Evaluation of particle swarm optimization techniques applied to maximum power point tracking in photovoltaic systems
Even with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage ( P ‐ V ) characteristic curve of photovoltaic (PV) arrays has multiple peaks under p...
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| Published in | International journal of circuit theory and applications Vol. 49; no. 7; pp. 1849 - 1867 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
01.07.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0098-9886 1097-007X |
| DOI | 10.1002/cta.2978 |
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| Abstract | Even with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (
P
‐
V
) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package. |
|---|---|
| AbstractList | Even with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (
P
‐
V
) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package. Even with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (P‐V) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package. |
| Author | Vázquez Seisdedos, Luis Giral, Roberto Trujillo Codorniu, Rafael Díaz Martínez, David |
| Author_xml | – sequence: 1 givenname: David orcidid: 0000-0002-1947-9264 surname: Díaz Martínez fullname: Díaz Martínez, David organization: Dept. Automática, Fac. Ing. Eléctrica Universidad de Oriente Santiago de Cuba Cuba – sequence: 2 givenname: Rafael orcidid: 0000-0001-7449-1939 surname: Trujillo Codorniu fullname: Trujillo Codorniu, Rafael organization: Dept. Automática, Fac. Ing. Eléctrica Universidad de Oriente Santiago de Cuba Cuba – sequence: 3 givenname: Roberto orcidid: 0000-0001-6582-6741 surname: Giral fullname: Giral, Roberto organization: Dept. Eng. Electrònica, Elèctrica i Automàtica Universitat Rovira i Virgili Tarragona Spain – sequence: 4 givenname: Luis orcidid: 0000-0001-5459-2265 surname: Vázquez Seisdedos fullname: Vázquez Seisdedos, Luis organization: Dept. Automática, Fac. Ing. Eléctrica Universidad de Oriente Santiago de Cuba Cuba |
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| SubjectTerms | Control methods Energy conversion efficiency Maximum power tracking Optimization techniques Particle swarm optimization Photovoltaic cells Scientific papers Shading Tracking control |
| Title | Evaluation of particle swarm optimization techniques applied to maximum power point tracking in photovoltaic systems |
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