Opposition-Based Tunicate Swarm Algorithm for Parameter Optimization of Solar Cells

Abstract

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum (6.83×10 -4 , 2.06×10 -4 , and 4.48×10 -6 ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator

“…Therefore, the concept of opposition learning was proposed, mainly to generate relative solutions of feasible solutions, evaluate relative solutions, and select better candidate solutions to improve the search capability of traditional swarm intelligence optimization techniques in solving nonlinear optimization problems. In recent years, OBL has been successfully applied to the genetic algorithm, sine cosine algorithm, ant lion optimizer, and other intelligent optimization algorithms [27][28][29][30][31][32][33]. Oppositional learning is defined as o + -lb ub P P = (16) where P = (p 1 , p 2 , …p n ) is a point in n-dimensional space, and [ , ]…”

Fennec Fox Optimization: A New Nature-Inspired Optimization Algorithm

“…Exploitation is the ability of an algorithm to search locally in the neighborhood of the previously obtained solution to find quasi-optimal solutions much closer to the exact global optimal. What is significant about these two capabilities is that an algorithm can converge to suitable solutions by providing the appropriate balance between exploration and exploitation [7].…”

“…Generate the target position intended for the escape of the 𝑖th fennec fox and evaluate its objective function using (7). 12.…”

A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions

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