Nowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system’s effectiveness.

中文翻译：

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基于人工深度神经网络的混合光伏/燃料电池系统微电网集成电源管理控制器

如今，由于人口和工业的增长，电力需求日益增加。由于环境问题，仅传统的发电厂无法满足消费者的需求。在这种现状下，最重要的是找到一种替代方式来满足消费者的需求。目前，大多数发达国家都集中精力开发替代资源，并为其研发活动投入巨资。大多数可再生能源都是自然友好的来源，例如风能、太阳能、燃料电池和水力/水源。使用可再生能源发电的结果仅取决于资源的可用性。由于自然资源的波动，全天可再生能源的可用性是可变的。这项研究工作讨论了两种主要的可再生能源发电来源：光伏 (PV) 电池和燃料电池。它们都为发电提供了基础，因此它们因其令人印象深刻的性能机制而非常受欢迎。上述基于可再生能源的发电系统是静态设备，因此与主电网中的线路损耗相比，功率损耗通常可以忽略不计。PV 和燃料电池 (FC) 电力系统需要一个控制器，以便在输入资源波动期间实现最大发电量。根据调查报告，提出了一种先进的最大功率点跟踪（MPPT）控制器算法。本文提出了一种基于深度神经网络（DNN-）的 MPPT 算法，该算法已使用 MATLAB 对 PV 和 FC 进行了模拟。本文的主要目的是开发最新的 DNN 控制器，以提高使用混合光伏和燃料电池系统产生的输出电能质量。在开发和模拟所提出的系统后，我们在不同的可能操作条件下进行了分析。最后，我们评估了基于 IEEE 1547 和 519 标准的仿真结果，以证明系统的有效性。