M. Dwivedi, Gitanjali Mehta, Asif Iqbal, H. Shekhar
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引用次数: 4
摘要
本研究的目的是通过改进最大功率点跟踪(MPPT)技术来提高光伏系统的性能,为未来一代提供可持续的绿色电力生产。传统的MPPT算法在均匀光照条件下表现良好,但在部分遮光条件下无法达到期望的最大功率点(MPP)。这就要求有必要开发有效的优化技术,使光伏系统在PSC下能够达到全局最大功率点(GMPP)。因此,本研究工作对粒子群优化算法在PSC下的跟踪性能进行了综合评价。将该优化技术与两种传统算法Perturb and Observe (P&O)和Incremental conductivity (INC)进行了比较研究。结果表明,粒子群算法能够快速收敛到GMPP,并且与传统算法相比具有更好的性能。
Performance enhancement of solar PV system under partial shaded condition using PSO
The purpose of this research is to focus on performance enhancement of Photovoltaic system by improving Maximum Power Point Tracking (MPPT) techniques for sustainable green electricity production for future generation. The conventional MPPT algorithms performs well in uniform irradiance condition but unable to reach at the desired maximum power point (MPP)in partial shading condition (PSC). This demands necessity for development of efficient optimization techniques those are capable of reaching the global maximum power point (GMPP) in a PV system under PSC. Accordingly, this research work provides a comprehensive assessment on tracking performance of Particle Swarm Optimization (PSO) algorithm under PSC. A comparative study of this optimization technique has been performed against two conventional algorithms named Perturb and Observe (P&O) and Incremental Conductance (INC). Results confirm that the PSO algorithm guarantees fast convergence to GMPP and have better performance in comparison with the conventional ones.