scholarly journals GREY WOLF OPTIMIZER BASED OPTIMAL PLACEMENT OF MULTIPLE FACTS DEVICES IN THE TRANSMISSION SYSTEM UNDER DYNAMIC LOADING SYSTEM

2021 ◽  
Vol 27 (1) ◽  
pp. 132-143
Author(s):  
YUSUF SAMUEL SUNDAY ◽  
OKORIE PATRICK UBEH ◽  
ABUBAKAR ADAMU SAIDU ◽  
ALHASSAN FAHAD
Keyword(s):  
Optimization Technique ◽  
Transmission System ◽  
Optimal Number ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Total System ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Facts Devices ◽  
Voltage Deviation

The application of grey wolf optimization technique for multiple FACTS placement is presented in this paper for the reduction of total system losses and minimization of voltage deviation via optimal placement of Flexible AC Transmission System (FACTS) device. Grey wolf optimization (GWO) technique is inspired by social hierarchy and hunting behaviour of wolves and offers a right balance between exploration and exploitation during the search for global optimal. Series-shunt FACTS device; unified power flow controller (UPFC) is considered as a formidable device that can provides an alternative option for the flexible controllability and improvement of power transfer capability of a transmission lines. The analyses were conducted by increasing the number of UPFC in the network in order to evaluate the optimal number of FACTS devices that would give the least loss under maximum loading and contingency conditions. The efficacy of this proposed technique is demonstrated on 31-bus, 330 kV Nigeria National Grid (NNG) using MATLAB environment. The results show that optimal placement of FACTS device along with optimization technique provides a promising solution to the high power loss and voltage deviation bedevilling Nigeria National Grid.

scholarly journals Application of Grey Wolf Optimizer for optimal placement of web advertisements

2019 ◽  
Vol 9 (2) ◽  
pp. 796-799
Keyword(s):  
Optimization Technique ◽  
Fine Tuning ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Web Pages ◽  
Grey Wolf ◽  
Major Factors

This paper throws enough exposure to reliable and optimal placement of advertisement by applying a nature inspired optimization technique. The grey-wolf optimizer helps us in determining how to assign web pages with relevant advertisements. Relevancy may increase by fine tuning the factors based on which our proposed mechanism is developed. We have taken into account fire major factors depending on which the optimizer is modeled. Later in this paper we have presented the robustness of each of this factor and how they influence the percentage of relevancy of advertisement placement

Optimal Placement of FACTS Devices for Voltage Stability Improvement with Economic Consideration Using Firefly Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 5-11
Author(s):  
S. Rajasekaran ◽  
S. Muralidharan
Keyword(s):  
Power Systems ◽  
Voltage Stability ◽  
Firefly Algorithm ◽  
Transmission System ◽  
Operating Conditions ◽  
Optimal Placement ◽  
Voltage Collapse ◽  
Facts Devices ◽  
The Cost ◽  
Bus System

Background: Increasing power demand forces the power systems to operate at their maximum operating conditions. This leads the power system into voltage instability and causes voltage collapse. To avoid this problem, FACTS devices have been used in power systems to increase system stability with much reduced economical ratings. To achieve this, the FACTS devices must be placed in exact location. This paper presents Firefly Algorithm (FA) based optimization method to locate these devices of exact rating and least cost in the transmission system. Methods: Thyristor Controlled Series Capacitor (TCSC) and Static Var Compensator (SVC) are the FACTS devices used in the proposed methodology to enhance the voltage stability of power systems. Considering two objectives of enhancing the voltage stability of the transmission system and minimizing the cost of the FACTS devices, the optimal ratings and cost were identified for the devices under consideration using Firefly algorithm as an optimization tool. Also, a model study had been done with four different cases such as normal case, line outage case, generator outage case and overloading case (140%) for IEEE 14,30,57 and 118 bus systems. Results: The optimal locations to install SVC and TCSC in IEEE 14, 30, 57 and 118 bus systems were evaluated with minimal L-indices and cost using the proposed Firefly algorithm. From the results, it could be inferred that the cost of installing TCSC in IEEE bus system is slightly higher than SVC.For showing the superiority of Firefly algorithm, the results were compared with the already published research finding where this problem was solved using Genetic algorithm and Particle Swarm Optimization. It was revealed that the proposed firefly algorithm gives better optimum solution in minimizing the L-index values for IEEE 30 Bus system. Conclusion: The optimal placement, rating and cost of installation of TCSC and SVC in standard IEEE bus systems which enhanced the voltage stability were evaluated in this work. The need of the FACTS devices was also tested during the abnormal cases such as line outage case, generator outage case and overloading case (140%) with the proposed Firefly algorithm. Outputs reveal that the recognized placement of SVC and TCSC reduces the probability of voltage collapse and cost of the devices in the transmission lines. The capability of Firefly algorithm was also ensured by comparing its results with the results of other algorithms.

scholarly journals Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

2021 ◽  
Vol 13 (6) ◽  
pp. 3314
Author(s):  
Rawan Shabbar ◽  
Anemone Kasasbeh ◽  
Mohamed M. Ahmed
Keyword(s):  
Markov Chain ◽  
Electric Vehicles ◽  
Process Model ◽  
Transportation Network ◽  
Optimal Placement ◽  
Death Process ◽  
Chain Model ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Net Profit

Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

scholarly journals A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
J. Avilés ◽  
J. C. Mayo-Maldonado ◽  
O. Micheloud
Keyword(s):  
Distributed Generation ◽  
Network Topology ◽  
Optimization Technique ◽  
Optimal Number ◽  
Optimal Placement ◽  
Evolutionary Approach ◽  
Power Losses ◽  
Network Configuration ◽  
Optimal Network ◽  
Particle Swarm Optimization Technique

A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.

scholarly journals A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
C. Mallika ◽  
S. Selvamuthukumaran
Keyword(s):  
Predictive Accuracy ◽  
Optimization Technique ◽  
Hybrid Optimization ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Diagnostic Model ◽  
Full Potential ◽  
Diabetes Diagnosis ◽  
Grey Wolf ◽  
Diagnosis System

AbstractDiabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a Crow Search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimization-based SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

Congestion management using grey wolf optimization in a deregulated power market

2021 ◽  
Keyword(s):  
Optimization Technique ◽  
Differential Evolution Algorithm ◽  
Congestion Management ◽  
Grey Wolf ◽  
Transmission Congestion ◽  
Power Sector ◽  
Grey Wolf Optimization ◽  
Congestion Cost ◽  
Real Coded Genetic Algorithm ◽  
Line Overload

Abstract Transmission congestion issues became more severe and difficult to control as the power sector became more deregulated. The grey wolf optimization algorithm is proposed to relieve congestion by rescheduling generation effectively, resulting in the least congestion cost. The selection of participating generators is based on sensitivity, and the proposed technique is used to determine the best-rescheduled output active power generation to minimize line overload. The IEEE-30 bus system is used to test the proposed optimization technique. It has been demonstrated that when compared to other algorithms like the real coded genetic algorithm, particle swarm optimization, and differential evolution algorithm, the proposed approach produces excellent results in terms of congestion cost.

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