TY - Jour A2 - Wen，Huiqing Au - Nureddin，Abdulbaset Abdulhamed Mohamed Au - Rahebi，Javad Au - Ab-Belkhair，Adel PY - 2020 DA - 2020/12/08 TI - 电源管理控制器，用于微耕地集成混合电动机PV /燃料基于人造深神经网络的电池系统SP - 8896412 VL - 2020 AB - 如今，由于人口和行业的增长，电力需求日益增加。单独的传统发电厂是不称职的，以满足由于环境问题为本的消费者需求。在这个目前的情况下，必不可少的是找到满足消费者需求的替代方式。在现在的日子里，大多数发达国家专注于开发替代资源，并为其研发活动投资巨额资金。最可再生能源是天然友好的来源，如风，太阳能，燃料电池和水力/水源。使用可再生能源的发电结果仅取决于资源的可用性。由于自然资源波动，全天可再生能源的可用性是可变的。本研究工作讨论了两个主要可再生能源发电资源：光伏（PV）细胞和燃料电池。他们俩都为发电的基础提供了基础，因此由于其令人印象深刻的性能机制，它们非常受欢迎。 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. SN - 1110-662X UR - https://doi.org/10.1155/2020/8896412 DO - 10.1155/2020/8896412 JF - International Journal of Photoenergy PB - Hindawi KW - ER -