Modelling and controlling of integrated photovoltaic-module and converter systems for partial shading operation using artificial intelligence

The thesis has three main themes: analysis and optimal design of Cuk DC-DC converters; integration of Cuk DC-DC converters with photovoltaic (PV) modules to improve operation during partial shading; and an artificial intelligence model for the PV module, permitting an accurate maximum power point (MPP) tracking in the integrated system. The major contribution of the thesis is the control of an integrated photovoltaic module and DC-DC converter configuration for obtaining maximum power generation under non-uniform solar illumination. In place of bypass diodes, the proposed scheme embeds bidirectional Cuk DC-DC converters within the serially connected PV modules. A novel control scheme for the converters has been developed to adjust their duty ratios, enabling all the PV modules to operate at the MPPs corresponding to individual lighting conditions. A detailed analysis of a step-down Cuk converter has been carried out leading to four transfer functions of the converter in two modes, namely variable input - constant output voltage, and variable output - constant input voltage. The response to switch duty ratio variation is shown to exhibit a non-minimum phase feature. A novel scheme for selecting the circuit components is developed using the criteria of suppressing input current and output voltage ripple percentages at a steady state, and minimising the time integral of squared transient response errors. The designed converter has been tested in simulation and in practice, and has been shown to exhibit improved responses in both operating modes. A Neuro-Fuzzy network has been applied in modelling the characteristics of a PV module. Particle-Swarm-Optimisation (PSO) has been employed for the first time as the training algorithm, with which the tuning speed has been improved. The resulting model has optimum compactness and interpretability and can predict the MPPs of individual PV modules in real time. Experimental data have confirmed its improved accuracy. The tuned Neuro-Fuzzy model has been applied to a practical PV power generation system for MPP control. The results have shown an average error of 1.35% compared with the maximum extractable power of the panel used. The errors obtained, on average, are also about four times less than those using the genetic-algorithm-based model proposed in a previous research. All the techniques have been incorporated in a complete simulation system consisting of three PV panels, one boost and two bidirectional Cuk DC-DC converters. This has been compared under the same weather conditions as the conventional approach using bypass diodes. The results have shown that the new system can generate 32% more power.