IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p7366-d935559.html
   My bibliography  Save this article

Junction Temperature Prediction of Insulated-Gate Bipolar Transistors in Wind Power Systems Based on an Improved Honey Badger Algorithm

Author

Listed:
  • Chao Zhou

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300401, China)

  • Bing Gao

    (State Grid Hengshui Electric Power Supply Company, Hengshui 053000, China)

  • Haiyue Yang

    (State Grid Hengshui Electric Power Supply Company, Hengshui 053000, China)

  • Xudong Zhang

    (Department of Mechatronics and Mechanical Engineering, Bochum University of Applied Sciences, 44801 Bochum, Germany)

  • Jiaqi Liu

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300401, China)

  • Lingling Li

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China
    Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300401, China)

Abstract

To reduce carbon dioxide emissions, wind power generation is receiving more attention. The conversion of wind energy into electricity requires frequent use of insulated-gate bipolar transistors (IGBTs). Therefore, it is important to improve their reliability. This study proposed a method to predict the junction temperature of IGBTs, which helps to improve their reliability. Limited by the bad working environment, the physical temperature measurement method proposed by previous research is difficult to apply. Therefore, a junction temperature prediction method based on an extreme learning machine optimized by an improved honey badger algorithm was proposed in this study. First, the data of junction temperature were obtained by the electro-heat coupling model method. Then, the accuracy of the proposed method was verified with the data. The results show that the average absolute error of the proposed method is 0.0303 °C, which is 10.62%, 11.14%, 91.67%, and 95.54% lower than that of the extreme learning machine optimized by a honey badger algorithm, extreme learning machine optimized by a seagull optimization algorithm, extreme learning machine, and back propagation neural network model. Therefore, compared with other models, the proposed method in this paper has higher prediction accuracy.

Suggested Citation

  • Chao Zhou & Bing Gao & Haiyue Yang & Xudong Zhang & Jiaqi Liu & Lingling Li, 2022. "Junction Temperature Prediction of Insulated-Gate Bipolar Transistors in Wind Power Systems Based on an Improved Honey Badger Algorithm," Energies, MDPI, vol. 15(19), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7366-:d:935559
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/7366/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/19/7366/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohamed Lotfi & Mohammad Javadi & Gerardo J. Osório & Cláudio Monteiro & João P. S. Catalão, 2020. "A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation," Energies, MDPI, vol. 13(1), pages 1-19, January.
    2. Pan, Lin & Xiong, Yong & Zhu, Ze & Wang, Leichong, 2022. "Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor," Renewable Energy, Elsevier, vol. 184(C), pages 1002-1017.
    3. Liu, Zhi-Feng & Li, Ling-Ling & Liu, Yu-Wei & Liu, Jia-Qi & Li, Heng-Yi & Shen, Qiang, 2021. "Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach," Energy, Elsevier, vol. 235(C).
    4. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    5. Zilang Hu & Xinglai Ge & Dong Xie & Yichi Zhang & Bo Yao & Jian Dai & Fengbo Yang, 2019. "An Aging-Degree Evaluation Method for IGBT Bond Wire with Online Multivariate Monitoring," Energies, MDPI, vol. 12(20), pages 1-18, October.
    6. Fen Zhou & Xuping Tu & Qingdong Wang, 2022. "Research on offshore wind power system based on Internet of Things technology [A survey of artificial neural network in wind energy systems]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 645-650.
    7. Jiang, Yichen & Liu, Shijie & Zao, Peidong & Yu, Yanwei & Zou, Li & Liu, Liqin & Li, Jiawen, 2022. "Experimental evaluation of a tree-shaped quad-rotor wind turbine on power output controllability and survival shutdown capability," Applied Energy, Elsevier, vol. 309(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    2. Essam H. Houssein & Awny Sayed, 2023. "Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification," Mathematics, MDPI, vol. 11(3), pages 1-27, January.
    3. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
    4. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    5. Jian Zhao & Bochen Zhang & Xiwang Guo & Liang Qi & Zhiwu Li, 2022. "Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-31, November.
    6. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Abdul Hai Alami & Enas Taha Sayed, 2023. "Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    7. Fatmah Y. Assiri & Mahmoud Ragab, 2023. "Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment," Mathematics, MDPI, vol. 11(19), pages 1-16, September.
    8. Sheng, Wanxing & Li, Rui & Yan, Tao & Tseng, Ming-Lang & Lou, Jiale & Li, Lingling, 2023. "A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm," Renewable Energy, Elsevier, vol. 204(C), pages 493-506.
    9. Eslami, N. & Yazdani, S. & Mirzaei, M. & Hadavandi, E., 2022. "Aphid–Ant Mutualism: A novel nature-inspired​ metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 362-395.
    10. Hua Li & Zhen Wang & Binbin Shan & Lingling Li, 2022. "Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction," Energies, MDPI, vol. 15(22), pages 1-21, November.
    11. Mahamed G. H. Omran & Maurice Clerc & Fatme Ghaddar & Ahmad Aldabagh & Omar Tawfik, 2022. "Permutation Tests for Metaheuristic Algorithms," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    12. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    13. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    14. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    15. Muhammad Haris Khan & Abasin Ulasyar & Abraiz Khattak & Haris Sheh Zad & Mohammad Alsharef & Ahmad Aziz Alahmadi & Nasim Ullah, 2022. "Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm," Energies, MDPI, vol. 15(16), pages 1-18, August.
    16. Chenyang Gao & Teng Li & Yuelin Gao & Ziyu Zhang, 2024. "A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems," Mathematics, MDPI, vol. 12(3), pages 1-35, January.
    17. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    18. Yang, Xiaohui & Zhang, Zhonglian & Mei, Linghao & Wang, Xiaopeng & Deng, Yeheng & Wei, Shi & Liu, Xiaoping, 2023. "Optimal configuration of improved integrated energy system based on stepped carbon penalty response and improved power to gas," Energy, Elsevier, vol. 263(PD).
    19. Liu, Zhi-Feng & Zhao, Shi-Xiang & Zhang, Xi-Jia & Tang, Yu & You, Guo-Dong & Li, Ji-Xiang & Zhao, Shuang-Le & Hou, Xiao-Xin, 2023. "Renewable energy utilizing and fluctuation stabilizing using optimal dynamic grid connection factor strategy and artificial intelligence-based solution method," Renewable Energy, Elsevier, vol. 219(P1).
    20. Zhang, Zhonglian & Yang, Xiaohui & Yang, Li & Wang, Zhaojun & Huang, Zezhong & Wang, Xiaopeng & Mei, Linghao, 2023. "Optimal configuration of double carbon energy system considering climate change," Energy, Elsevier, vol. 283(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7366-:d:935559. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.