最大功率点跟踪
粒子群优化
占空比
升压变换器
光伏系统
最大功率原理
转换器
算法
功率(物理)
计算机科学
控制理论(社会学)
工程类
电压
电气工程
逆变器
物理
控制(管理)
量子力学
人工智能
作者
Karthikeyan Sathasivam,İlhan Garip,Saeed Hassan Saeed,Yaser Yais,Ali Ihsan Alanssari,Ali Adhab Hussein,Jenan Ali Hammoode,Alaa M. Lafta
标识
DOI:10.1080/15325008.2023.2228795
摘要
The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system.
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