<abstract xmlns="http://www.w3.org/1999/xhtml"><p>A hybrid maximum power point tracking method has been proposed for the photovoltaic system using a stochastic evolutionary search and a deterministic hill climbing algorithm. The proposed approach employs the particle swarm optimizer (PSO) to solve a dynamic optimization problem related to the control task in a <italic>PV</italic> system. The position of the best particle is updated by the hill climbing algorithm, and the position of the rest of the particles by the classic PSO rule. The presented method uses the re-randomization mechanism, which places five consecutive particles randomly, but in specified intervals. This mechanism helps track the maximum power point under partially shaded conditions.</p></abstract>

A hybrid maximum power point tracking method has been proposed for the photovoltaic system using a stochastic evolutionary search and a deterministic hill climbing algorithm. The proposed approach employs the particle swarm optimizer (PSO) to solve a dynamic optimization problem related to the control task in a PV system. The position of the best particle is updated by the hill climbing algorithm, and the position of the rest of the particles by the classic PSO rule. The presented method uses the re-randomization mechanism, which places five consecutive particles randomly, but in specified intervals. This mechanism helps track the maximum power point under partially shaded conditions.

Hybrid MPPT Algorithm for PV Systems Under Partially Shaded Conditions Using a Stochastic Evolutionary Search and a Deterministic Hill Climbing

Institute of Control and Industrial Electronics

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A hybrid maximum power point tracking method has been proposed for the photovoltaic system using a stochastic evolutionary search and a deterministic hill...

Hybrid MPPT Algorithm for PV Systems Under Partially Shaded Conditions Using a Stochastic Evolutionary Search and a Deterministic Hill Climbing

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 49 - 59

Eingereicht: 16. Okt. 2017

Akzeptiert: 03. Dez. 2017

DOI: https://doi.org/10.5277/ped170212

Schlüsselwörter
maximum power point tracking, photovoltaic system, hybrid part–stochastic part–deterministic search rule, particle swarm optimization, partial shading, hill climbing

© 2017 Karol Basiński et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hybrid MPPT Algorithm for PV Systems Under Partially Shaded Conditions Using a Stochastic Evolutionary Search and a Deterministic Hill Climbing

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 49 - 59

Eingereicht: 16. Okt. 2017

Akzeptiert: 03. Dez. 2017

DOI: https://doi.org/10.5277/ped170212

Schlüsselwörtermaximum power point tracking, photovoltaic system, hybrid part–stochastic part–deterministic search rule, particle swarm optimization, partial shading, hill climbing

© 2017 Karol Basiński et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
maximum power point tracking, photovoltaic system, hybrid part–stochastic part–deterministic search rule, particle swarm optimization, partial shading, hill climbing