Maximum power point tracking using neural networks for grid-connected photovoltaic system
This paper proposes a method of maximum power point tracking (MPPT) using neural networks for gridconnected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on output from neur...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Online Access: | http://www.scopus.com/inward/record.url?eid=2-s2.0-33847203656&partnerID=40&md5=56c0bf47e92da8f0369dc1f5e88f2019 http://cmuir.cmu.ac.th/handle/6653943832/1269 |
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| Institution: | Chiang Mai University |
| Language: | English |
| Summary: | This paper proposes a method of maximum power point tracking (MPPT) using neural networks for gridconnected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on output from neural networks to control a switch of a boost converter. Back-propagation neural networks is utilized as pattern classifier. Back-propagation neural networks is an example of nonlinear layered feed-forward networks. The single phase inverter uses hysteresis current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. MPPT using neural networks are simulated and implemented to evaluate performance. Simulation and experimental results are provided for neural networks and fixed duty ratio under the same atmospheric condition. From the simulation and experimental results, neural networks can deliver more power than the conventional controller. |
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