Design of MPPT PV using Particle Swarm Optimization Algorithm under Partial Shading Condition

Efendi S Wirateruna, Annisa Fitri Ayu Millenia
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引用次数: 1

Abstract

Fossil energy sources experience a decrease each year when the demand increases significantly. In the case of environmental issues, renewable energy sources (RES) can be energy alternatives. The photovoltaic module is RES with unique characteristics, especially partial shading conditions. This condition leads to the PV characteristic curve experiencing multiple peaks. The paper conducted the simulation of the PV solar panel module using MATLAB Simulink. The Maximum Power Point Tracking (MPPT) PV is also described based on a particle swarm optimization (PSO) algorithm. The proposed algorithm can address multiple peak curve problems due to partial shading conditions. For comparison, the conventional algorithm, perturb & observe, is presented. The PV module is divided into three group cells with irradiance differences for each group to illustrate the partial shading condition. The result shows that the PSO algorithm guarantees optimal and fast response for the operating PowerPoint. It needs about 0.04 seconds to maintain at the optimal power point, 129 Watt, compared with the perturb and observe algorithm performance that only kept at the lower operating power point, 67 Watt at 0.06 second. Thus, the PSO algorithm can tackle the partial shading condition with a fast response to maintain the maximum PowerPoint. Therefore, the PSO algorithm is the proper solution for tracking the optimum operating power point under partial shading conditions.
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部分遮阳条件下基于粒子群算法的MPPT光伏设计
当需求显著增加时,化石能源每年都会减少。就环境问题而言,可再生能源(RES)可以成为替代能源。光伏组件是具有独特特性的可再生能源,特别是部分遮阳条件。这种情况导致PV特性曲线出现多个峰值。本文利用MATLAB Simulink对光伏太阳能电池板组件进行了仿真。本文还介绍了基于粒子群优化(PSO)算法的最大功率点跟踪(MPPT)。该算法可以解决部分遮阳条件下的多峰曲线问题。为了比较,本文提出了传统的摄动&观测算法。光伏组件分为三组电池,每组的辐照度不同,以说明部分遮阳条件。结果表明,粒子群算法保证了运行中的PowerPoint的最优快速响应。与仅维持在较低工作功率点67瓦特0.06秒的扰动和观察算法性能相比,维持在最优功率点129瓦特大约需要0.04秒。因此,粒子群算法可以快速地处理部分遮阳条件,以保持最大的ppt。因此,粒子群算法是在部分遮阳条件下跟踪最优运行功率点的合适解决方案。
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