Solar Photovoltaic (PV) system is an excellent renewable energy solution in today’s scenario. Harvesting maximum power from the solar PV system under dynamic meteorological conditions is a challenging task. Numerous bio-inspired Maximum Power Point Tracking (MPPT) strategies have been proposed in the literature. The conventional methods of MPPT control are easy and simple to implement, but has drawbacks such as steady state oscillations and inability to track the maximum power under swiftly varying irradiances and partial shading conditions. This paper proposes a Grasshopper Optimization Algorithm (GOA) tuned MPPT technique with the objective of obtaining optimal duty cycle, D, to control a DC–DC boost converter. The efficacy of the proposed system under start up transients, line disturbances, load disturbances, servo conditions and partial shading conditions are evaluated and compared with the conventional Perturb and Observe (P&O) based MPPT and the familiar Particle Swarm Optimization (PSO) based MPPT algorithm using MATLAB Simulink platform. It is observed that the proposed GOA tuned MPPT technique gives good steady state and dynamic response compared to P&O and PSO based MPPT algorithms, verified in terms of rise time, settling time, percentage maximum overshoot, Integral Squared Error and Integral Absolute Error.

在当今的情况下，太阳能光伏（PV）系统是一种出色的可再生能源解决方案。在动态气象条件下从太阳能光伏系统中获取最大功率是一项艰巨的任务。文献中已经提出了许多生物启发的最大功率点跟踪（MPPT）策略。传统的MPPT控制方法易于实现，但存在诸如稳态振荡以及无法在快速变化的辐照度和部分阴影条件下跟踪最大功率的缺点。本文提出了一种蚱hopper优化算法（GOA）调谐的MPPT技术，旨在获得最佳占空比D，以控制DC-DC升压转换器。评估了拟议系统在启动瞬态，线路干扰，负载干扰，伺服条件和部分阴影条件下的功效，并将其与基于常规扰动和观察（P＆O）的MPPT和基于粒子群优化（PSO）的基于MPPT算法进行了比较使用MATLAB Simulink平台。可以看出，与基于P＆O和PSO的MPPT算法相比，提出的GOA调谐MPPT技术具有良好的稳态和动态响应，并在上升时间，稳定时间，最大过冲百分比，积分平方误差和积分绝对误差方面得到了验证。