Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Abstract

When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I)voltage (V) and power (P) voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer's datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1. 8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

“…This section of the paper comprehensively deliberates the results obtained by the proposed I-AVO algorithm and other selected algorithms, such as AVO, SMA, MPA, ( 39) [83], Chaotic GBO [64], Chaotic Jaya [108], and OBL-GWO. All the selected algorithms are combined with the NR method to get a fair result for performance comparison.…”

“…Any SO situation can be widened to more complex cases, such as multi-objective, many-objective, hybrid, robust optimization, large-scale, and fuzzy optimization [51][52][53]. As a substitute to deterministic methods, there are plenty of population-based optimizers, such as Teaching-Learning Based Optimizer (TLBO) [54], Particle Swarm Optimizer (PSO) [55], Differential Evolution (DE) [56], Sine-Cosine Optimizer (SCO) [57], Gray Wolf Optimizer (GWO) [58,59], Seagull Optimization (SO) [60], Political Optimizer (PO) [61], Rao Algorithm (RAO) [62], Whale Optimizer (WO) [63], Gradient-Based Optimizer (GBO) [64], Equilibrium Optimizer (EO) [65,66], Moth-Flame Optimizer (MFO) [67], Slime Mold Algorithm (SMA) [68], Marine-Predator Algorithm (MPA) [69,70], Hunger Games Search (HGS) [71,72], Runge-Kutta Optimizer (RKO) [73], which are recognized as nature-inspired and evolutionary methods. A robust evolutionary foundation and a genre free of metaphors are required for the most reliable methods.…”

“…As a result, improving these methods is difficult because it necessitates more exertion in the optimization process, computing, statistics, and noise impacts. The chaotic drifted JAYA algorithm [79], improved JAYA [80], linear population size reduction based on success history adaptive DE (LSHADE) [46], GWO-PSO [81], GWO-Cuckoo search [82], chaotic GBO (CGBO) [64], Opposition-Based Learning EO (OBLEO) algorithm [83], OBLGWO [84], and others are some examples. The PV parameter estimation optimization problem is a hot topic right now.…”

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

“…The leader's positions are improved based on random function as in (25), and (26). The best global score and position are identified as in (27).…”

Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method

“…), and population-based algorithms (Teaching-Learning-based Optimizer (TLBO) and its variants [82,83], Jaya algorithm and its variants [84][85][86], Political Optimizer (PO) [87], Rao algorithm (RAO) [88][89][90], Coyote Optimization Algorithm (COA) [91], Gradient-Based Optimizer (GBO) [92,93] etc.,). Some hybrid variants, such as PSO-GWO [94], PSO-WOA [95], GSA-PSO [96] etc., and some improved variants of algorithms based on opposition-based learning [15,86], Gaussian Mutation (GM) [97], Cauchy Mutation (CM) [57,98], Chaotic GBO [99], Opposition-based GBO [100], Chaos random number generation [22,49,101], Nelder-Mead Simplex [78,102] etc. are also applied to parameter estimation problem of solar cell and modules.…”