Application of Improved Ant Colony Optimization on Economical Operation of Automatic Generation Control Units in Hydropower Station

2013 ◽  
Vol 860-863 ◽  
pp. 2101-2106 ◽  
Author(s):  
Yi Fan Li ◽  
Ke Guan Wang ◽  
Chuan Li Gong
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Automatic Generation ◽  
Ant Colony ◽  
Automatic Generation Control ◽  
Hydropower Station ◽  
Control Units ◽  
Convergence Performance ◽  
Generation Control

This paper proposed an improved ant colony optimization(ACO), to solve the economical operating dispatch of automatic generation control(AGC) units in hydropower station. The improved ant colony algorithm PSO-ACO imported particle swarm optimization is put forward. Both of the global convergence performance and the effectiveness of this algorithm is improved by using self-adaptive parameters and importing PSO to optimize the current ant paths. The mathematical description and procedure of the PSO-ACO are given with the maximum plant generating efficiency model as an example. Finally the superiority of the PSO-ACO is demonstrated by the application of AGC units on right bank of Three Gorges hydropower station. The optimal solution is more accurate and the calculation speed is higher than other methods.

Automatic Generation Control of Hydro-Hydro Interconnected Power System Based on Ant Colony Optimization

2017 ◽  
pp. 153-170 ◽  
Author(s):  
Jagatheesan Kaliannan ◽  
Anand B ◽  
Nguyen Gia Nhu ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Pid Controller ◽  
Absolute Error ◽  
Automatic Generation ◽  
Ant Colony ◽  
Automatic Generation Control ◽  
Interconnected Power System ◽  
Integral Time ◽  
Generation Control ◽  
Sudden Load

Each hydropower system incorporates with appropriate hydro turbine, and hydro governor unit. In the current work, an Automatic Generation Control (AGC) of two equal hydropower systems with Proportional-Integral-Derivative (PID) controller was investigated. The gain values of the PID controllers were tuned using Ant Colony Optimization (ACO) technique with one percent Step Load Perturbation (1% SLP) in area 1. The Integral Square Error (ISE), Integral Time Square Error (ITSE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE) were chosen as the objective function in order to optimize the controller's gain values. The experimental results reported that the IAE based PID controller improved the system performance compared to other objective functions during sudden load disturbance.

Application of Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1217-1220
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pheromone Concentration ◽  
Local Optimal Solution ◽  
Searching Ability ◽  
Excessive Accumulation

This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

scholarly journals Automatic Generation Control using Genetic Algorithm Based PID Controller

2020 ◽  
Vol 9 (5) ◽  
pp. 1047-1053
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Thermal Power ◽  
Optimal Solution ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Parameter Uncertainties ◽  
Survival Of The Fittest ◽  
Generation Control ◽  
Plant Parameter

In this paper Automatic Generation Control (AGC) of a single-area thermal power plant without reheat turbine is introduced using a Proportional Integral Derivative (PID) controller. The gains of the controller are optimized using Genetic Algorithm (GA). The problem of tuning the PID controller is formulated as optimization problem with constraints on proportional, derivative and integral gains. The proposed algorithm uses Darwin’s law of natural selection and survival of the fittest to reach the optimal solution. The simulation results confirm the system’s ability to retain frequency while handling sudden load disturbances. The second part of the investigation includes robustness testing of the system against plant parameter variations. The results are verified and the system performance is found to be robust against parameter uncertainties

scholarly journals Tuning PID Controller Using Multiobjective Ant Colony Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ibtissem Chiha ◽  
Noureddine Liouane ◽  
Pierre Borne
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pid Controllers ◽  
Tuning Method ◽  
Classic Approach ◽  
Optimum Solution ◽  
Multiobjective Ant Colony Optimization

This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp,Ki, andKd) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

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