Ingenious golden section search MPPT algorithm for PEM fuel cell power system

Neural Computing and Applications - Among several types of fuel cells available in the market, proton exchange membrane fuel cell (PEMFC) is characterized by low operating temperature, high...     

Microgrid management using hybrid inverter fuzzy-based control

Neural Computing and Applications - Microgrid systems are becoming a very promising solution to meet the power demand growth especially in remote areas where diesel generators (DG) are commonly...     

Indirect P&O type-2 fuzzy-based adaptive step MPPT for proton exchange membrane fuel cell

Neural Computing and Applications - The output power of the fuel cell depends on the operating conditions, such as the temperature and membrane water content. Therefore, a robust maximum power...     

How fuzzy logic can improve PEM fuel cell MPPT performances?

This paper addresses the development of new variable step size fuzzy based MPPT controller. In this study, the fuzzy logic approach is firstly used to auto-scale the variable step size of the Incremental Conductance (IC) MPPT controller. Secondly, the proposed variable step size fuzzy based MPPT controller is used to track the output power of the PEM fuel cell system composed of 7 kW fuel cell supplying a 50Ω resistive load via a DC-DC boost converter controlled using the proposed MPPT. The proposed variable step size fuzzy-based MPPT controller is compared to the conventional fixed step size IC, the variable step size IC and the fuzzy scaled variable step size IC MPPTs using the implemented Matlab/Simulink PEM Fuel Cell power system model. Comparative simulation results between the four studied MPPTs show better performances for the proposed fuzzy based variable step size MPPT in term of: response time reduction between 3.6% and 82.35%; overshoot reduction between 34.55% and 100%; and ripple reduction between 70.93% and 100%, improving as consequence the fuel cell lifetime affected by high current ripple.     

Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions

This paper concerns the study and simulation of a PV array self-organizing configuration. It introduces a new method to reconfigure the PV array using a genetic algorithm in order to maximize the output power as well as reducing the number of switching. The proposed method involves the simulation of a PV array composed of 16 panels 4 strings with 4 panels in series and associated parallel, as well as an algorithm that controls the improvement of the overall performance under different shading conditions. The obtained results using MATLAB/Simulink simulation show improvement rating varying between 106.49 and 171.03%, which is huge compared to a static configuration operating below the total available power. Another important point is the number of iterations needed to find the optimal configuration (between 6 and 132 for a population of 50 configurations tested at each generation); this means that in the worst case (132 iterations), the proposed algorithm performed 132 × 50 = 6600 configurations instead of 16¹⁶ = 1.84 × 10¹⁹ necessary in case of exhaustive search to test all possible configurations. This last point is very important in the implementation of the proposed system in auto-tuning of the system in real-time condition. Besides using genetic algorithm to track the optimal configuration, our main contribution consists of improving the output power while reducing the number of switching by keeping PV modules, if possible, in same position (0 switching) or on the same line/column (1 switching) in few iteration needing only two sensors one for the voltage and another for the current of the PV array.