scholarly journals Reactive power planning by opposition-based grey wolf optimization method

2018 ◽  
Vol 28 (6) ◽  
pp. e2551 ◽  
Author(s):  
Saurav Raj ◽  
Biplab Bhattacharyya
Keyword(s):  
Reactive Power ◽  
Optimization Method ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Reactive Power Planning

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 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 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 Maximum Loadability Enhancement with a Hybrid Optimization Method

2018 ◽  
Vol 7 (3) ◽  
pp. 323-330
Author(s):  
E. E. Hassan ◽  
T. K. A. Rahman ◽  
Z. Zakaria ◽  
N. Bahaman ◽  
M. H. Jifri
Keyword(s):  
Transmission Lines ◽  
Power Plants ◽  
Voltage Stability ◽  
Reactive Power ◽  
Optimization Technique ◽  
Optimization Method ◽  
Stressed Condition ◽  
Bacterial Foraging ◽  
Reactive Power Planning ◽  
The Cost

Nowadays, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and hence develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the cost of new reactive resources while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The comparison common optimization Meta-Heuristic Evolutionary Programming and original Bacterial Foraging techniques were chosen to verify the performance using the proposed ATBFO method. As a result, the ATBFO method is confirmed as the best suitable solution in solving the identified RPP objective functions.

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