Multicluster-based particle swarm optimization algorithm for photovoltaic maximum power point tracking

Wei Ru Chen, Liang-Rui Chen, Chia Hsuan Wu, Ci Min Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, a multi-cluster-based particle swarm optimization (MC-PSO) algorithm for photovoltaic (PV) maximum power point tracking (MPPT) is proposed to promote the MPPT performance in the partial shading condition. During the tracking process, each PV module is viewed as a particle and the PV modules with similar characteristics are put into the same cluster. The particles in the same cluster can refer the information to each other to realize the MPPT in the partial shading condition. In addition, multiple sampling points can be obtained at the same time to avoid the misjudgement problem during insolation changing rapidly. Thus, the tracking speed is also improved. The simulation results used by MATLAB is done and compared with the perturbation and observation (P&O) MPPT algorithm. The accuracy of MPPT of the proposed MC-PSO is improved to 96.3% in the partial shading condition. Finally, a 2.1kW prototype is implemented to verify the feasibility. The generated energy using the proposed method compared to the conventional P&O method is increased about 13.3%.

Original languageEnglish
Title of host publication2015 IEEE 2nd International Future Energy Electronics Conference, IFEEC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479976577
DOIs
Publication statusPublished - 2015 Dec 18
Event2nd IEEE International Future Energy Electronics Conference, IFEEC 2015 - Taipei, Taiwan
Duration: 2015 Nov 12015 Nov 4

Other

Other2nd IEEE International Future Energy Electronics Conference, IFEEC 2015
CountryTaiwan
CityTaipei
Period15-11-0115-11-04

Fingerprint

Particle swarm optimization (PSO)
Incident solar radiation
MATLAB
Sampling

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Cite this

Chen, W. R., Chen, L-R., Wu, C. H., & Lai, C. M. (2015). Multicluster-based particle swarm optimization algorithm for photovoltaic maximum power point tracking. In 2015 IEEE 2nd International Future Energy Electronics Conference, IFEEC 2015 [7361493] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IFEEC.2015.7361493
Chen, Wei Ru ; Chen, Liang-Rui ; Wu, Chia Hsuan ; Lai, Ci Min. / Multicluster-based particle swarm optimization algorithm for photovoltaic maximum power point tracking. 2015 IEEE 2nd International Future Energy Electronics Conference, IFEEC 2015. Institute of Electrical and Electronics Engineers Inc., 2015.
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abstract = "In this paper, a multi-cluster-based particle swarm optimization (MC-PSO) algorithm for photovoltaic (PV) maximum power point tracking (MPPT) is proposed to promote the MPPT performance in the partial shading condition. During the tracking process, each PV module is viewed as a particle and the PV modules with similar characteristics are put into the same cluster. The particles in the same cluster can refer the information to each other to realize the MPPT in the partial shading condition. In addition, multiple sampling points can be obtained at the same time to avoid the misjudgement problem during insolation changing rapidly. Thus, the tracking speed is also improved. The simulation results used by MATLAB is done and compared with the perturbation and observation (P&O) MPPT algorithm. The accuracy of MPPT of the proposed MC-PSO is improved to 96.3{\%} in the partial shading condition. Finally, a 2.1kW prototype is implemented to verify the feasibility. The generated energy using the proposed method compared to the conventional P&O method is increased about 13.3{\%}.",
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Chen, WR, Chen, L-R, Wu, CH & Lai, CM 2015, Multicluster-based particle swarm optimization algorithm for photovoltaic maximum power point tracking. in 2015 IEEE 2nd International Future Energy Electronics Conference, IFEEC 2015., 7361493, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE International Future Energy Electronics Conference, IFEEC 2015, Taipei, Taiwan, 15-11-01. https://doi.org/10.1109/IFEEC.2015.7361493

Multicluster-based particle swarm optimization algorithm for photovoltaic maximum power point tracking. / Chen, Wei Ru; Chen, Liang-Rui; Wu, Chia Hsuan; Lai, Ci Min.

2015 IEEE 2nd International Future Energy Electronics Conference, IFEEC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7361493.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In this paper, a multi-cluster-based particle swarm optimization (MC-PSO) algorithm for photovoltaic (PV) maximum power point tracking (MPPT) is proposed to promote the MPPT performance in the partial shading condition. During the tracking process, each PV module is viewed as a particle and the PV modules with similar characteristics are put into the same cluster. The particles in the same cluster can refer the information to each other to realize the MPPT in the partial shading condition. In addition, multiple sampling points can be obtained at the same time to avoid the misjudgement problem during insolation changing rapidly. Thus, the tracking speed is also improved. The simulation results used by MATLAB is done and compared with the perturbation and observation (P&O) MPPT algorithm. The accuracy of MPPT of the proposed MC-PSO is improved to 96.3% in the partial shading condition. Finally, a 2.1kW prototype is implemented to verify the feasibility. The generated energy using the proposed method compared to the conventional P&O method is increased about 13.3%.

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Chen WR, Chen L-R, Wu CH, Lai CM. Multicluster-based particle swarm optimization algorithm for photovoltaic maximum power point tracking. In 2015 IEEE 2nd International Future Energy Electronics Conference, IFEEC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7361493 https://doi.org/10.1109/IFEEC.2015.7361493