Parameters determination of proton exchange membrane fuel cell stack electrical model by employing the hybrid water cycle moth‐flame optimization algorithm

Abstract

In order to properly control the operation of a fuel cell (FC), it is essential to have a precise model of the FC. In this paper, we merged the hybrid water cycle moth-flame optimization (WCMFO) algorithm and the notion of measurement uncertainty to extract the parameters of the proton exchange membrane fuel cell (PEMFC) by using current-voltage characteristics (I-V). The integration of the notion of uncertainty made the proposed approach more robust to disturbance. Consequently, a curve (I-V) of the model estimated more precise and very similar to the real curve. To validate the performance of our approach, three commercial PEMFCs with their empirical data (I-V) are examined as Ballard, NedSstack PS6, and BCS 500-W. The problem of PEMFC models with seven parameters was investigated. The performance analysis is carried out by applying the sum of the squared error (SSE) and the root mean squared error (RMSE) to compare the calculated and empirical data, our approach is affirmed by its large superiority (SSE and RMSE are in the order of 10 −29 and 10 −15 , respectively) compared to other methods recently published in Literature (SSE and RMSE are in the order of 10 −2 and 10 −2 , respectively).

Power System Stability Enhancement Using a Novel Hybrid Algorithm Based on the Water Cycle Moth-Flame Optimization

“…PID Based PSS PID-PSS ensures a robust control performance. It operates a function that provides an appropriate torque on the generator rotor by compensating the phase lag between the machine's electrical torque and the exciter input [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The transfer function of the PID_PSS is given by Equation (3):…”

“…The hybrid algorithm has proven its superior performance over the Artificial Bee Colony algorithm (ABC), Genetic Algorithm (GA), Cuckoo Search (CS), and Gravitational Search Algorithm (GSA) [14]. WCMFO has been used in several applications such as parameters determination of proton exchange membrane fuel cell stack electrical model [15], and optimal overcurrent relay coordination in Microgrid [16].…”

An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

“…15 To name some metaheuristics in their original version utilized to find PEMFC models' unknown parameters, they are particle swarm optimization (PSO), 16 hunger games search (HGS), 17 salp swarm optimizer (SSO), 18 satin bowerbird optimizer (SBO), 19 grey wolf optimizer (GWO), 20 multiverse optimizer (MVO), 21 slime mould algorithm (SMA), 22 atom search optimizer (ASO), 23 bird mating optimizer (BMO), 24 gradient-based optimizer (GBO), 25 and whale optimization algorithm (WOA). 26 In addition, a number of meta-heuristics developed by improving or hybridizing the original algorithms have been employed to address the studied problem, such as adaptive sparrow search algorithm (ASSA), 27 transferred adaptive differential evolution (TRADE), 28 developed coyote optimization algorithm (DCOA), 29 improved barnacles mating optimization (IBMO), 30 improved monarch butterfly optimization (IMBO), 31 improved salp swarm algorithm (ISSA), 32 improved artificial ecosystem optimizer (IAEO), 33 modified monarch butterfly optimization (MMBO), 34 modified gorilla troops optimizer (MGTO), 35 modified artificial electric field algorithm (mAEFA), 36 chaotic mayfly optimization algorithm (CMOA), 37 hybrid interior search algorithm (HISA), 38 hybrid vortex search algorithm and differential evaluation (HVSA-DE), 39 hybrid water cycle mouth-flame optimization (WCMFO), 40 and hybrid sine-cosine crow search algorithm (SCCSA). 41 The results presented in these studies show the potential of the meta-heuristic methods to obtain the optimal PEMFC parameters with a high level of accuracy.…”

An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells

“…There are several other parameter estimation PEMFC techniques based on metaheuristic algorithms: improved chaotic grey wolf optimization algorithm [26], modified farmland fertility optimizer [18], hunger games search algorithm [27], improved version of the Archimedes optimization algorithm [28], moth-flame optimization [19], Levenberg-Marquardt backpropagation algorithm [29], whale optimization algorithm [30], marine predator algorithm optimizer [31], pathfinder algorithm [32], hybrid water cycle moth-flame optimization algorithm [33], improved fluid search optimization algorithm [34], Seeker optimization algorithm [35], improved grass fibrous root optimization algorithm [36], developed coyote optimization algorithm [37], improved TLBO with elite strategy [38], developed owl search algorithm [39], modified artificial electric field algo-Energies 2021, 14, 7115 5 of 23 rithm [40], Supply-Demand-Based Optimization Algorithm [41], convolutional neural network optimized by balanced deer hunting optimization algorithm [42], and chaos game optimization technique [43].…”