Investigation and validation of an eleven level symmetric modular multilevel inverter using grey wolf optimization and differential evolution control algorithm for solar PV applications

Circuit World ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 117-127
Author(s):  
Albert Alexander Stonier ◽  
Gnanavel Chinnaraj ◽  
Ramani Kannan ◽  
Geetha Mani
Keyword(s):  
Differential Evolution ◽  
Optimization Techniques ◽  
Multilevel Inverter ◽  
Solar Pv ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Photovoltaic Array ◽  
Evolution Algorithms ◽  
Evolution Control

Purpose This paper aims to examine the design and control of a symmetric multilevel inverter (MLI) using grey wolf optimization and differential evolution algorithms. Design/methodology/approach The optimal modulation index along with the switching angles are calculated for an 11 level inverter. Harmonics are used to estimate the quality of output voltage and measuring the improvement of the power quality. Findings The simulation is carried out in MATLAB/Simulink for 11 levels of symmetric MLI and compared with the conventional inverter design. A solar photovoltaic array-based experimental setup is considered to provide the input for symmetric MLI. Field Programmable Gate Array (FPGA) based controller is used to provide the switching pulses for the inverter switches. Originality/value Attempted to develop a system with different optimization techniques.

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 An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique

2021 ◽  
Vol 6 (3) ◽  
pp. 911-931
Author(s):  
Abhishek Sharma ◽  
Abhinav Sharma ◽  
Averbukh Moshe ◽  
Nikhil Raj ◽  
Rupendra Kumar Pachauri
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Parameter Extraction ◽  
Solar Pv ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Pv Cell ◽  
Gravitational Search

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chittaranjan Paital ◽  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Prasant Ranjan Dhal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Simulation Software ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes

2020 ◽  
Vol 23 (13) ◽  
pp. 2850-2865 ◽  
Author(s):  
Parsa Ghannadi ◽  
Seyed Sina Kourehli ◽  
Mohammad Noori ◽  
Wael A Altabey
Keyword(s):  
Objective Function ◽  
Damage Identification ◽  
Optimization Techniques ◽  
Mode Shapes ◽  
Grey Wolf ◽  
Objective Functions ◽  
Expansion Process ◽  
Grey Wolf Optimization ◽  
Skeletal Structures ◽  
Equivalent Reduction

Vibration-based structural damage identification through optimization techniques has become an interesting research topic in recent years. Dynamic characteristics such as frequencies and mode shapes are used to construct the objective function. The objective functions based on only frequencies are not very sensitive to damage in large structures. However, objective functions based on both mode shapes and frequencies are very effective. In real measurement condition, the number of installed sensors is limited, and there are no economic reasons for measuring the mode shapes at all degrees of freedom. In this kind of circumstances, mode expansion methods are used to address the incompleteness of mode shapes. In this article, the system equivalent reduction and expansion process is applied to determine the unmeasured mode shapes. Two experimental examples including a cantilever beam and a truss tower are investigated to show system equivalent reduction and expansion process’ efficiency in estimating unmeasured mode shapes. The results show that the technique used for expansion is influential. Damage identification is formulated as an optimization problem, and the residual force vector based on expanded mode shapes is considered as an objective function. In order to minimize the objective function, grey wolf optimization and Harris hawks optimization are used. Numerical studies on a 56-bar dome space truss and experimental validation on a steel frame are performed to demonstrate the efficiency of the developed approach. Both numerical and experimental results indicate that the combination of the grey wolf optimization and expanded mode shapes with system equivalent reduction and expansion process can provide a reliable approach for determining the severities and locations of damage of skeletal structures when it compares with those obtained by Harris hawks optimization.

A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization

2021 ◽  
Vol 12 (2) ◽  
pp. 1-21
Author(s):  
Gokul Yenduri ◽  
Veeranjaneyulu Naralasetti
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Software Metrics ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Software Metric ◽  
New Variant ◽  
Conventional Models

Maintainability index (MI) is a software metric that offers measurements of the maintainability before release of the software by facilitating several substantial features of the system. In general, there is a common formula for determining the MI for all the software metrics to ensure the system's reliability. As it does not provide appropriate results regarding the reliability of the system, it is essential to focus on the next level of MI of software. Hence, this paper intends to allot an optimal weight and a constant to each software metric, which is optimized by grey wolf optimization (GWO). As a result, it can provide a new variant of MI by proposed enhanced model-GWO (EM-GWO). This optimized MI can ensure the efficiency of the respective software in such a way that it can provide an enhanced score from the system. Further, the proposed method is compared with conventional models such as enhanced model-generic algorithm (EM-GA), EM-particle swarm optimization (PSO), EM-ant bee colony (ABC), EM-differential evolution (DE), and EM-fire fly (FF), and the results are obtained.

scholarly journals Optimal Pattern Synthesis of Linear Antenna Array Using Grey Wolf Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Prerna Saxena ◽  
Ashwin Kothari
Keyword(s):  
Antenna Arrays ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Main Beam ◽  
Linear Antenna ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pattern Synthesis ◽  
Uniform Array ◽  
Linear Antenna Arrays

The aim of this paper is to introduce the grey wolf optimization (GWO) algorithm to the electromagnetics and antenna community. GWO is a new nature-inspired metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It has potential to exhibit high performance in solving not only unconstrained but also constrained optimization problems. In this work, GWO has been applied to linear antenna arrays for optimal pattern synthesis in the following ways: by optimizing the antenna positions while assuming uniform excitation and by optimizing the antenna current amplitudes while assuming spacing and phase as that of uniform array. GWO is used to achieve an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. GWO is also applied for the minimization of the first side lobe nearest to the main beam (near side lobe). Various examples are presented that illustrate the application of GWO for linear array optimization and, subsequently, the results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired evolutionary algorithms. The results suggest that optimization of linear antenna arrays using GWO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques.

scholarly journals SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
M. Q. Li ◽  
L. P. Xu ◽  
Na Xu ◽  
Tao Huang ◽  
Bo Yan
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
State Of The Art ◽  
Sar Image ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Sar Image Segmentation ◽  
Segmentation Approach

An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

A novel PV array interconnection scheme to extract maximum power based on global shade dispersion using grey wolf optimization algorithm under partial shading conditions

Circuit World ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Balraj R. ◽  
Albert Alexander Stonier
Keyword(s):  
Optimization Algorithm ◽  
Energy Systems ◽  
Maximum Power ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Partial Shading ◽  
Content Type ◽  
Pv Systems ◽  
Pv Array ◽  
Grey Wolf Algorithm

Purpose Partial shading causes significant power decreases in the PV systems. The purpose of this paper is to address this problem, connectivity regulation is designed to reduce partial shading problems. Design/methodology/approach In this approach, the partial shading was estimated and dispersed evenly on the whole array by global shade dispersion technique (GSD). The grey wolf algorithm was implemented for the interconnection of arrays by an efficient switching matrix. Findings After the implementation of the GSD technique using a grey wolf algorithm, the performance under different shading conditions was analyzed using the MatLab simulation tool. The results were compared with total cross-tied (TCT), Su Do Ku and the proposed method of reconfiguration, where the proposed method improves the maximum power of the PV system appropriately. Research limitations/implications This methodology uses any size of PV systems. Social implications Replacement of conventional energy systems with renewable energy systems such as solar helps the environment clean and green. Originality/value The GSD interconnection scheme using the grey wolf optimization algorithm has proved an improved output performance compared with the existing TCT and Sudoku based reconfiguration techniques. By comparing with existing techniques in literature, the proposed method is more advantageous for reducing mismatch losses between the modules of any size of the PV array with less operating time.

Grey wolf optimization based parameter selection for support vector machines

2016 ◽  
Vol 35 (5) ◽  
pp. 1513-1523 ◽  
Author(s):  
Sathish Eswaramoorthy ◽  
N. Sivakumaran ◽  
Sankaranarayanan Sekaran
Keyword(s):  
Design Methodology ◽  
Support Vector ◽  
Svm Classifier ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Vector Machines ◽  
Human Perceptions ◽  
Selection For

Purpose The purpose of this paper is to tune support vector machine (SVM) classifier using grey wolf optimizer (GWO). Design/methodology/approach The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters. Findings The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis. Originality/value A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram.

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