scholarly journals Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

2021 ◽  
Vol 12 (1) ◽  
pp. 27
Author(s):  
Hassan Shokouhandeh ◽  
Sohaib Latif ◽  
Sadaf Irshad ◽  
Mehrdad Ahmadi Kamarposhti ◽  
Ilhami Colak ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Reactive Power Compensation ◽  
Voltage Profile ◽  
Bee Colony ◽  
Control Devices

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

scholarly journals Artificial bee colony algorithm for economic load dispatch with wind power energy

2016 ◽  
Vol 13 (3) ◽  
pp. 347-360 ◽  
Author(s):  
Amin Safari ◽  
Davoud Sheibai
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Large Scale ◽  
Artificial Bee Colony ◽  
Economic Load Dispatch ◽  
Power Networks ◽  
Power Generator ◽  
Bee Colony ◽  
Wind Power Generator

This paper presents an efficient Artificial Bee Colony (ABC) algorithm for solving large scale economic load dispatch (ELD) problems in power networks. To realize the ELD, the valve-point loading effect, system load demand, power losses, ramp rate limits and prohibited operation zones are considered here. Simulations were performed on four different power systems with 3, 6, 15 and 40 generating units and the results are compared with two forms of power systems, one power system is with a wind power generator and other power system is without a wind power generator. The results of this study reveal that the proposed approach is able to find appreciable ELD solutions than those of previous algorithms.

scholarly journals Optimal Placement and Sizing of SVC for Improving Voltage Profile of Power System

2011 ◽  
pp. 146-150
Author(s):  
Shraddha Udgir ◽  
Sarika Varshney ◽  
Laxmi Srivastava
Keyword(s):  
Power Systems ◽  
Power System ◽  
Capital Investment ◽  
Reactive Power ◽  
Test System ◽  
Vital Role ◽  
Optimal Location ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Stability Margins

In emerging electric power systems, increased transactions often lead to the situations where the system no longer remains in secure operating region. The flexible AC transmission system (FACTS) controllers can play a vital role in the power system security enhancement. However, due to high capital investment, it is necessary to place these controllers optimally in a power system. FACTS devices can regulate the active and reactive power control as well as adaptive to voltage-magnitude control simultaneously because of their flexibility and fast control characteristics. Placement of these devices at optimal location can lead to control in line flow and maintain bus voltages in desired level and so improve voltage profile and stability margins. This paper proposes a systematic method for finding optimal location of SVC to improve voltage profile of a power system. A contingency analysis to determine the critical outages with respect to voltage security is also examined in order to evaluate the effect of SVC on the location analysis. Effectiveness of the proposed method is demonstrated on IEEE 30-bus test system.

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 561-600
Author(s):  
Selcuk Aslan
Keyword(s):  
Big Data ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Abc Algorithm ◽  
Bee Colony ◽  
De Algorithm ◽  
Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

Multi-Objective VAR Dispatch Using Particle Swarm Optimization

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Mr. S. Durairaj ◽  
Dr. P.S. Kannan ◽  
Dr. D. Devaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Reactive Power ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Voltage Profile ◽  
Control Variables ◽  
Swarm Optimization ◽  
Voltage Stability Enhancement

Reactive Power Dispatch (RPD) is one of the important tasks in the operation and control of power system. The objective is to apply Particle Swarm Optimization (PSO) algorithm for arriving optimal settings of RPD problem control variables. Incorporation of PSO as a derivative free optimization technique in solving RPD problem significantly relieves the assumptions imposed on the optimized objective functions. The proposed algorithm has been applied to find the optimal reactive power control variables in IEEE 30-bus system and in a practical Indian power system with different objectives that reflect loss minimization, voltage profile improvement and voltage stability enhancement. The results of this approach have been compared with the results of Genetic Algorithm (GA). The results are promising and show the effectiveness and robustness of the proposed approach.

Steady State Load Shedding to Prevent Blackout in the Power System using Artificial Bee Colony Algorithm

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
R. Mageshvaran ◽  
T. Jayabarathi
Keyword(s):  
Steady State ◽  
Power System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Optimal Solution ◽  
Load Shedding ◽  
Solution Quality ◽  
Bee Colony ◽  
The Difference

Real and reactive power deficiencies due to generation and overload contingencies in a power system may decline the system frequency and the system voltage. During these contingencies cascaded failures may occur which will lead to complete blackout of certain parts of the power system. Under such situations load shedding is considered as an emergency control action that is necessary to prevent a blackout in the power system by relieving overload in some parts of the system. The aim of this paper is to minimize the amount of load shed during generation and overload contingencies using a new meta-heuristic optimization algorithm known as artificial bee colony algorithm (ABC). The optimal solution for the problem of steady state load shedding is done by taking squares of the difference between the connected and supplied real and reactive power. The supplied active and reactive powers are treated as dependent variables modeled as functions of bus voltages only. The proposed algorithm is tested on IEEE 14, 30, 57, and 118 bus test systems. The applicability of the proposed method is demonstrated by comparison with the other conventional methods reported earlier in terms of solution quality and convergence properties. The comparison shows that the proposed algorithm gives better solutions and can be recommended as one of the optimization algorithms that can be used for optimal load shedding.

scholarly journals Minimizing Power Loss Using Modified Artificial Bee Colony Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-118
Author(s):  
Nur Azlin Ashiqin Mohd Amin ◽  
Siti Hafawati Jamaluddin ◽  
Nur Syuhada Muhammat Pazil ◽  
Norwaziah Mahmud ◽  
Norhanisa Kimpol
Keyword(s):  
Power System ◽  
Power Loss ◽  
Power Flow ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Control Variable ◽  
Test System ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Reactive Power Flow

Electrical energy losses are found in any part of the power system. In the power system, it is essential to minimize the real power loss in transmission lines. The voltage deviation at the load buses through controlling the reactive power flow is very important. This ensures the secured operation of power systems regarding voltage stability and the economics of the process due to loss minimization. In this paper, the Modified Artificial Bee Colony (MABC) algorithm is implemented to solve the power system's optimal reactive power flow problem. Generator bus voltages, transformer tap positions, and settings of switched shunt of compensators are used as decision variables to control the reactive power flow. These control variable values are adjusted for loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system. The results are compared with Firefly algorithm (FA) and Artificial Bee Colony (ABC) algorithm method to prove the effectiveness of the newest algorithm. The power loss results are quite productive, and the algorithm is the most efficient than the other methods such as ABC algorithm and FA algorithm. These results are produced by Matlab 2017b.

scholarly journals Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3187
Author(s):  
Nien-Che Yang ◽  
Danish Mehmood ◽  
Kai-You Lai
Keyword(s):  
Power Systems ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Manhattan Distance ◽  
Third Order ◽  
Multi Objective ◽  
Power Filters ◽  
Bee Colony ◽  
Passive Power

Passive power filters (PPFs) are most effective in mitigating harmonic pollution from power systems; however, the design of PPFs involves several objectives, which makes them a complex multiple-objective optimization problem. This study proposes a method to achieve an optimal design of PPFs. We have developed a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm with a minimum Manhattan distance. Four different types of PPFs, namely, single-tuned, second-order damped, third-order damped, and C-type damped order filters, and their characteristics were considered in this study. A series of case studies have been presented to prove the efficiency and better performance of the proposed method over previous well-known algorithms.

Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices

2014 ◽  
Vol 3 (2) ◽  
pp. 38-58 ◽  
Author(s):  
Susanta Dutta ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Power System ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Facts Devices ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Bee Colony Optimization ◽  
Reactive Power Dispatch

This paper illustrates, for the first time, the use of artificial bee colony optimization (ABC) technique to study optimal reactive power dispatch (ORPD) in power system with the use of flexible AC transmission systems (FACTS). FACTS controller cannot only increase the power transmission capacity without installing new transmission lines, but they can also enhance voltage profile and reduce transmission loss in power system. Two types of FACTS devices namely, thyristor-controlled series capacitor (TCSC) and thyristor-controlled phase shifter (TCPS) are considered in this paper. A standard IEEE 30-bus test system with multiple TCSC and TCPS devices is used for two different objective functions to validate the performance of the proposed method. The simulation results demonstrates the ability of the ABC to produce better optimal solutions compared to particle swarm optimization with inertia weight approach (PSOIWA) and real coded genetic algorithm (RGA).

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