Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification

Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm

Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules

A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

The high level of penetration of photovoltaic (PV) systems into electric power grids increases rapidly due to many merits of such promising technology. In the simulation investigation of PV systems, an accurate PV model is vital, where it plays an important role through the dynamic analysis of these systems. The mathematical model of the PV module is a nonlinear I
-
 V
 characteristic including many unknown parameters as data provided by the PV manufacturers' are inadequate. This paper introduces a novel application of the whale optimisation algorithm (WOA) for estimating the parameters of the single, double, and three diode PV models of a PV module. The WOA-based PV models are validated by the simulation results, which are carried out under various environmental conditions using MATLAB program. The effectiveness of the WOA-based PV models is checked by comparing their results with that obtained using other optimisation methods. To obtain a realistic study, these simulation outcomes are compared with the experimental outcomes of a Kyocera KC200GT PV module. The WOA-based PV model is efficiently evaluated by comparing the absolute current error of this model with that obtained using other PV models. Using this meta-heuristic algorithm application, an accurate PV model can be obtained.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2018.5317

Parameters estimation of single- and multiple-diode photovoltaic model using whale optimisation algorithm

While addressing the issue of improving the performance of Photovoltaic (PV) systems, the simulation results are highly influenced by the PV model accuracy. Building the PV module mathematical model is based on its I-V characteristic, which is a highly nonlinear relationship. All the PV cells’ data sheets do not provide full information about their parameters. This leads to a nonlinear mathematical model with several unknown parameters. This paper proposes a new application of the Grasshopper Optimization Algorithm (GOA) for parameter extraction of the three-diode PV model of a PV module. Two commercial PV modules, Kyocera KC200GT and Solarex MSX-60 PV cells are utilized in examining the GOA-based PV model. The simulation results are executed under various temperatures and irradiations. The proposed PV model is evaluated by comparing its results with the experimental results of these commercial PV modules. The efficiency of the GOA-based PV model is tested by making a fair comparison among its numerical results and other optimization method-based PV models. With the GOA, a precise three-diode PV model shall be established.

https://www.mdpi.com/1996-1073/13/2/497/pdf

Parameter Estimation of Three Diode Photovoltaic Model Using Grasshopper Optimization Algorithm

Contribution of Photovoltaic (PV) systems is rapidly growing and great attention is given to the design of PV controllers to enhance both the performance of PV systems and the low voltage ride through (LVRT) capability during abnormal operational conditions. This article presents a novel application of the salp swarm algorithm (SSA) in order to optimally tune the PV controllers to enhance the LVRT of grid-connected PV systems. Enhancement of LVRT is indicated in percentage undershoots or overshoots, settling time and steady-state error of voltage response. A control strategy is applied to the DC-DC converter to obtain a maximum power point tracking operation through a proportional-integral (PI)-based open fractional voltage control. The grid side inverter controls both the point of common coupling voltage and the DC-link voltage through PI-based cascaded-voltage control. To get PI controller parameters that guarantee the optimum design of the controllers, the fitness function is optimized by using the SSA. The proposed optimal control scheme is tested under various fault scenarios and compared with other conventional optimization-based PI controllers to examine its validity under PSCAD environment. The effectiveness of the optimal control scheme is verified by comparing the simulation results with the practical results of the PV system.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2019.0726

Salp swarm algorithm-based optimal control scheme for LVRT capability improvement of grid-connected photovoltaic power plants: design and experimental validation

Parameter extraction of a solar cell is essential in the simulation and design calculation of photovoltaic (PV) systems. The mathematical model of the PV module is a non-linear I 
–
 V characteristic including several unknown parameters as the PV manufacturers’ data are not sufficient. This study presents a method for estimating the parameters of the single- and double-diode PV models of a PV module based on a nature-inspired meta-heuristic optimisation algorithm known as the whale optimisation algorithm (WOA). The validity of the proposed WOA-based PV model is verified by comparing its simulation results with the experimental results for the PV modules under different environmental conditions.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2017.0662

Omnia S. Elazab

Papers

Parameters estimation of single- and multiple-diode photovoltaic model using whale optimisation algorithm

Parameter Estimation of Three Diode Photovoltaic Model Using Grasshopper Optimization Algorithm

Salp swarm algorithm-based optimal control scheme for LVRT capability improvement of grid-connected photovoltaic power plants: design and experimental validation

Whale optimisation algorithm for photovoltaic model identification