Optimal power tracker for stand-alone photovoltaic system using Artificial Neural Network (ANN) and Particle Swarm Optimisation (PSO)
In recent years, many intelligent techniques and approaches have been introduced into photovoltaic (PV) system for the utilisation of free harvest renewable energy. Generally, the output power generation of the PV system rely on the intermittent solar insolation, cell temperature, efficiency of the...
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| Pengarang-pengarang Utama: | , , |
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| Format: | Conference or Workshop Item |
| Diterbitkan: |
2012
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| Subjek-subjek: | |
| Capaian Atas Talian: | http://eprints.uthm.edu.my/6110/ http://eprints.uthm.edu.my/6110/1/Optimal_Power_Tracker_for_Stand%2DAlone.pdf |
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| Ringkasan: | In recent years, many intelligent techniques and
approaches have been introduced into photovoltaic (PV) system
for the utilisation of free harvest renewable energy. Generally,
the output power generation of the PV system rely on the
intermittent solar insolation, cell temperature, efficiency of the
PV panel and its output voltage level. Consequently, it is
essential to track the generated power of the PV system and
utilise the collected solar energy optimally. Artificial Neural
Network (ANN) is initially used to forecast the solar insolation
level and followed by the Particle Swarm Optimisation (PSO)
to optimise the power generation of the PV system based on the
solar insolation level, cell temperature, efficiency of PV panel
and output voltage requirements. This paper proposes an
integrated offline PSO and ANN algorithms to track the solar
power optimally based on various operation conditions due to
the uncertain climate change. The proposed approach has the
capability to estimate the amount of generated PV power at a
specific time. The ANN based solar insolation forecast has
shown satisfactory results with minimal error and the generated
PV power has been optimised significantly with the aids of the
PSO algorithm. |
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