In recent years, various control methods have been proposed for maximum power point tracking (MPPT) of photovoltaic (PV) power plants. Different MPPT methods for PV systems in the literature have been evaluated in terms of energy efficiency, energy conversion, dynamic performance and reliability in different environmental conditions. Among the various MPPT methods, the Artificial Neural Network (ANN) MPPT is one of the best methods due to its ability in noise rejection and no need for prior information of physical parameters. For implementing the ANN-based MPPT two input variables including temperature and irradiance and an output variable containing voltage of MPP are taken into account. In this paper, a hybrid shuffled frog leaping and pattern search (HSFL–PS) algorithm is used for optimizing ANN-based MPPT in a grid-tied PV system. The P&O approach is used for the tracking cycle procedure and starts a precise tracking scheme after training the ANN and specification of neuron weights. MATLAB/Simulink is utilized for simulation tests to confirm the performance of the offered MPPT method. The outcomes from simulation tests validate the improved performance of the recommended MPPT in comparison with the conventional methods with a fast response of 011 sec.

近年来，针对光伏（PV）发电厂的最大功率点跟踪（MPPT）提出了各种控制方法。文献中针对光伏系统的不同 MPPT 方法在不同环境条件下的能源效率、能量转换、动态性能和可靠性方面进行了评估。在各种 MPPT 方法中，人工神经网络 (ANN) MPPT 是最好的方法之一，因为它具有抑制噪声的能力并且不需要物理参数的先验信息。为了实现基于 ANN 的 MPPT，需要考虑两个输入变量，包括温度和辐照度，以及一个包含 MPP 电压的输出变量。在本文中，混合混洗青蛙跳跃和模式搜索（HSFL-PS）算法用于优化并网光伏系统中基于 ANN 的 MPPT。P& O 方法用于跟踪循环过程，并在训练 ANN 和神经元权重规范后启动精确的跟踪方案。MATLAB/Simulink 用于仿真测试以确认所提供的 MPPT 方法的性能。模拟测试的结果验证了推荐的 MPPT 与具有 011 秒快速响应的传统方法相比的改进性能。