Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Artificial Intelligence in Power Electronics

Background:

• Photovoltaic (PV) systems are crucial for renewable energy generation.
• Maximum Power Point Tracking (MPPT) algorithms are essential for maximizing PV energy output.
• Existing MPPT algorithms face challenges in efficiency and tracking accuracy under dynamic conditions.

Purpose of the Study:

• To introduce and evaluate a novel RNA algorithm for efficient Maximum Power Point Tracking (MPPT) in photovoltaic systems.
• To enhance the energy efficiency and tracking speed of PV systems.
• To validate the proposed algorithm's performance against established MPPT methods.

Main Methods:

• Development of a two-segment RNA algorithm comprising an artificial neural network for reference power generation and a Recursive Bit Assignment (RBA) network for variable duty cycle control.
• Simulation of the PV system using MATLAB under varying irradiance and temperature conditions.
• Comparative analysis of the proposed RNA algorithm with Perturb and Observe, Neural Network, and Adaptive Neural Inference System algorithms.

Main Results:

• The proposed RNA algorithm demonstrated fast tracking time, high energy efficiency, and accurate tracking of the true maximum power point.
• The algorithm maintained acceptable ripple levels in the PV system output.
• Superior performance of the RNA algorithm was observed across various scenarios, including irradiance variations, temperature fluctuations, and partial shading.

Conclusions:

• The RNA algorithm presents an efficient and robust solution for MPPT in photovoltaic systems.
• The proposed algorithm significantly improves PV system performance compared to existing methods, especially under challenging environmental conditions.
• The study validates the effectiveness of the RNA algorithm for practical PV system applications.