn | 2666-7207 |
| language | English |
| last_indexed | 2024-04-24T23:12:36Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj.art-6b3b28f9a32d48ada9208960db467b352024-03-17T07:59:02ZengElsevierResults in Control and Optimization2666-72072024-03-0114100389Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environmentRoberto Inomoto0Alfeu J. Sguarezi Filho1José Roberto Monteiro2Eduardo C. Marques da Costa3Polytechnic School of the University of São Paulo - POLI-USP, SP, Brazil; Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC - UFABC, SP, Brazil; Corresponding author at: Polytechnic School of the University of São Paulo - POLI-USP, SP, Brazil.Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC - UFABC, SP, BrazilSchool of Engineering of São Carlos, University of São Paulo, SP, BrazilPolytechnic School of the University of São Paulo - POLI-USP, SP, BrazilThis paper proposes a novel controller optimization of boost converter by tunning two controllers of voltage and current in PV (Photovoltaic) boost converters: Sliding Mode Control (SMC) or Sliding Mode plus Proportional-Integrative. Genetic Algorithm (GA) optimization is applied in a Internet of Things (IoT) context, in which the server side consists of running the GA and thereafter used to tune the SMC and SMPIC of the PV plant boost converter. Communication between the IoT (PV plant) and cloud server comprises to the acquired currents and voltages from PV to the server and controllers parameters from server to IoT. Data from the IoT is applied to calculate the fitness function for a given solution, which learns the solar plant (machine learning). Experimental results using hardware are considered, in order to evaluate the performance, and results are compared between heuristic and deterministic parameters from SMC or SMPIC, proving the reduction of overshoot and settling time.http://www.sciencedirect.com/science/article/pii/S2666720724000195PhotovoltaicBoost converterMaximum power point trackingGenetic algorithmSliding mode control |
| spellingShingle | Roberto Inomoto Alfeu J. Sguarezi Filho José Roberto Monteiro Eduardo C. Marques da Costa Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment Results in Control and Optimization Photovoltaic Boost converter Maximum power point tracking Genetic algorithm Sliding mode control |
| title | Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment |
| title_full | Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment |
| title_fullStr | Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment |
| title_full_unstemmed | Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment |
| title_short | Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment |
| title_sort | genetic algorithm based tuning of sliding mode controllers for a boost converter of pv system using internet of things environment |
| topic | Photovoltaic Boost converter Maximum power point tracking Genetic algorithm Sliding mode control |
| url | http://www.sciencedirect.com/science/article/pii/S2666720724000195 |
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