光伏系统
最大功率点跟踪
微电网
粒子群优化
计算机科学
最大功率原理
地铁列车时刻表
功率(物理)
背景(考古学)
控制理论(社会学)
工程类
电压
控制(管理)
电气工程
逆变器
算法
人工智能
古生物学
物理
量子力学
生物
操作系统
作者
Debabrata Mazumdar,Pabitra Kumar Biswas,Chiranjit Sain,Furkan Ahmad,Rishiraj Sarker,Taha Selim Ustun
摘要
The output of photovoltaic (PV) systems is significantly impacted by the vagaries of ambient temperature, solar irradiance, and environmental fluctuations. To achieve the utmost attainable power from PV systems, it is desired to be efficient at the maximum power point in diverse weather climates. Maximum power point tracking (MPPT) is used to schedule a designated location from where the highest power can be harvested. In the context of solar photovoltaic systems connected with DC microgrid platforms, this study introduces a recently developed drone squadron optimization (DSO) scheme that tracks the global maximum power point under PSCS difficulties. Furthermore, an exhaustive comparative analysis has been presented among particle swarm optimization (PSO), cuckoo search algorithm (CUSA), and grey wolf optimization (GWO) under different operating environments to endorse the supremacy of the nominated technique. The suggested method performs noticeably faster than many other methods currently in use, and in addition to offering the highest power, it can also use bidirectional power flow regulation in both constant and variable air conditions. Lastly, an MPPT system interfaced with the DC microgrid based on DSO ensures a sustainable and reliable architecture to provide at load in low power generating situations.
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