Control Strategies for the DAB Based PV Interface System.

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Power Electronics

Background:

• Photovoltaic (PV) arrays require efficient interface systems to maximize energy harvest.
• The Dual Active Bridge (DAB) converter is a key component in power conversion for PV systems.
• Existing control strategies for DAB converters face challenges in stability and performance.

Purpose of the Study:

• To propose and evaluate two novel control strategies for a DAB converter-based PV interface system.
• To enhance maximum power point tracking (MPPT) efficiency in PV arrays.
• To compare the performance of feedback (PI-based) and feed-forward (ANN-based) control strategies.

Main Methods:

• Implementation of a PI controller with Perturb and Observe (P&O) MPPT for PV terminal voltage regulation.
• Development of an Artificial Neural Network (ANN) for direct phase shift angle control to achieve maximum power.
• Modeling and simulation using MATLAB/SIMULINK and EMTDC/PSCAD, followed by experimental validation.

Main Results:

• Both proposed control strategies demonstrated accurate and fast responses in simulations.
• The ANN-based feed-forward strategy exhibited superior dynamic performance compared to the PI-based feedback strategy.
• Experimental results validated the effectiveness of the proposed PV interface system.

Conclusions:

• The proposed DAB converter interface system effectively maximizes power extraction from PV arrays.
• The ANN-based feed-forward control strategy provides a stable and high-performance solution for PV MPPT.
• The developed system shows significant potential for improving the efficiency of solar energy conversion.