Development of grey wolf optimization based modified fast terminal sliding mode controller for three phase...

Area of Science:

• Renewable Energy Systems
• Power Electronics
• Control Systems

Background:

• Conventional Maximum Power Point Tracking (MPPT) methods struggle with partial shading, leading to reduced solar energy conversion efficiency.
• Existing hybrid MPPT algorithms improve performance but suffer from slow tracking speeds and longer response times.

Purpose of the Study:

• To develop a novel hybrid MPPT algorithm that overcomes the limitations of conventional and existing hybrid methods.
• To enhance the efficiency and tracking performance of photovoltaic (PV) systems under partial shading conditions.

Main Methods:

• A new hybrid algorithm integrating the Grey Wolf Optimizer (GWO) and a modified fast terminal sliding mode controller (MFTSMC) was developed.
• The proposed algorithm was incorporated into a three-phase inverter-based converter (ILBC) PV system.
• Simulations were conducted using MATLAB/SIMULINK, and a hardware prototype was tested on 5 x 200 W solar PV modules.

Main Results:

• The GWO-MFTSMC algorithm demonstrated superior performance compared to Artificial Neural Network-Fast Terminal Sliding Mode Controller (ANN-FTSMC) and Particle Swarm Optimization-Fast Terminal Sliding Mode Controller (PSO-FTSMC) methods.
• Achieved a conversion efficiency of 99.72% in simulations and 96.15% in hardware realization.
• The proposed method showed improved tracking efficiency and faster response under partial shading.

Conclusions:

• The proposed GWO-MFTSMC hybrid algorithm offers a significant improvement for MPPT in PV systems, especially under challenging partial shading conditions.
• The integration of GWO and MFTSMC provides a robust and efficient solution for maximizing solar energy harvesting.
• The study validates the effectiveness of the proposed method through both simulation and hardware implementation.