The need to combat the increase in global warming is well taken by solar energy lead renewable energy resources. The techno-economic feasibility of solar systems in the form of photovoltaic (PV) generation is highly dependent upon its operating conditions. The nonlinear control problem is further worsened by partial shading (PS) environment causing major power losses. Bio-inspired maximum power point tracking (MPPT) control techniques, in literature, exhibit some major common drawbacks such as high tracking and settling time, oscillations at global maxima (GM), and local maxima (LM) trapping under PS conditions. This paper presents a novel search and rescue (SRA) optimization algorithm based MPPT control of PV systems to circumvent these shortcomings. Enhancement in performance of PV systems, fast and effective tracking of GM and very low oscillations at GM are the improvements exhibited by the proposed technique. Comprehensive case study-wise comparison of the SRA technique is made with recently developed grasshopper optimization (GHO), grey wolf optimization (GWO), particle swarm optimization (PSO), Cuckoo Search (CS), and PSO-gravitational search (PSOGS) based MPPT techniques that elaborate the qualitative, quantitative and statistical viewpoints. The experimental verification and field atmospheric data of Islamabad, the capital city of Pakistan, is utilized to validate the practicality of the proposed SRA based MPPT controller in real-world applications. As compared to the above-mentioned MPPT techniques, the proposed SRA achieves up to 8% more power and 5% more energy. Furthermore, the settling time and tracking time are shortened by up to 72% and 180% respectively. The simplicity of implementation, robustness, and up to 99.93% power tracking efficiency in steady-state are the prominent features of the proposed SRA control strategy.

以太阳能为主导的可再生能源很好地满足了应对全球变暖加剧的需求。光伏 (PV) 发电形式的太阳能系统的技术经济可行性在很大程度上取决于其运行条件。非线性控制问题因部分阴影 (PS) 环境而进一步恶化，从而导致主要功率损失。在文献中，仿生最大功率点跟踪 (MPPT) 控制技术表现出一些主要的共同缺点，例如跟踪和稳定时间长、全局最大值 (GM) 处的振荡以及 PS 条件下的局部最大值 (LM) 捕获。本文提出了一种基于光伏系统 MPPT 控制的新型搜索和救援 (SRA) 优化算法，以规避这些缺点。提高光伏系统的性能，对 GM 的快速有效跟踪和 GM 的极低振荡是所提出的技术所展示的改进。SRA 技术的综合案例研究与最近开发的基于蝗虫优化 (GHO)、灰狼优化 (GWO)、粒子群优化 (PSO)、布谷鸟搜索 (CS) 和 PSO 引力搜索 (PSOGS) 的比较阐述定性、定量和统计观点的 MPPT 技术。利用巴基斯坦首都伊斯兰堡的实验验证和现场大气数据来验证所提出的基于 SRA 的 MPPT 控制器在实际应用中的实用性。与上述 MPPT 技术相比，所提出的 SRA 实现了高达 8% 的功率和 5% 的能量。此外，稳定时间和跟踪时间分别缩短了 72% 和 180%。所提出的 SRA 控制策略的突出特点是实现的简单性、鲁棒性和高达 99.93% 的稳态功率跟踪效率。