Research on transformer condition monitoring based on short time Fourier transform (STFT)

Yuchen Zhao; Qian Wang; Lei Shi; Haorui Liu

doi:10.1117/12.2637353

15 June 2022 Research on transformer condition monitoring based on short time Fourier transform (STFT)

Yuchen Zhao, Qian Wang, Lei Shi, Haorui Liu

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122851D (2022) https://doi.org/10.1117/12.2637353
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

Based on the development and practical application of transformer maintenance technology, this paper solves the core technical problems in on-line monitoring and fault diagnosis of transformer operating conditions. Though the integrated sensor of sound, vibration and temperature, the working conditions and operating state parameters such as sound, vibration and temperature of the transformer are collected. By using network communication to transmit data to the cloud platform for storage, multi-information fusion artificial intelligence modeling is performed on the collected operating conditions and operating status data to obtain an anomaly detection model. An anomaly detection model is deployed on the cloud platform to perform real-time detection on the data collected online, and if the system detects an abnormal state, an abnormality warning is performed. The short-time Fourier transform (STFT) and deep neural network are used to establish a relationship model between monitoring data and transformer faults, and the model is used to realize fault diagnosis.

Citation Download Citation

Yuchen Zhao, Qian Wang, Lei Shi, and Haorui Liu "Research on transformer condition monitoring based on short time Fourier transform (STFT)", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122851D (15 June 2022); https://doi.org/10.1117/12.2637353

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