scholarly journals Planning for Distribution System with Grey Wolf Optimization Method

2020 ◽  
Vol 15 (4) ◽  
pp. 1485-1499
Author(s):  
Muhammad Mohsin Ansari ◽  
Chuangxin Guo ◽  
Muhammad Suhail Shaikh ◽  
Nitish Chopra ◽  
Inzamamul Haq ◽  
...  
Keyword(s):  
Distribution System ◽  
Optimization Method ◽  
Grey Wolf ◽  
Grey Wolf Optimization

scholarly journals GWO Based Optimal Reactive Power Coordination of DFIG, ULTC and Capacitors

2018 ◽  
Vol 11 (3) ◽  
pp. 805
Author(s):  
Mogaligunta Sankaraiah ◽  
Sanna Suresh Reddy ◽  
M Vijaya Kumar
Keyword(s):  
Wind Power ◽  
Distribution System ◽  
Power Plants ◽  
Reactive Power ◽  
Optimization Method ◽  
Major Influence ◽  
Grey Wolf Optimization ◽  
Wind Power Plants ◽  
Tap Changer ◽  
Load Tap Changer

<p>Wind is available with free of cost anywhere in the world, this wind can be used for power generation due to many advantages. This attracts the researchers to work on wind power plants. The presence of wind power plants on distribution system causes major influence on voltage controlled devices (VCDs) in terms of life of the devices. Therefore, this paper proposes grey wolf optimization method (GWO) together with forecasted load one day in advance. VCDs are on load tap changer (ULTC) and capacitors (CS), there are two main objectives first one is curtail of distribution network (DN) loss and second one is curtailing of ULTC and CS switching’s. Objectives are achieved by controlling the reactive power of DFIG in coordination with VCDs. The proposed method is planned and applied in Matlab/Simulink on 10KV practical system with DFIG located at different locations. To validate the efficacy of GWO, results are compared with conventional and dynamic programming methods without profane grid circumstances.</p>

scholarly journals Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Binghui Xu ◽  
Tzu-Chia Chen ◽  
Danial Ahangari ◽  
S. M. Alizadeh ◽  
Marischa Elveny ◽  
...  
Keyword(s):  
High Performance ◽  
Laboratory Data ◽  
Optimization Method ◽  
Good Alternative ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Bio Oil ◽  
Learning Machine

This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose were aggregated from previous sources and reports. The results of various analyses showed that this algorithm has a high ability to predict actual results. The calculated values of R2, MRE (%), MSE, and RMSE were obtained as 0.973, 1.98, 0.0568, and 0.241, respectively. According to the results of various analyses, the high performance of this model in predicting the output values was proved. Also, by comparing this model with the previously proposed models in terms of accuracy, it was observed that this model had a better performance. This algorithm can be a good alternative to costly and time-consuming laboratory data.

scholarly journals Network Reconfiguration of Primary Distribution System Using GWO Algorithm

2017 ◽  
Vol 7 (6) ◽  
pp. 3226 ◽  
Author(s):  
A. V. Sudhakara Reddy ◽  
M. Damodar Reddy ◽  
M. Satish Kumar Reddy
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Systems ◽  
Network Reconfiguration ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Real Power ◽  
Power Distribution System ◽  
Power Loss Reduction

This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.

Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method

Energy ◽  
2020 ◽  
Vol 193 ◽  
pp. 116616 ◽  
Author(s):  
Di Miao ◽  
Wei Chen ◽  
Wei Zhao ◽  
Tekle Demsas
Keyword(s):  
Parameter Estimation ◽  
Fuel Cells ◽  
Optimization Method ◽  
Pem Fuel Cells ◽  
Grey Wolf ◽  
Grey Wolf Optimization

Feature Selection Method based on Grey Wolf Optimization and Simulated Annealing

2019 ◽  
Vol 12 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Optimization Method ◽  
Selection Method ◽  
Attribute Selection ◽  
Binary Particle Swarm Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Whale Optimization

Feature selection sometimes also known as attribute subset selection is a process in which optimal subset of features are elected with respect to target data by reducing dimensionality and removing irrelevant data. There will be 2^n possible solutions for a dataset having n number of features which is difficult to solve by conventional attribute selection method. In such cases metaheuristic-based methods generally outruns the conventional methods. Therefore, this paper introduces a binary metaheuristic feature selection method bGWOSA which is based on grey wolf optimization and simulated annealing. The proposed feature selection method uses simulated annealing for enhancing the exploitation rate of grey wolf optimization method. The performance of the proposed binary feature selection method has been examined on the ten feature selection benchmark datasets taken from UCI repository and compared with binary cuckoo search, binary particle swarm optimization, binary grey wolf optimization, binary bat algorithm and binary hybrid whale optimization method. Statistical analysis and Experimental results validate the efficacy of proposed method.

scholarly journals On the application of nature-inspired grey wolf optimizer algorithm in geodesy

2020 ◽  
Vol 10 (1) ◽  
pp. 48-52
Author(s):  
M. Yetkin ◽  
O. Bilginer
Keyword(s):  
Optimization Problems ◽  
Optimization Method ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Absolute Value ◽  
Robust Parameter ◽  
First Time ◽  
Electronic Distance Measurement

AbstractNowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach.

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