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Adaptive Neuro-fuzzy Algorithm for Pitch Control of Variable-speed Wind Turbine

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  • Intelligent Control and Applications
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Abstract

With increasing size of wind turbines (WTs), the power regulation and fatigue loads on WT structures emerge as major problems to wind power industry. Pitch angle is scheduled above the rated wind speed to keep the power captured by variable-speed wind turbine (VSWT) around its rated value and release the fatigue load on WT structure. In this paper, a hybrid intelligent learning based adaptive neuro-fuzzy algorithm is proposed to schedule the pitch angle of 2 Megawatt (MW) VSWT. The artificial neural network (ANN) trains the parameters of fuzzy membership functions (MFs) using least squares estimator method in forward pass and back propagation gradient descent method in backward pass. The simulation is done in MATLAB and results are compared with multilayer perceptron feed-forward neural network (MLPFFNN) and fuzzy logic-based pitch controllers. The results indicate the effectiveness of proposed neuro-fuzzy algorithm which outperforms the other two methods.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Aamer Bilal Asghar

  2. Khazina Naveed is with the Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Khazina Naveed

  3. State Key Laboratory for Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Gang Xiong

  4. Cloud Computing Center, Chinese Academy of Sciences, Beijing, China

    Gang Xiong

  5. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yong Wang

Authors
  1. Aamer Bilal Asghar
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  2. Khazina Naveed
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  3. Gang Xiong
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  4. Yong Wang
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Correspondence to Yong Wang.

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This work was supported, in part, by the National Natural Science Foundation of China under Grants 61872365, 61773381, U1909218, U1909204, U19B2029 and Chinese Guangdong’s S&T project (2019B1515120030, 2020B0909050001).

Aamer Bilal Asghar received his Ph.D. degree in control theory and control engineering from the School of Control Science and Engineering, Dalian University of Technology, Dalian, China in 2018, an M.S. degree in electrical engineering from Government College University Lahore, Pakistan in 2014, and a B.S. degree in electronic engineering from The Islamia University of Bahawalpur, Pakistan in 2008. He has 13 years of experience in Industry and Academia. Currently, he is serving as an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at COMSATS University Islamabad (CUI), Lahore campus, Pakistan. He is the HEC approved Ph.D. supervisor and the in-charge of Instrumentation and Measurement LAB at ECE dept. CUI, Lahore campus. He is the author and co-author of several SCI Impact Factor (IF) International Journal papers. His research interest areas include artificial intelligence, intelligent control, fuzzy logic control, artificial neural networks, ANFIS, genetic algorithm machine learning, and renewable energy technologies.

Khazina Naveed is doing an M.S. degree in computer science from COMSATS University Islamabad, Lahore campus, Pakistan. She received her B.S. degree in bioinformatics from the Department of Biosciences, COMSATS University Islamabad, Sahiwal, Pakistan, in 2019. Her research interest areas include artificial intelligence, machine learning, data mining, computer programming, and bioinformatics.

Gang Xiong received his B.Eng. and M.Eng. degrees from the Department of Precision Instrument, Xi’an University of Science and Technology, Xi’an, China, in 1991 and 1994, respectively, and a Ph.D. degree in control science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 1996. From 1996 to 1998, he was a Postdoctoral and Associate Scientist with the Institute of Industrial Process Control, Zhejiang University. He is the Deputy Director of Beijing Engineering Research Center for Intelligent Systems and Technology, and Deputy Director of Cloud Computing Center, Chinese Academy of Sciences (CAS). His research interests include parallel control and management, modeling and optimization of complex systems, cloud computing and big data, intelligent manufacturing, and intelligent transportation systems.

Yong Wang received his B.S. degree from the Department of Mathematics, Shandong University, Jinan, China, in 1998, an M.Eng. degree from the Department of Computer Science and Engineering, China University of Petroleum, Beijing, China, in 2004, and a Ph.D. degree in pattern recognition & intelligent systems from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2008. From 2008 to 2010, he was a Post doctor in the College of Artificial Intelligence, University of Chinese Academy of Sciences. In 2010, he started his present position as a Research Scientist in the College of Artificial Intelligence, University of Chinese Academy of Sciences. He is the author and co-author of over 30 refereed journal and conference proceeding papers. His research interests include pattern recognition and data mining, and modeling and optimization of complex systems.

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Asghar, A.B., Naveed, K., Xiong, G. et al. Adaptive Neuro-fuzzy Algorithm for Pitch Control of Variable-speed Wind Turbine. Int. J. Control Autom. Syst. 20, 3788–3798 (2022). https://doi.org/10.1007/s12555-021-0675-y

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  • DOI: https://doi.org/10.1007/s12555-021-0675-y

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