Stability enhancement of multi-machine power systems using Ant colony optimization-based static Synchronous Compensator

2020 ◽  
Vol 83 ◽  
pp. 106589 ◽  
Author(s):  
Rajeev Kumar ◽  
Rajveer Singh ◽  
Haroon Ashfaq
Keyword(s):  
Power Systems ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Static Synchronous Compensator ◽  
Stability Enhancement

An ant colony optimization algorithm-based emergency control strategy for voltage collapse mitigation

2012 ◽  
Vol 3 (2) ◽  
pp. 147-156 ◽  
Author(s):  
R. A. El-Sehiemy ◽  
A. A. A. El Ela ◽  
A. M. M. Kinawy ◽  
M. T. Mouwafia
Keyword(s):  
Power Systems ◽  
Ant Colony Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Ant Colony ◽  
Voltage Collapse ◽  
Ant Colony Optimization Algorithm ◽  
Preventive Control ◽  
Control Actions

Abstract This paper presents optimal preventive control actions using ant colony optimization (ACO) algorithm to mitigate the occurrence of voltage collapse in stressed power systems. The proposed objective functions are: minimizing the transmission line losses as optimal reactive power dispatch (ORPD) problem, maximizing the preventive control actions by minimizing the voltage deviation of load buses with respect to the specified bus voltages and minimizing the reactive power generation at generation buses based on control variables under voltage collapse, control and dependent variable constraints using proposed sensitivity parameters of reactive power that dependent on a modification of Fast Decoupled Power Flow (FDPF) model. The proposed preventive actions are checked for different emergency conditions while all system constraints are kept within their permissible limits. The ACO algorithm has been applied to IEEE standard 30-bus test system. The results show the capability of the proposed ACO algorithm for preparing the maximal preventive control actions to remove different emergency effects.

Ant Colony Optimization Algorithm for Electrical Power Systems Applications

2022 ◽  
pp. 37-59
Author(s):  
Ragab A. El-Sehiemy ◽  
Almoataz Y. Abdelaziz
Keyword(s):  
Power Systems ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Electrical Power ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Electrical Power System ◽  
Electrical Power Systems ◽  
Recent Developments

Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Recently, meta-heuristic global optimization algorithms have become a popular choice for solving complex and intricate problems, which are otherwise difficult to solve by traditional methods. This chapter reviews the recent applications of ant colony optimization (ACO) algorithm in the field of electrical power systems. Also, the progress of the ACO algorithm and its recent developments are discussed. This chapter covers the aspects like (1) basics of ACO algorithm, (2) progress of ACO algorithm, (3) classification of electrical power system applications, and (4) future of ACO for modern power systems application.

Optimal design of multi-machine power system stabilizer using robust ant colony optimization technique

2011 ◽  
Vol 34 (7) ◽  
pp. 829-840 ◽  
Author(s):  
M Mary Linda ◽  
N Kesavan Nair
Keyword(s):  
Power Systems ◽  
Power System ◽  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Operating Conditions ◽  
Ant Colony ◽  
Fine Tuning ◽  
Power System Stabilizer ◽  
System Stabilizer ◽  
Genetic Local Search

In this paper, a multi-objective design of the multi-machine Power System Stabilizer (PSS) using Ant Colony Optimization (ACO) is proposed. The fine tuning of PSS parameters problem is converted to an optimization problem that is resolved by an ACO-based dominant metaheuristic technique. The strength of the proposed ACO-based PSS is tested on two different multi-machine power systems under diverse operating conditions. The outcomes of the proposed ACOPSS are compared with the Conventional PSS, Genetic Local Search-based PSS, Chaotic Optimization-based PSS and Particle Swarm Optimization-based PSS (PSOPSS). From the simulation results it can be inferred that the ACOPSS reduces the settling time and maximum overshoot more than the other techniques.

scholarly journals Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 738
Author(s):  
Vijo M Joy ◽  
S. Krishnakumar
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Peak Load ◽  
Back Propagation ◽  
Minimum Cost ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Load Scheduling

For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.

Automatic Generation Control of Thermal-Thermal-Hydro Power Systems with PID Controller Using Ant Colony Optimization

2017 ◽  
pp. 761-778 ◽  
Author(s):  
Jagatheesan Kaliannan ◽  
Anand Baskaran ◽  
Nilanjan Dey
Keyword(s):  
Power Systems ◽  
Power System ◽  
Objective Function ◽  
Ant Colony Optimization ◽  
Pid Controller ◽  
Pi Controller ◽  
Ant Colony ◽  
Hydro Power ◽  
Proportional Integral ◽  
Hydro Power Plants

In this work, Artificial Intelligence (AI) based Ant Colony Optimization (ACO) algorithm is proposed for Load Frequency Control (LFC) of interconnected multi–area hydrothermal power systems. Area 1&2 are thermal power systems and area 3 is a hydro power system, all the areas are interconnected through the appropriate tie-line. Thermal and hydro power plants are applied with reheat turbine and electric governor respectively. Investigated power system initially applied with conventional Proportional-Integral (PI) controller and controller parameters are optimized by using trial and error method considering Integral Time Absolute Error (ITAE) objective function. After that, the system is equipped with Proportional – Integral – Derivative (PID) controller and controller parameters are optimized by using ACO algorithm with ITAE objective function. The superiority of the proposed algorithm has been demonstrated by comparing conventional controller. Finally, The Simulation results of multi-area power system prove the effectiveness of the proposed optimization technique in LFC scheme and show its superiority over conventional PI controller.

Automatic Generation Control of Thermal-Thermal-Hydro Power Systems with PID Controller using Ant Colony Optimization

2015 ◽  
Vol 6 (2) ◽  
pp. 18-34 ◽  
Author(s):  
Jagatheesan Kaliannan ◽  
Anand Baskaran ◽  
Nilanjan Dey
Keyword(s):  
Power Systems ◽  
Power System ◽  
Objective Function ◽  
Ant Colony Optimization ◽  
Pid Controller ◽  
Pi Controller ◽  
Load Frequency Control ◽  
Ant Colony ◽  
Hydro Power ◽  
Proportional Integral

In this work, Artificial Intelligence (AI) based Ant Colony Optimization (ACO) algorithm is proposed for Load Frequency Control (LFC) of interconnected multi–area hydrothermal power systems. Area 1&2 are thermal power systems and area 3 is a hydro power system, all the areas are interconnected through the appropriate tie-line. Thermal and hydro power plants are applied with reheat turbine and electric governor respectively. Investigated power system initially applied with conventional Proportional-Integral (PI) controller and controller parameters are optimized by using trial and error method considering Integral Time Absolute Error (ITAE) objective function. After that, the system is equipped with Proportional – Integral – Derivative (PID) controller and controller parameters are optimized by using ACO algorithm with ITAE objective function. The superiority of the proposed algorithm has been demonstrated by comparing conventional controller. Finally, The Simulation results of multi-area power system prove the effectiveness of the proposed optimization technique in LFC scheme and show its superiority over conventional PI controller.

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