Purpose

The solar photovoltaic (PV) system is one of the outstanding, clean and green energy options available for electrical power generation. The varying meteorological operating conditions impose various challenges in extracting maximum available power from the solar PV system. The drawbacks of conventional and evolutionary algorithms-based maximum power point tracking (MPPT) approaches are its inability to extract maximum power during partial shading conditions and quickly changing irradiations. Hence, the purpose of this paper is to propose a modified elephant herding optimization (MEHO) based MPPT approach to track global maximum power point (GMPP) proficiently during dynamic and steady state operations within less time.

Design/methodology/approach

A MEHO-based MPPT approach is proposed in this paper by incorporating Gaussian mutation (GM) in the original elephant herding optimization (EHO) to enhance the optimizing capability of determining the optimal value of DC–DC converter’s duty cycle (D) to operate at GMPP.

Findings

The effectiveness of the proposed system is compared with EHO based MPPT, Firefly Algorithm (FA) MPPT and particle swarm optimization (PSO) MPPT during uniform irradiation condition (UIC) and partial shading situation (PSS) using simulation results. An experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

Originality/value

With the proposed MEHO MPPT, it has been noted that the settling period is lowered by 3.1 times in comparison of FA MPPT, 1.86 times when compared to PSO based MPPT and 1.29 times when compared to EHO based MPPT with augmented efficiency of 99.27%.

中文翻译：

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基于光伏太阳能系统的改进大象放牧优化调整 MPPT 的性能比较

目的

太阳能光伏 (PV) 系统是可用于发电的杰出、清洁和绿色能源选择之一。不同的气象运行条件对从太阳能光伏系统提取最大可用功率提出了各种挑战。基于传统和进化算法的最大功率点跟踪 (MPPT) 方法的缺点是它无法在部分阴影条件和快速变化的辐照条件下提取最大功率。因此，本文的目的是提出一种改进的基于大象放牧优化 (MEHO) 的 MPPT 方法，以便在更短的时间内在动态和稳态操作期间熟练地跟踪全局最大功率点 (GMPP)。

设计/方法/方法

本文提出了一种基于 MEHO 的 MPPT 方法，将高斯变异 (GM) 结合到原始的大象羊群优化 (EHO) 中，以增强确定 DC-DC 转换器占空比 (D) 最佳值的优化能力，以在GMPP。

发现

在均匀照射条件（UIC）和局部阴影情况（PSS）下，使用仿真结果将所提出系统的有效性与基于 EHO 的 MPPT、萤火虫算法（FA）MPPT 和粒子群优化（PSO）MPPT 进行了比较。已经设计并实施了一个实验装置。获得的模拟结果通过实验结果得到验证，证明了所提出的技术对于有效的绿色能源解决方案的可行性。

原创性/价值

使用所提出的 MEHO MPPT，已经注意到与 FA MPPT 相比，稳定期降低了 3.1 倍，与基于 PSO 的 MPPT 相比降低了 1.86 倍，与基于 EHO 的 MPPT 相比降低了 1.29 倍，效率提高了 99.27%。