For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive optimization algorithms for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems in order to meet the energy demands of grid systems. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s

对于太阳能光伏（PV）系统中的最大功率点跟踪（MPPT），元启发式优化技术在过去几十年中得到了广泛应用。这是因为面对不断变化的环境因素，传统的 MPPT 方法无法监测全球 MPP。因此，有必要使用基于智能的控制算法来进行 MPPT 控制。本研究的主要目的是研究和评估三种前沿且独特的MPPT 控制优化算法的有效性，包括 Mongoose Optimization (MO)、Prairie Dog Optimization Algorithm (PDOA) 和混合 PDOA + MO。它还旨在选择最有效和最复杂的优化算法来满足电网系统的能源需求。本研究的最初贡献是用于 MPPT 控制的三种替代元启发式模型的实施和性能评估。这项工作的目标是最大限度地提高光伏系统的发电量，以满足电网系统的能源需求。本工作采用了三种不同的控制策略，包括 MO + MPPT、PDOA + MPPT 和 MO + PDOA + MPPT 来实现这一目标。为了评估有效性和改进的性能结果，这项工作考虑了许多参数，包括时间、误差、功率、THD 等。此外，通过全面的模拟和比较研究，MO、PDOA 和混合 PDOA + MO 技术的结果也在这项工作中得到了测试和证实。还对当前和拟议的监管模型的峰值时间、稳定时间和增加时间进行了比较。结果和生成的波形表明，混合 PDOA + MO 在效率提高 99.5%、低上升时间 1.6 s、低峰值时间 1.05 s、最小稳定时间 1.24 s、误差方面优于其他控制模型。速率为0.48，响应时间为0.005秒，跟踪时间为0.0019秒