Electrical characterization of photovoltaic modules using farmland fertility optimizer

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

“…In this work, a novel methodology based on Hybridized Arithmetic optimization Algorithm (HAOA) and the Efficient Newton Raphson (ENR) method is proposed to precisely extract the parameters of the single and doublediode models using real experimental data obtained under various weather conditions. The proposed HAOAENR is verified based on several statistical data and compared with three variants of the AOA algorithm and with recently wellpublished papers such as: HAOALW, HAOANR, AOAL, Farmland Fertility Optimization (FFANR) [119], Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimization (CHCLPSONR) [120], Improved Slime Mould optimizer (ImSMALW) [121], Marine Predators Algorithm (MPALW) [51], Self-adaptive Ensemble-based Differential Evolution algorithm (SEDEL) [95], and Time-Varying Acceleration Coefficients PSO (TVACPSONR) [61]. As per the literature, the output current equation based on RMSE statistical criteria is solved using the Linear (L), Lambert W function (LW), and Newton Raphson (NR) methods.…”

Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model

“…To verify the validity of the AEFA‐PV model, its optimal control variables are compared with conventional and recent metaheuristic based ODM and DDM for this module such as GCPSO, 16 TVACPSO, TLBO, GWO and ICA, 17 ISCE, 24 IWOA, 25 FFO, 40 NMSOLMFO, 52 PPSO, 55 HISA, 56 EHA‐NMS, 58 TSLLS, 59 and RF 60 as depicted in Table 3 (It may be useful declaring that the presented values of TDM model parameters are per module). It is a well‐meaning for remarking here that these optimal parameters are very near enough each other.…”

“…28 Additional to the above-said brief review, flow of researches in this regard have been recently published such as MSA, 29 opposition-based sine cosine, 30 salpswarm, 31 JAYA algorithm, 32 manta-rays foraging optimizer 33 GWO and cuckoo search, 34 collaborative swarm intelligence, 35 BSA, 36 improved ant lion optimizer, 37 WCA 38 and many more. [39][40][41][42][43][44][45] It can be seen that among all these papers in the literature, few of them have treated TDM to indicate its performance for example using WOA, 5 SFO, 9 PSO, 17 MSA, 29 WCA, 38 and FFO. 40 The later mentioned justifies that still more work should be paid in this regard to optimize the performance of TDM.…”

“…[39][40][41][42][43][44][45] It can be seen that among all these papers in the literature, few of them have treated TDM to indicate its performance for example using WOA, 5 SFO, 9 PSO, 17 MSA, 29 WCA, 38 and FFO. 40 The later mentioned justifies that still more work should be paid in this regard to optimize the performance of TDM. At the same context, on the basis of no-free-lunch theorem, not all optimization algorithms solve all problems, where one algorithm can successfully solve a certain engineering problem and at the same time fails to solve another optimization problem.…”

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

“…The input parameters and their ranges for both models are introduced in Table I and limits of PV cell are depicted in (28) [43], [59], [60]. The results of the CBMO for the mentioned PV units are compared with the results of other results of the published optimizers such as Salp Swarm algorithm (SSA) [61], improved slime mould algorithm (ImSMA) [62], interval branch and bound global optimization algorithm (IBBGOA) [63], Improved sine cosine approach (ISCA) [64], NelderMead moth flame (NMMFO) [65], Lévy flight Backtracking search algorithm (LFBSA) [66], farmland fertility optimizer (FFO) [48], Wind driven whale PSO (WDWPSO) [44], Guaranteed Convergence PSO (GCPSO) [67], logistic chaotic Rao-1 optimization algorithm (LCROA) [68], similarity-guided DE (SGDE) [69], Biogeography-based learning PSO (BLPSO) [69], grey wolf optimizer and cuckoo search (GWOCS) [70], Multiswarm spiral leader PSO (M-SLPSO) [71], NLBMA [45], directional permutation DE (DPDE) [72], enhanced marine predators algorithm (EMPA) [73], modified teachinglearning based optimization (MTLBO) [74], BMA [45], and lightning attachment procedure optimization (LAPO) [45]. Detailed is coming in the next few paragraphs.…”