Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

Performance Evaluation of Solar-PV-Based Non-Isolated Switched-Inductor and Switched-Capacitor High-Step-Up Cuk Converter

Solar photovoltaic (PV) is the most promising renewable energy source available on Earth. Three topologies based on a switched-inductor capacitor and non-isolated high-step-up Cuk converter have been proposed for solar PV. These topologies of the Cuk converter have higher boosting ability than conventional Cuk and boost converters and can reduce the voltage stress of the main switch. A small voltage rating and on-state resistance can give higher efficiency of the converter. The voltage boosting ability of all three topologies was compared to each other and with a conventional Cuk converter. The boosting capability of the third converter was 11 times at 0.75 duty cycle with a solar PV source. These converters do not use a coupled inductor and transformer, which leads to less volume, reducing coupling/core saturation loss, and thus the cost of the converter. A solar PV system of 12 volts was used for boosting with these converters for analysis of the feasibility of use with renewables. The three topologies of the switched-inductor and switched-capacitor (SLSC) Cuk converter were designed and simulated in MATLAB/Simulink to evaluate their effectiveness.

Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer

The global trend towards renewable energy sources, especially solar energy, has had a significant impact on the development of scientific research to manufacture high-performance solar cells. The issue of creating a model that simulates a solar module and extracting its parameter is essential in designing an improved and high performance photovoltaic system. However, the nonlinear nature of the photovoltaic cell increases the challenge in creating this model. The application of optimization algorithms to solve this issue is increased and developed rapidly. In this paper, a developed version of eagle strategy GBO with chaotic (ESCGBO) is proposed to enhance the original GBO performance and its search efficiency in solving difficult optimization problems such as this. In the literature, different PV models are presented, including static and dynamic PV models. Firstly, in order to evaluate the effectiveness of the proposed ESCGBO algorithm, it is executed on the 23 benchmark functions and the obtained results using the proposed algorithm are compared with that obtained using three well-known algorithms, including the original GBO algorithm, the equilibrium optimizer (EO) algorithm, and wild horse optimizer (WHO) algorithm. Furthermore, both of original GBO and developed ESCGBO are applied to estimate the parameters of single and double diode as static models, and integral and fractional models as examples for dynamic models. The results in all applications are evaluated and compared with different recent algorithms. The results analysis confirmed the efficiency, accuracy, and robustness of the proposed algorithm compared with the original one or the recent optimization algorithms.