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 ( $6.83\times 10 ^{-4}$ , $2.06\times 10 ^{-4}$ , and $4.48\times 10 ^{-6}$ ) 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.

In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algorithm (TSA), particle swarm optimization (PSO), grey wolf optimization (GWO), flower pollination algorithm (FPA), and perturb and observe (P&O). A quantitative and qualitative analysis was carried out based on three distinct partial shading conditions. It was observed that the proposed TSA-PSO technique had remarkable success in locating the maximum power point and had quick convergence at the global maximum power point. The presented TSA-PSO MPPT algorithm achieved a PV tracking efficiency of 97.64%. Furthermore, two nonparametric tests, Friedman ranking and Wilcoxon rank-sum, were also employed to validate the effectiveness of the proposed TSA-PSO MPPT method.

Parameter extraction of the photovoltaic cell is a highly nonlinear complex optimization problem. This article proposes a new hybrid version of whale optimization and particle swarm optimization algorithm to optimize the photovoltaic cell parameters. The exploitation ability of particle swarm optimization with adaptive weight function is implemented in the pipeline mode with a whale optimization algorithm to improve its exploitation capability and convergence speed. The performance of the proposed hybrid algorithm is compared with six different optimization algorithms in terms of root mean square error and rate of convergence. The simulation result shows that the proposed hybrid algorithm produces not only optimized parameters at different irradiation levels (i.e., 1000 W/m², 870 W/m², 720 W/m², and 630 W/m²) but also estimates minimum root mean square error even at a low level of irradiations. Furthermore, the statistical analysis validates that the average accuracy and robustness of the proposed algorithm are better than other algorithms. The best values of root mean square error generated by the proposed algorithm are $7.1700\times {10}^{-4}$ and $9.8412\times {10}^{-4}$ for single-diode and double-diode models. It is observed that the estimated parameters based on the optimization process are highly consistent with the experimental data.

In this paper, average modeling and analysis of a dual-supercapacitor bank, actively balanced by a bidirectional buck-boost converter, is presented. In such a system, natural balancing is achieved when the converter is operated in open loop with 50% duty cycle, eliminating the need for measuring the voltage of each storage device. Nevertheless, excessive currents arise even for slight voltage misbalance because of the highly underdamped nature of the system. In order to remedy this drawback, bidirectional pulse-by-pulse inductor current limitation is introduced, which is equivalent to adding a peak-current-mode-like control loop to the system. Since the duty cycle never exceeds 50%, compensation ramp is not required to maintain stability. On the other hand, while the uncontrolled system dynamics is linear, introducing the current limit mechanism turns the closed-loop dynamics into a nonlinear one, burdening the analysis task and thus calling for suitable average model to perform fast simulations for system analysis. Therefore, dynamical equations of the system are developed in order to derive the switching-cycle-averaged model and reveal the tradeoff between current limit level, balancing time and efficiency for the worst case of imbalance. Simulations and experiments support the presented findings.