Publicação
Experimental evaluation of Kalman filter based MPPT in grid-connected PV system
| Resumo: | Photovoltaic (PV) energy is becoming an important alternative energy source, since it is abundant in nature, non-polluting and requires low maintenance. However, it suffers from low energy conversion efficiency, which can be even lower if the photovoltaic generator does not operate around a so-called Maximum Power Point (MPP). Tracking this point, which changes its location depending on weather conditions, is a very important step in the design of a photovoltaic system. Several techniques have been investigated in the literature in the MPP context. However, some techniques such as the Kalman filter are steel unknown with a lack of information in real test conditions, since their evaluation is limited only in simulation and literature review. This work presents an experimental evaluation of the Kalman filter based on a comparison with two well-known maximum power point tracking (MPPT) algorithms, which are the Perturbation and observation (among the simplest techniques) and the Particle Swarm Optimization (among the most complex techniques). The experimental tests were carried out under real atmospheric conditions, using Matlab/Simulink and the 1103 dSPACE real-time controller board. The results show that the Kalman filter has a higher aptitude to operate closer to the MPP, with a low oscillation in steady-state compared to the other MPPT evaluated in this work. However, the technique’s flaw lies in the shadow situation where it can not differentiate between the local and global optimums, unlike the particle swarm optimization. |
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| Autores principais: | Chellal, Majd |
| Assunto: | Photovoltaic (PV) Maximum power point tracking (MPPT) Perturbation and observation (PO) Particle swarm optimization (PSO) Kalman Filter (KF) Grid connected PV system dSPACE 1103 |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | Photovoltaic (PV) energy is becoming an important alternative energy source, since it is abundant in nature, non-polluting and requires low maintenance. However, it suffers from low energy conversion efficiency, which can be even lower if the photovoltaic generator does not operate around a so-called Maximum Power Point (MPP). Tracking this point, which changes its location depending on weather conditions, is a very important step in the design of a photovoltaic system. Several techniques have been investigated in the literature in the MPP context. However, some techniques such as the Kalman filter are steel unknown with a lack of information in real test conditions, since their evaluation is limited only in simulation and literature review. This work presents an experimental evaluation of the Kalman filter based on a comparison with two well-known maximum power point tracking (MPPT) algorithms, which are the Perturbation and observation (among the simplest techniques) and the Particle Swarm Optimization (among the most complex techniques). The experimental tests were carried out under real atmospheric conditions, using Matlab/Simulink and the 1103 dSPACE real-time controller board. The results show that the Kalman filter has a higher aptitude to operate closer to the MPP, with a low oscillation in steady-state compared to the other MPPT evaluated in this work. However, the technique’s flaw lies in the shadow situation where it can not differentiate between the local and global optimums, unlike the particle swarm optimization. |
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