A grid-tied PV-fuel cell multilevel inverter under PQ open-loop control scheme

Power generating entities’ connection to utility grids requires power converters to achieve high efficiency and low injected current harmonic distortion. The control of the power converter plays a crucial role in the grid-tied power converter’s performance. Various control techniques for grid-tied inverters ranging from classical to intelligent are introduced in several exist. Evaluating the current state and trend in grid-tied power inverters and related control methods, research shows that most works in this area focus on grid integration using the close-loop and other advanced control approaches. This is because these control methods are preferred since they provide adequate performance in case of uncertainties in the system. This investigation can aprove that PQ open-loop control technique can operate sufficiently and cost-effectively in grid-tied renewable and alternative power systems under normal operating conditions. Hence, this paper aims to assess the performance of a centralized single-stage grid-tied three-level diode clamped inverter connected to a PV-Fuel cell unit. An active and reactive power open-loop control scheme is employed to operate the inverter and achieves a current harmonic distortion below 5%. The system comprises a 150 kW/700 V PV, a 150 kW/1400 V fuel cell, a 265 kW multilevel inverter operating at a rated voltage of 415 V, and an LCL filter. Two operating scenarios are adopted to investigate the system’s responses further. In the first scenario, a local load of 509.2 kW is supplied from the PV-fuel cell inverter. The load also receives the grid’s power to meet the demand as the PV-fuel cell inverter provides only 265 kW. Whereas in the other scenario, the PV-fuel cell unit provides power to supply a local load while transporting the surplus to the grid. The results reveal the developed model’s good performance with a current harmonic distortion of 0.33%

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller