Modified Grasshopper Optimization Improved Maximum Power Point Tracking in Partially Shaded Photovoltaic Conditions

The drastically increased use of fossil fuels in recent years has led to environmental pollution and increased costs of using fossil fuels. There are many advantages to using PV as an energy source such as being environmentally friendly, freely available, and abundant in nature. The electrical potential produced by photovoltaic panels typically remains at a low level and is influenced by external factors like vegetation, structures, and atmospheric conditions. The problem at hand can be resolved through the implementation of an MPPT system, which enables photovoltaic systems to generate optimal output power even when subjected to partial shading, through the utilization of optimization algorithms. One of them is the Modified Grasshopper Optimization Algorithm (MGOA). This algorithm is a modified optimization method of the Grasshopper Optimization Algorithm (GOA). Modifications are made to the random value adjustment with a range of 0.5 to 1. In addition, the attraction between grasshoppers (f) and the length of the grasshopper pull (l) adjust to the value of the resulting Target power. MGOA demonstrates superior performance in generating PV output power with reduced ripple in comparison to GOA. Simulation results show that the MGOA algorithm can produce an accuracy rate of up to 99.85%. This result demonstrates that the PV-generated power is not trapped at the Local Maximum Power Point (LMPP).