Chong Li - IEEE Xplore Author Profile
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Chong Li

Publications
7
Citations
3
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (18)
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Chong Li

Affiliation

Xi’an Thermal Power Research Institute Company Ltd.
Xi’an
Shaanxi, China

Publication Topics

Biography

Chong Li was born in Henan, China, in 1989. He received the Ph.D. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, Shaanxi, China, in 2020.,He is currently with Xi’an Thermal Power Research Institute Company Ltd., Xi’an. His research interests include fault diagnosis and condition assessment of wind turbines.(Based on document published on 14 January 2025).
Publications
7
Citations
3
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (18)
Author's Published Works

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Diagnosis of Demagnetization Faults in PMSGs Based on Performance Evaluation of Machine Learning Algorithms Through Flux and Current Signals

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Volume: 74 | Journal Article |
This study proposed an effective machine learning (ML)-based fault diagnosis method for demagnetization faults, including “healthy, 30% unipolar demagnetization, 50% multimagnet demagnetization, 100% adjacent pole demagnetization, and 40% uniform demagnetization,” in a 25-kW permanent magnet synchronous generator (PMSG) that considers algorithm accuracy and signal processing technique efficiency, ...Show More

Diagnosis of Demagnetization Faults in PMSGs Based on Performance Evaluation of Machine Learning Algorithms Through Flux and Current Signals

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

Year: 2025 | Volume: 74 | Journal Article |

Demagnetization Modeling and Fault Diagnosis in a 25-kW PMSG Using Finite Element Method and Machine Learning Techniques

Nadeem Shahbaz;Yu Chen;Sichao Zhang;Feng Liang;Siyu Du;Shouwang Zhao;Yong Ma;Chong Li;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
In wind power power systems, the reliable functioning of Permanent Magnet Synchronous Generators (PMSG) depends on condition monitoring and fault diagnosis. Using 3D simulation models, we present a diagnostic approach for identifying multiple demagnetization problems in PMSG. Specifically, we use the four states of demagnetization: healthy condition, 50% unipolar magnet breakage, 75% demagnetizati...Show More

Demagnetization Modeling and Fault Diagnosis in a 25-kW PMSG Using Finite Element Method and Machine Learning Techniques

Nadeem Shahbaz;Yu Chen;Sichao Zhang;Feng Liang;Siyu Du;Shouwang Zhao;Yong Ma;Chong Li;Yong Zhao

Year: 2024 | Conference Paper |

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Generator for Wind Turbine Based on ResNet-50 Transfer Learning Model

Siyu Du;Yu Chen;Sichao Zhang;Feng Liang;Nadeem Shahbaz;Guoqiang Zhu;Shouwang Zhao;Shuang Wang;Yong Ma;Chong Li;Zhongjie Wang;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
This paper utilizes Convolutional Neural Networks (CNN) with Residual Networks (ResNet) to identify and classify demagnetization faults in images. Firstly, we collected current feature datasets corresponding to two types of permanent magnet wind turbines, namely 25 kW and 2 MW, representing the source and target domains. Using the source domain dataset, we trained a ResNet50 model specifically for...Show More

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Generator for Wind Turbine Based on ResNet-50 Transfer Learning Model

Siyu Du;Yu Chen;Sichao Zhang;Feng Liang;Nadeem Shahbaz;Guoqiang Zhu;Shouwang Zhao;Shuang Wang;Yong Ma;Chong Li;Zhongjie Wang;Yong Zhao

Year: 2024 | Conference Paper |

Demagnetization Fault Diagnosis Based on Feature Extraction and Stacking Ensemble Learning for Permanent Magnet Synchronous Generator

Sichao Zhang;Yu Chen;Feng Liang;Siyu Du;Nadeem Shahbaz;Shouwang Zhao;Chong Li;Wei Deng;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
During the operation of a permanent magnet wind turbine, magnet demagnetization failure may occur, which directly affects the regular operation of the wind turbine and adversely affects wind power generation. This paper proposes a demagnetization fault diagnosis method for permanent magnet generators based on feature extraction and stacking integrated learning. A permanent magnet generator with a ...Show More

Demagnetization Fault Diagnosis Based on Feature Extraction and Stacking Ensemble Learning for Permanent Magnet Synchronous Generator

Sichao Zhang;Yu Chen;Feng Liang;Siyu Du;Nadeem Shahbaz;Shouwang Zhao;Chong Li;Wei Deng;Yong Zhao

Year: 2024 | Conference Paper |

Classification of Demagnetization Faults based on Performance Evaluation of Machine Learning Algorithms via Flux and Vibration Signals in Permanent Magnet Wind Turbine Generator

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Fault diagnosis and condition monitoring play a significant role in wind turbines as they guarantee safety and reliability and avoid perilous conditions; therefore, fault diagnosis prior to its existence saves both time and costs. This paper proposes a machine learning-based fault diagnosis technique using vibration and leakage flux for multiple demagnetization faults, including “healthy, 30% unip...Show More

Classification of Demagnetization Faults based on Performance Evaluation of Machine Learning Algorithms via Flux and Vibration Signals in Permanent Magnet Wind Turbine Generator

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

Year: 2023 | Conference Paper |

Demagnetization Fault Diagnosis Based on Feature Extraction and Convolutional Neural Network for Permanent Magnet Generator

Sichao Zhang;Yu Chen;Feng Liang;Nadeem Shahbaz;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2023 | Conference Paper |
During the operation of permanent magnet wind turbines, magnetic steel demagnetization faults may occur, directly affecting the normal operation of wind turbines and having adverse effects on wind power generation. This article proposes a demagnetization fault diagnosis method for permanent magnet generators based on feature extraction and convolutional neural network. A permanent magnet generator...Show More

Demagnetization Fault Diagnosis Based on Feature Extraction and Convolutional Neural Network for Permanent Magnet Generator

Sichao Zhang;Yu Chen;Feng Liang;Nadeem Shahbaz;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

Year: 2023 | Conference Paper |

Finite Element Simulation on Irreversible Demagnetization of Permanent Magnet Synchronous Generator

Yonggang Jia;Yan Du;Yang Wang;Bo Zhang;Wen Cao;Yuanyang Ren;Chong Li

2022 9th International Conference on Condition Monitoring and Diagnosis (CMD)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Irreversible demagnetization of permanent magnet wind turbines is one of the main causes of motor failures. Due to the difficulty in representing irreversible demagnetization faults of wind turbines, online monitoring and fault diagnosis techniques for permanent magnet wind turbines are severely limited. In this paper, Maxwell software is used to build a Permanent magnet generator(PMG) model to si...Show More

Finite Element Simulation on Irreversible Demagnetization of Permanent Magnet Synchronous Generator

Yonggang Jia;Yan Du;Yang Wang;Bo Zhang;Wen Cao;Yuanyang Ren;Chong Li

Year: 2022 | Conference Paper |

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