Abstract
The fuel cell and photovoltaic (PV) play important roles in the future of power supply because they are clean and zero-radiation energy sources. Therefore, many advanced countries have invested heavily in these technologies to reduce the dependence on fossil fuels or nuclear energy. To maximize effectiveness, the two energy sources need to be combined with DC/DC converter and digital controller.This work employs fuzzy logic control for hybrid fuel cell and PV power systems. In the hybrid fuel cell system, the fuzzy sliding surface control implemented through a digital signal processor is used to simplify the rule base and achieve stable output voltage under different load situations. Experimental results show that the output voltage of the hybrid fuel cell system is kept stable at a constant value even though the rule number is only a square root multiplied in relation to the standard fuzzy control. Because of the simpler rule base of the sliding surface fuzzy control, the gate count of logic synthesis with a field-programmable gate array (FPGA) can be reduced. Based on experimental results, fuzzy sliding surface control is successfully applied to a FPGA system to achieve stable output voltage. Subsequently, the proposed algorithm is simulated and tested on hybrid fuel cell systems in digital workstations. Simulation results based on the register transfer level (RTL) are highly consistent with our design target.
In PV power systems, output efficiency is determined by the performance of maximum power point tracking (MPPT) algorithms. The perturbation and observation method (P&O) and the incremental conductance method (INC) are commonly adopted in PV power systems. The above methods have slower speeds for tracking the optimal operating point and exhibit continuous oscillation around the optimal operating point which leads to power loss and system instability. Therefore, type-1 fuzzy control has been introduced to increase tracking speeds and reduce the steady-state oscillation of P&O and INC algorithms. However, this fuzzy control cannot suppress the noise. Here, the interval type-2 fuzzy control algorithm is chosen for the MPPT because it provides faster tracking capability and prevents noise interference. Experimental results indicate that the efficiencies achieved by the interval type-2 fuzzy control algorithm are 98.9% under constant weather conditions and 99.2% under varying weather conditions, which are more effective compared to those of INC and type-1 fuzzy control algorithms.
| Date of Award | 2013 |
|---|---|
| Original language | Chinese (Traditional) |
| Supervisor | Chih-Lung Lin (Supervisor) |