Optimal reactive power dispatch using ant colony optimization algorithm

2011 ◽  
Vol 93 (2) ◽  
pp. 103-116 ◽  
Author(s):  
A. A. Abou El-Ela ◽  
A. M. Kinawy ◽  
R. A. El-Sehiemy ◽  
M. T. Mouwafi
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

scholarly journals BAT algorithm and Ant Colony Optimization based Optimal Reactive Power Dispatch to Improve Voltage Stability

2017 ◽  
Vol 2 (6) ◽  
pp. 27 ◽  
Author(s):  
Rayudu Katuri ◽  
Guduri Yesuratnam ◽  
Askani Jayalaxmi
Keyword(s):  
Ant Colony Optimization ◽  
Voltage Stability ◽  
Reactive Power ◽  
Optimization Technique ◽  
Bat Algorithm ◽  
Ant Colony ◽  
Voltage Profile ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

One of the important tasks of a power system engineer is to run the system in safe and reliable mode for secure operation with increase in loading. So, it is significant to perform voltage stability analysis by optimal reactive power dispatch with Artificial Intelligence (AI) techniques. This paper presents the application of Ant Colony Optimization (ACO) and BAT algorithms for Optimal Reactive Power Dispatch (ORPD) to enhance voltage stability. The proposed ACO and BAT algorithms are used to find the optimal settings of On-load Tap changing Transformers (OLTC), Generator excitation and Static Var Compensators (SVC) to minimize the sum of the squares of the voltage stability L– indices of all the load buses. By calculating system parameters like L-Index, voltage error/deviation and real power loss for the practical Equivalent of Extra High Voltage (EHV) Southern Region Indian 24 bus system, voltage profile is improved and voltage stability is enhanced. A comparative analysis is done with the conventional optimization technique like Linear Programming (LP) for the given objective function to demonstrate the effectiveness of proposed ACO and BAT algorithms. 

scholarly journals Pareto-based allocations of multi-type flexible AC transmission system devices for optimal reactive power dispatch using Kinetic Gas Molecule Optimization algorithm

2020 ◽  
Vol 53 (1-2) ◽  
pp. 239-249 ◽  
Author(s):  
Pradeep Panthagani ◽  
R Srinivasa Rao
Keyword(s):  
Optimization Algorithm ◽  
Reactive Power ◽  
Transmission System ◽  
Flexible Ac Transmission ◽  
Flexible Ac Transmission System ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Gas Molecule ◽  
Reactive Power Dispatch ◽  
Ac Transmission

Optimal reactive power dispatch is one of the key factors to attain cost-effective and stable functioning of power system. It is a complicated non-linear optimization issue with a combination of discrete and continuous control variables. Due to this complex feature of optimal reactive power dispatch, optimization technique has become an efficient method to solve this problem. In this work, Kinetic Gas Molecule Optimization algorithm with Pareto optimality is proposed for solving multi-objective optimal reactive power dispatch problem. The presentation of Kinetic Gas Molecule Optimization is improved by computing inertia weight and acceleration coefficients dynamically rather than a fixed value. Because of this reason, the searching capability of the particles in each iteration is improved. However, to improve the power system performance in optimal reactive power dispatch scenario, additional flexible AC transmission system devices like static VAR compensator, thyristor-controlled series compensator, and unified power flow controller are introduced to provide stable results when compared to conventional output because flexible AC transmission system devices are capable of controlling the flow of real power and reactive power. These details are implemented and tested on IEEE 30-bus test system with various objectives. The performance of proposed method is validated from MATLAB, which shows the value of power loss as 4.3583 and voltage deviation as 0.26499 with cost of US$469.6417 per MVAR, which shows considerably superior results when compared with implemented particle swarm optimization results. The proposed method provides an efficient result for solving multi-objective optimal reactive power dispatch issues.

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