High voltage gain DC-DC boost converters are used widely in grid-connected applications through integration with the Renewable Energy Sources (RESs). Photovoltaic (PV) arrays or Fuel Cells (FCs) generate a limited value of the DC voltages and then for high power and high voltage applications, at the first stage, these voltages should be increased. This study presents a high gain, single-switched, and efficient DC-DC boost converter using the switched-capacitor and switched-inductor cells. These blocks easily can enhance the voltage and present an input current with the less values of the ripples. This will be done through replacing the location of the input inductors and by applying a switched-inductor block. Magnetizing in parallel and demagnetizing in series for the inductors present the smaller input current stresses. A single switch is used for the proposed boost converter that directly decreases the complexity of the control circuit for obtaining a fixed DC voltage at the output side for flexible input voltages or loads. More voltages will be presented by the used switched-capacitor cell simply by adding several diodes and capacitors. A deep and detailed mathematical analysis will be presented for continuous (CCM) and discontinuous conduction modes (DCM) and a 200 W laboratory-scaled prototype is presented. The results of the hardware tests confirm the correctness of the theoretical analysis and simulation results.

Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic (PV) system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts, efficacy, flexibility, and easy-to-implement procedural frameworks. However, the nonlinearity and complexity of the PV parameter identification caused swarm and evolutionary optimizers to exhibit Immaturity in the obtained solutions. In this study, an effective metaheuristic algorithm based on tunicate swarm optimization (TSA) is proposed for parameter identification of PV models. The proposed improved algorithm (ITSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm’s exploration ability while also preventing premature convergence. The suggested algorithm’s performance is confirmed using ten mathematical test functions and the outcomes are compared with TSA as well as some effective optimization algorithms. The proposed ITSA optimally identifies various parameters in the PV model, such as single diode (SDM), double diode (DDM), and PV modules. Based on the comprehensive comparisons, results indicate that the improved ITSA algorithm has higher convergence accuracy and better stability than the original TSA and other studied algorithms.

This paper aims to introduce an adaptive metaheuristic algorithm based on tunicate swarm optimization (TSA) for effectively solving global optimization problems and the optimum design of a shallow spread foundation. The proposed adaptive tunicate swarm optimization (ATSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm’s exploration ability while also preventing premature convergence. The suggested algorithm’s performance is confirmed using a set of 23 mathematical test functions of well-known CEC 2017 and the outcomes are compared with TSA as well as some effective optimization algorithms. In addition, the new method automates the optimum design of shallow spread foundations while taking two objectives into account: cost and CO2 emissions. The analysis and design procedures are based on both geotechnical and structural limit states. A case study of a spread foundation has been solved using the proposed methodology, and a sensitivity analysis has been conducted to investigate the effect of soil parameters on the total cost and embedded CO2 emissions of the foundation. The simulation results demonstrate that, when compared to other competing algorithms, ATSA is superior and may produce better optimal solutions.