Optimasi PV Array Menggunkan Maximum Power Point Tracking dengan Algoritma FireFly dan Partical Swarm Optimization kondisi Normal dan Partial Shadding

Fuad Hasan, Muhyidin, MMoh. Andy Faturrahman
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Abstract

Most solar power plants are not capable of operating independently at maximum capacity, the voltage characteristics of solar energy usually follow the voltage (V) of the storage (battery) or load (load) which is directly connected to solar energy. Of course, the irradiation received by the PV module does not always receive uniform radiation so that the power produced does not always have maximum power and causes double peaks. An MPPT (Maximum Power Point Tracking) system is required to optimize PV output. However, the commonly used methods are often trapped by peaks below the optimal peak and require a long convergence time. This research compares the tracking performance and tracking time of two methods, FF (Firefly Algorithm) and PSO (Particle Swarm Optimization), to find the best performance in partial shadow conditions. The PSO algorithm has an average efficiency of 99.1043% and the FF algorithm has an average efficiency of 99.4073% in 10 trials in each of the 6 cases.
在正常和部分遮阳条件下,利用 FireFly 算法和粒子群优化最大功率点跟踪进行光伏阵列优化
大多数太阳能发电站都无法独立运行,无法达到最大发电量,太阳能的电压特性通常与直接与太阳能相连的储能设备(蓄电池)或负载(负荷)的电压(V)一致。当然,光伏组件接收到的辐照并不总是均匀的,因此产生的电能并不总是最大功率,而是会出现双峰。这就需要一个 MPPT(最大功率点跟踪)系统来优化光伏输出。然而,常用的方法往往会被低于最佳峰值的峰值所困,并且需要较长的收敛时间。本研究比较了 FF(萤火虫算法)和 PSO(粒子群优化)两种方法的跟踪性能和跟踪时间,以找出在部分阴影条件下的最佳性能。在 6 种情况下,PSO 算法的平均效率为 99.1043%,FF 算法在每种情况下的 10 次试验中的平均效率为 99.4073%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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