Abstract
In this paper a new control system on basis of group method for data handling neural networks (GMDH-NNs) is designed for voltage and power regulation in the photovoltaic (PV)/Fuel/Battery systems. The dynamics of all subsystems are considered to be fully uncertain. The suggested GMDH-NN is learned using online tuning rules that are concluded through the robustness investigation. The challenging operation conditions such as variable unknown dynamics, unknown temperature and irradiation and suddenly changes in output load are taken into account and are handled by suggested control system. The superiority of the suggested method is shown by simulation in several scenarios and comparison with other techniques.
Highlights
The importance of renewable energies such as PV panels is increasing day by day due to some attractive features such as abundance and clearity
The PV panels need to be combined with storages systems such as batteries
Remark 1: The main topic of this study is to present a control system for voltage and power regulation not a maximum power point tracking algorithm
Summary
The importance of renewable energies such as PV panels is increasing day by day due to some attractive features such as abundance and clearity. The efficiency of PV panels is significantly undesirable, because of high dependance on weather conditions. The PV panels need to be combined with storages systems such as batteries. Fuel cells as the backup systems can be used to make a better energy balance. The control object is the output voltage to be regulated in a desired level in versus of variable load, temperature and irradiation. In [1], hybrid energy storage systems are
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