Design of the PID Controller for Hydro-turbines Based on Optimization Algorithms
In this study, multiple objective particle swarm optimization (MOPSO), genetic algorithm, bees, and reinforcement learning (RL) are used to calculate the rise time (tr), integral square-error, integral of time-multiplied-squared-error, integral absolute error, and integral of time multiplied by abso...
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Published in | International journal of control, automation, and systems Vol. 18; no. 7; pp. 1758 - 1770 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Bucheon / Seoul
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.07.2020
Springer Nature B.V 제어·로봇·시스템학회 |
Subjects | |
Online Access | Get full text |
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Summary: | In this study, multiple objective particle swarm optimization (MOPSO), genetic algorithm, bees, and reinforcement learning (RL) are used to calculate the rise time (tr), integral square-error, integral of time-multiplied-squared-error, integral absolute error, and integral of time multiplied by absolute error of the system transfer function and then we use a fuzzy algorithm on MOPSO, GA, bees, and RL based on the frequency sensitivity margin of a water turbine governor to optimize the proportional gain (kp) and integral gain (ki) and calculate the relative collapsing frequency response values. The MOPSO algorithm returned the optimal result. The radial basis function (RBF) neural network curve is obtained from the MOPSO algorithm with three variables (i.e., kp, ki, kd = 0.6 and grid frequency deviations values), and finally we identify and predict three variable values near the RBF neural network curve through deep learning. The result of the grid frequency deviation is close to 0, and the gain response time is better for damping the frequency oscillations in different operating conditions. |
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Bibliography: | http://link.springer.com/article/10.1007/s12555-019-0254-7 |
ISSN: | 1598-6446 2005-4092 |
DOI: | 10.1007/s12555-019-0254-7 |