A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms.

中文翻译：

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部分遮光条件下光伏最大功率点跟踪控制的增强灰狼优化算法

部分遮光条件 (PSC) 是光伏 (PV) 系统中最常见的问题之一。它会导致光伏系统的输出功率急剧下降。元启发式算法 (MHA) 可以跟踪具有多个峰值的功率电压 (PV) 曲线中的最大功率点。灰狼优化(GWO)算法是一种基于MHA的新型优化算法。它已被用于解决许多应用中的优化问题，包括光伏系统的 MPPT。然而，对于各种 PSC，原始 GWO (OGWO) 中的精度和跟踪时间仍有待进一步提高。因此，已经提出了一些改进的灰狼优化 (MGWO) 算法来改进 GWO。然而，只进行了渐进式的改进。因此，本文提出了一种改进的 GWO，称为增强灰狼优化 (EGWO)。所提出的方法在OGWO中添加了加权平均、跳跃行为和非线性收敛因子。特别是，由于真正的狼在捕猎时可能会进行扑扑动作，因此将扑扑的加入完善了 GWO 算法并产生了很大的改进。仿真和实验表明，EGWO可以大幅减少跟踪时间（高达OGWO的45.5%），与OGWO相比，动态跟踪效率可提高2%以上。此外，与一些现有的 GWO 和其他基于群的算法相比，EGWO 实现了最高的最大功率点。包含 pounceing 完善了 GWO 算法并产生了很大的改进。仿真和实验表明，EGWO可以大幅减少跟踪时间（高达OGWO的45.5%），与OGWO相比，动态跟踪效率可提高2%以上。此外，与一些现有的 GWO 和其他基于群的算法相比，EGWO 实现了最高的最大功率点。包含 pounceing 完善了 GWO 算法并产生了很大的改进。仿真和实验表明，EGWO可以大幅减少跟踪时间（高达OGWO的45.5%），与OGWO相比，动态跟踪效率可提高2%以上。此外，与一些现有的 GWO 和其他基于群的算法相比，EGWO 实现了最高的最大功率点。