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Monitoring and enhanced dynamic modeling of battery by genetic algorithm using LabVIEW applied in photovoltaic system

机译：应用光伏系统LabVIEW遗传算法监测和增强电池动态建模

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The dynamic modeling of batteries used in photovoltaic systems (PVS), exceptionally in stand-alone or microgrid systems, is considered as an important issue and a major problem for the monitoring and simulation and even for fault detection applications. In this work, we propose as first step an enhancement of CIEMAT model by the improvement of 21 charge and discharge parameters; also we propose an enhancement of 4 parameters for the estimation approximately of the gassing and the saturation levels by a new static method using genetic algorithm. Basically, this method relies on static measurements by charging of the battery with different constant currents to give a more accurate estimation of this area. Furthermore, a real-time interface system using LabVIEW using the improved model was proposed in this work to provide online estimation and measurements of all battery data and characteristics in PVS. The proposed robust and low-cost method of simulation and monitoring can be applied for the study of battery fault detection in PVS.
机译：光伏系统（PVS）的动态建模（PVS）在独立或微电网系统中出现异常，被认为是监控和仿真的重要问题，甚至用于故障检测应用。在这项工作中，我们提出了通过提高21个电荷和放电参数来提高CIEMAT模型的第一步;此外，我们提出了通过使用遗传算法的新的静态方法来提高4个参数，以便通过新的静态方法进行估计。基本上，该方法依赖于通过具有不同恒定电流的电池充电来依靠静态测量来提供对该区域的更准确的估计。此外，在这项工作中提出了使用改进模型的使用LabVIEW的实时接口系统，为PVS的所有电池数据和特性提供在线估计和测量。建议的稳健和低成本的仿真和监测方法可以应用于PVS中电池故障检测的研究。

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