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Optimization of PV System Using Particle Swarm Algorithm under Dynamic Weather Conditions

机译：在动态天气条件下使用粒子群算法的PV系统优化

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The increasing burden on conventional energy sources and the environmental problems are motivating the world towards the use of solar energy as it offers various advantages such as lack of emission of green house gases, everlasting sun energy and low maintenance cost. The performance of solar system is influenced by partial shaded conditions (PSC). This results in reduction of power from photo-voltaic (PV) system. So to enhance power from solar system under varying weather conditions, maximum power point tracking (MPPT) is essential. The presence of PSC leads to generation of multiple peaks on PV characteristics which minimize the effectiveness of traditional MPPT techniques. This paper introduces the approach of particle swarm optimization (PSO) algorithm for extraction of peak power of PV structure. The recommended technique provides robustness, high efficiency and reliability towards maximum power point (MPP). The accuracy of intended algorithm is validated using MATLAB SIMULINK with DC-DC boost converter and results have been compared with perturb and observe (PO) approach.
机译：传统能源的负担越来越大，使世界促使世界利用太阳能，因为它提供了各种优势，例如缺乏绿色房屋气体排放，永恒的太阳能和低维护成本。太阳系的性能受部分阴影条件（PSC）的影响。这导致来自光伏（PV）系统的功率降低。因此，为了在不同的天气条件下从太阳系上提高功率，最大功率点跟踪（MPPT）至关重要。 PSC的存在导致对PV特性的多个峰的产生，这最小化了传统MPPT技术的有效性。本文介绍了用于提取光伏结构峰值功率的粒子群优化（PSO）算法的方法。推荐的技术为最大功率点（MPP）提供稳健性，高效率和可靠性。使用MATLAB Simulink验证预期算法的准确性，通过DC-DC升压转换器和结果与扰动和观察（PO）方法进行了比较。

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