Modern PV arrays are generally designed with bypass diodes to avoid damage. However, such arrays exhibit multiple peaks in their P-V characteristics under partial shading conditions. Owing to the limitation in the abi...
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Modern PV arrays are generally designed with bypass diodes to avoid damage. However, such arrays exhibit multiple peaks in their P-V characteristics under partial shading conditions. Owing to the limitation in the abilities of conventional maximumpowerpointtrackingalgorithms in such cases, the application of other optimisation algorithms has been explored. This study proposes a modified particle velocity-based particle swarm optimisation (MPV-PSO) algorithm for tracking the globalpower peak of the multiple peak P-V characteristics. The MPV-PSO algorithm is both adaptive and deterministic in nature. It eliminates the inherent randomness in the conventional PSO algorithm by excluding the use of random numbers in the velocity equation. The proposed algorithm also eliminates the need for tuning the weight factor, the cognitive and social acceleration coefficients by introducing adaptive values for them which adjust themselves based on the particle position. These adaptive values also solve problems like oscillations about the global best position during steady-state operation and particles getting trapped in local minima. The effectiveness of the proposed MPV-PSO algorithm is validated through MATLAB/Simulink simulations and hardware experiments.
The aim of this paper is to compare an optimal and a sub-optimal strategy for the Fuel Cell Hybrid power Systems based on maximumpowerpointtrackingalgorithms (with global feature or not) with the basic energy mana...
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The aim of this paper is to compare an optimal and a sub-optimal strategy for the Fuel Cell Hybrid power Systems based on maximumpowerpointtrackingalgorithms (with global feature or not) with the basic energy management strategy, namely the static Feed-Forward strategy considered as reference. The fuel economy is used as the unique performance indicator. The gaps in fuel economy for two Real-Time Optimization strategies based on global Extremum Seeking algorithm and Perturb & Observe algorithm are compared to highlight the advantages of the global optimization strategies. Up to 5 L fuel economy was obtained for optimal strategies compared to sub-optimal ones. Also, the gaps in fuel economy are estimated for the proposed strategies using two levels of the FC current slope. The results of this study obtained for constant load are validated on a variable and unknown profile of the load power as well.
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