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Adaptive Critic Based Neuro-Fuzzy Tracker for Improving Conversion Efficiency in PV Solar Cells

机译:基于自适应批判的神经模糊跟踪器,可提高光伏太阳能电池的转换效率

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The output power of photovoltaic systems is directly related to the amount of solar energy collected by the system and it is therefore necessary to track the sun's position with high accuracy. This study proposes multi-agent adaptive critic based nero fuzzy solar tracking system dedicated to PV panels. The proposed tracker ensures the optimal conversion of solar energy into electricity by properly adjusting the PV panels according to the position of the sun. To evaluate the usefulness of the proposed method, some computer simulations are performed and compared with fuzzy PD controller. Obtained results show the proposed control strategy is very robust, flexible and could be used to get the desired performance levels. The response time is also very fast. Simulation results that have been compared with fuzzy PD controller show that our method has the better control performance than fuzzy PD controller.
机译:光伏系统的输出功率与系统收集的太阳能量直接相关,因此有必要高精度地跟踪太阳的位置。这项研究提出了基于多智能体自适应批评家的Nero模糊太阳跟踪系统,专门用于光伏电池板。提出的跟踪器可通过根据太阳的位置适当调整光伏面板,确保将太阳能最佳地转换为电能。为了评估该方法的有效性,进行了一些计算机仿真,并与模糊PD控制器进行了比较。获得的结果表明,所提出的控制策略非常健壮,灵活,可用于获得所需的性能水平。响应时间也非常快。与模糊PD控制器的仿真结果表明,该方法具有比模糊PD控制器更好的控制性能。

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