A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a...

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Control Systems

Background:

• Photovoltaic systems (PVS) are crucial for solar energy capture but face challenges in maximizing power output due to environmental fluctuations.
• Maximum Power Point Tracking (MPPT) techniques are essential for optimizing PVS performance, with Incremental Conductance (INC) being a recognized method.
• Adapting traditional MPPT methods like INC to dynamic environmental conditions remains a significant challenge in grid-connected PVS.

Purpose of the Study:

• To introduce an innovative adaptive MPPT technique, INC-FST, for grid-connected PVS.
• To enhance the classical INC method by integrating a PID controller updated via a fuzzy self-tuning controller.
• To dynamically regulate the boost converter signal for optimal DC-to-grid power transfer.

Main Methods:

• Developed an adaptive MPPT algorithm named INC-FST, combining INC with a fuzzy self-tuning PID controller.
• Implemented INC-FST to dynamically regulate the boost converter in grid-connected PVS.
• Conducted a comparative evaluation against conventional MPPT methods (INC, P&O, INC-FL) under diverse climate scenarios.

Main Results:

• The INC-FST algorithm achieved superior efficiencies of 99.80%, 99.76%, and 99.73% across three distinct climate scenarios.
• Demonstrated enhanced precision in control indices, including minimized overshoot and reduced rise time.
• Showcased significant improvements in maximizing PVS power output compared to traditional methods.

Conclusions:

• The proposed INC-FST adaptive MPPT technique offers a robust solution for optimizing grid-connected PVS performance.
• INC-FST effectively addresses the challenge of fluctuating environmental conditions, leading to higher energy yields.
• This advanced MPPT strategy provides a more precise and efficient method for solar energy conversion.