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Design of the PID Controller for Hydro-turbines Based on Optimization Algorithms

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  • Control Theory and Applications
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Abstract

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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Authors and Affiliations

  1. The Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, No.70, Lienhai Road, Kaohsiung, 80424, Taiwan

    Jau-Woei Perng, Yi-Chang Kuo & Kuan-Chung Lu

Authors
  1. Jau-Woei Perng
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  2. Yi-Chang Kuo
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  3. Kuan-Chung Lu
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Correspondence to Yi-Chang Kuo.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Editor Kyoung Kwan Ahn. The authors appreciate Director Yao-Tsung Chang and Deputy Director Hsi-Hsiang Chang of the contributions provided from Taiwan Power Company Southern Region Construction Office. Funding: This study was funded by a grant from Ministry of Science and Technology, Taiwan, under Grant no. MOST 108-2218-E-110-005.

Jau-Woei Perng was born in Hsinchu of Taiwan in 1973. He received his B.S. and M.S. degrees in electrical engineering from Yuan Ze University located in Chungli of Taiwan in 1995 and 1997, respectively, and a Ph.D.degree in electrical and control engineering from National Chiao Tung University (NCTU) located in Hsinchu of Taiwan in 2003. From 2004 to 2008, he was a Research Assistant Professor at the Department of Electrical and Control Engineering, NCTU. Since 2008, he has worked for the Department of Mechanical and Electromechanical Engineering at National Sun Yat-Sen University in Kaohsiung of Taiwan, where he is currently a Professor. His research mainly concerns robust control, nonlinear control, fuzzy logic control, neural networks, mobile robots, systems engineering and intelligent vehicle control.

Yi-Chang Kuo was born in Kaohsiung of Taiwan in 1976. He received his B.S. and M.S. degrees in biomedical engineering respectively in 2001 and 2003 from Chung Yuan Christian University located in Zhongli of Taiwan. From 2004 to 2008, he was a computer information maintenance engineer. Since 2008, he has worked for the TaiPower Company in Taiwan, where he is currently an Electrical and Mechanical Design Specialist. In 2014, he also became a Ph.D. student, and he was selected as a doctoral candidate in 2016. His research mainly concerns fuzzy logic control, neural networks, optimal ground system, optimal electric grid, signal processing, optimal substation, optimization algorithms, power system.

Kuan-Chung Lu was born in Kaohsiung of Taiwan in 1991. He received his B.S. degrees in Systems and Naval Mechatronic Engineering in 2014 from National Cheng Kung University, and an M.S. degrees in Mechanical and Electro-mechanical Engineering in 2016 from National Sun Yat-Sen University. His research focus on MOPSO, fuzzy logic control and motor control.

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Perng, JW., Kuo, YC. & Lu, KC. Design of the PID Controller for Hydro-turbines Based on Optimization Algorithms. Int. J. Control Autom. Syst. 18, 1758–1770 (2020). https://doi.org/10.1007/s12555-019-0254-7

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  • DOI: https://doi.org/10.1007/s12555-019-0254-7

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