Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions
Most PV systems are equipped with classical algorithms such as Perturb and Observe, Hill climbing and Incremental Conductance for Maximum Power Point Tracking Control (MPPT). The simplicity and ease of implementation of these conventional techniques are seen as the main reason of their utilization i...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722014068 |
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| author | Jean de Dieu Nguimfack-Ndongmo Bello Pierre Ngoussandou Deli Goron Derek Ajesam Asoh Dieudonné Kaoga Kidmo Eustace Mbaka Nfah Godpromesse Kenné |
| author_facet | Jean de Dieu Nguimfack-Ndongmo Bello Pierre Ngoussandou Deli Goron Derek Ajesam Asoh Dieudonné Kaoga Kidmo Eustace Mbaka Nfah Godpromesse Kenné |
| author_sort | Jean de Dieu Nguimfack-Ndongmo |
| collection | DOAJ |
| description | Most PV systems are equipped with classical algorithms such as Perturb and Observe, Hill climbing and Incremental Conductance for Maximum Power Point Tracking Control (MPPT). The simplicity and ease of implementation of these conventional techniques are seen as the main reason of their utilization in PV systems. However, researchers’ attention has, in recent years, been attracted by artificial intelligence-based techniques which can better perform within the bounds of the nonlinearity of PV system characteristics. In this paper, an adaptive nonlinear technique is developed for both MPPT control and voltage stabilization of a Single-Ended Primary Inductance Converter. This control scheme based on Radial Basis function (RBF) neural network is equally used for approximation of unmeasurable or unmeasured variables of the PV system. The main objective of this nonlinear controller is to tract the maximum power and to stabilize the DC output voltage under real environmental conditions. The proposed technique has been numerically tested in a Matlab/Simulink environment under real climatic conditions and load variations. The close-loop stability of the controller is verified by Lyapunov’s theory and the proposed algorithm gives satisfactory results compared to Extremum Seeking Control-based MPPT used in the same conditions. |
| first_indexed | 2024-04-10T08:49:18Z |
| format | Article |
| id | doaj.art-a4b8e0def5aa40c7a6a1eba79d57ecb3 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T08:49:18Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-a4b8e0def5aa40c7a6a1eba79d57ecb32023-02-22T04:31:00ZengElsevierEnergy Reports2352-48472022-11-01810371052Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditionsJean de Dieu Nguimfack-Ndongmo0Bello Pierre Ngoussandou1Deli Goron2Derek Ajesam Asoh3Dieudonné Kaoga Kidmo4Eustace Mbaka Nfah5Godpromesse Kenné6Department of Electrical and Power Engineering, Higher Technical Teacher Training College (HTTTC), University of Bamenda, Bambili, P.O. Box 39, Bamenda, North-West, Cameroon; Unité de Recherche d’Automatique et d’Informatique Appliquée (UR-AIA), Département de Génie Électrique, IUT FOTSO Victor Bandjoun, Université de Dschang, B.P. 134 Bandjoun, Ouest, Cameroon; Corresponding authors.Department of Renewable Energy, National Advanced School of Engineering, University of Maroua, P.O. Box 46, Maroua, Far-North, CameroonDepartment of Renewable Energy, National Advanced School of Engineering, University of Maroua, P.O. Box 46, Maroua, Far-North, CameroonDepartment of Electrical and Power Engineering, Higher Technical Teacher Training College (HTTTC), University of Bamenda, Bambili, P.O. Box 39, Bamenda, North-West, Cameroon; Department of Electrical and Electronic Engineering, National Higher Polytechnic Institute (NAHPI), University of Bamenda, Bambili, P.O. Box 39, Bamenda, North-West, Cameroon; Laboratoire de Génie Electrique, Mécatronique et Traitement du Signal, ENSPY, Université de Yaoundé I, Ngoa-Ekelle, Yaoundé, B.P. 337, Centre, CameroonDepartment of Renewable Energy, National Advanced School of Engineering, University of Maroua, P.O. Box 46, Maroua, Far-North, Cameroon; Corresponding authors.Department of Electrical and Electronic Engineering, National Higher Polytechnic Institute (NAHPI), University of Bamenda, Bambili, P.O. Box 39, Bamenda, North-West, Cameroon; Unité de Recherche d’Automatique et d’Informatique Appliquée (UR-AIA), Département de Génie Électrique, IUT FOTSO Victor Bandjoun, Université de Dschang, B.P. 134 Bandjoun, Ouest, CameroonUnité de Recherche d’Automatique et d’Informatique Appliquée (UR-AIA), Département de Génie Électrique, IUT FOTSO Victor Bandjoun, Université de Dschang, B.P. 134 Bandjoun, Ouest, CameroonMost PV systems are equipped with classical algorithms such as Perturb and Observe, Hill climbing and Incremental Conductance for Maximum Power Point Tracking Control (MPPT). The simplicity and ease of implementation of these conventional techniques are seen as the main reason of their utilization in PV systems. However, researchers’ attention has, in recent years, been attracted by artificial intelligence-based techniques which can better perform within the bounds of the nonlinearity of PV system characteristics. In this paper, an adaptive nonlinear technique is developed for both MPPT control and voltage stabilization of a Single-Ended Primary Inductance Converter. This control scheme based on Radial Basis function (RBF) neural network is equally used for approximation of unmeasurable or unmeasured variables of the PV system. The main objective of this nonlinear controller is to tract the maximum power and to stabilize the DC output voltage under real environmental conditions. The proposed technique has been numerically tested in a Matlab/Simulink environment under real climatic conditions and load variations. The close-loop stability of the controller is verified by Lyapunov’s theory and the proposed algorithm gives satisfactory results compared to Extremum Seeking Control-based MPPT used in the same conditions.http://www.sciencedirect.com/science/article/pii/S2352484722014068RBF-neuro observerMPPT controllerVoltage stabilizationPV systemsNonlinear controlReal climatic conditions |
| spellingShingle | Jean de Dieu Nguimfack-Ndongmo Bello Pierre Ngoussandou Deli Goron Derek Ajesam Asoh Dieudonné Kaoga Kidmo Eustace Mbaka Nfah Godpromesse Kenné Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions Energy Reports RBF-neuro observer MPPT controller Voltage stabilization PV systems Nonlinear control Real climatic conditions |
| title | Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions |
| title_full | Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions |
| title_fullStr | Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions |
| title_full_unstemmed | Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions |
| title_short | Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions |
| title_sort | nonlinear neuro adaptive mppt controller and voltage stabilization of pv systems under real environmental conditions |
| topic | RBF-neuro observer MPPT controller Voltage stabilization PV systems Nonlinear control Real climatic conditions |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722014068 |
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