Optimal Sizing of Stand-alone PV System Using Grey Wolf optimization

2019 ◽  
Author(s):  
Ashraf Khalil ◽  
Ali Asheibi
Keyword(s):  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pv System ◽  
Optimal Sizing

scholarly journals Performance and Optimization of MPPT Techniques for Modeling, and Control of Solar PV System

2020 ◽  
Vol 8 (6) ◽  
pp. 920-924
Keyword(s):  
Output Power ◽  
Optimization Techniques ◽  
Maximum Power ◽  
Solar Pv ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pv System ◽  
Power Requirement ◽  
Point Tracking ◽  
Non Linear

today the power sector requirement is increasing continuously and reserve of fossil fuel is limited so we have already moved toward renewable generation. Demand of renewable sources of energy should be our prime focus to mitigate the power requirement. The solar power generation is of the best choice for power generate because it is freely available. Maximum power point tracking (MPPT) techniques is one of the most useful method to get maximum power at any instant of time. Classical MPPT techniques fail to provide an accurate output power thus; optimization of MPPT techniques play an important role in maximization of output power. Considering the dependency on renewable energy uses, this paper, presents various types of optimization to track MPPT techniques implemented on Photovoltaic (PV) system. These techniques applied for solar system is helpful in designing and improving efficiency of the PV system. Due to non linear characteristics of PV array a non-linear controller is most suitable for MPPT applications. The paper, first describe different types of characteristics of solar PV cell used for MPPT technique and followed by different optimization techniques incorporating fazzy, neural network Grey Wolf Optimization (GWO), Simplified Firefly Algorithm (SFA), Enhanced Grey Wolf Optimization (EGWO), Particle Swarm Optimization (PSO), etc have been discussed. Performance has been analyzed based on efficiency, tracking speed, converter used, application and implementation cost etc.

scholarly journals Maximum Power Point Tracking using Grey Wolf Technique Under Fast-Changing Irradiance

2020 ◽  
Vol 9 (11) ◽  
pp. 365-371
Keyword(s):  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Nature Inspired Algorithm ◽  
Power Point

In this paper, maximum power point tracking (MPPT) using Grey wolf optimization (GWO) algorithm is presented using MATLAB/Simulink. As we know that meta-heuristic or nature-inspired algorithm has proven to be superior in performance compared to the conventional MPPT methods. Grey Wolf optimization algorithm is a meta-heuristic algorithm based on the hunting behaviour of grey wolves. The proposed system includes modelling of PV system under changing irradiance and the MPPT control is driven by GWO algorithm. Most of the conventional MPPTs are unable to track multiple peaks and also shows oscillations on the output side, for this reason proposed MPPT algorithm is used in this paper. For eliminating oscillations, this algorithm has proven to be better compared to perturb and observe (P&O) and particle swarm optimization (PSO). The results are compared in terms of output power.

scholarly journals A Novel MPPT Design for a Partially Shaded PV System Using Spotted Hyena Optimization Algorithm

2021 ◽  
Vol 11 (6) ◽  
pp. 7776-7781
Author(s):  
B. Korich ◽  
A. Benaissa ◽  
B. Rabhi ◽  
D. Bakria
Keyword(s):  
Spotted Hyena ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Systems ◽  
Remarkable Improvement ◽  
Perturb And Observe ◽  
Power Point

Partial shading is a common problem in photovoltaic (PV) systems, known for its difficulty. Numerous attempts have been conducted to mitigate this problem. Some of these efforts deploy metaheuristic optimization with a view to tracking the multiple-peak P–V curve in a partial shading PV system. Hence, this paper proposes a novel metaheuristic algorithm to track the maximum power point of PV systems using the Spotted Hyena Optimization (SHO) algorithm. When evaluated, the SHO algorithm proved to be very fast, robust, and accurate in standard conditions, Partial Shading Conditions (PSCs), and irradiance variations. Also, the results reveal a remarkable improvement in the performance when we compare the SHO algorithm with the Grey Wolf Optimization (GWO) algorithm and the Perturb and Observe (P&O) algorithm.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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