Transformer Partial Discharge Monitoring Technology Based on Internal Sensors and EEMD-WD

• Published in: 2025 2nd International Conference on Electrical Technology and Automation Engineering (ETAE) pp. 269 - 273
• Main Authors: Jiang, Changming, Tang, Yunpeng, Cao, Xu, Wu, Shengli, Cao, Cheng, Zhang, Zhiyong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.05.2025
• Subjects: Accuracy; Filters; Internal PD Sensor; Magnetic sensors; Monitoring; Monitoring Technology; Partial Discharge; Partial discharges; Power system stability; Reliability Analysis; Signal to noise ratio; Transformers; Ultra-High Voltage Transformer; Voltage transformers; White noise
• DOI: 10.1109/ETAE65337.2025.11089657

Ultra-high voltage AC transformers are critical components in power systems, and their stability is essential for the safe operation of the grid. Partial discharge (PD) monitoring plays a crucial role in transformer maintenance and health management. However, existing technologies face limitations in ultra-high voltage AC transformers with high voltage, strong electric fields, and complex structures, making it difficult to achieve fast, accurate, and reliable monitoring. To address this challenge, this paper proposes a transformer partial discharge monitoring technology based on internal sensors and the Ensemble Empirical Mode Decomposition and Wavelet Denoising (EEMD-WD) anti-interference algorithm. By developing an internal PD sensor with a rod structure, the interference from external environmental noise is effectively avoided, enhancing monitoring accuracy. Additionally, the EEMD-WD algorithm efficiently filters out white noise in PD signals while retaining valid information, thereby improving the signal-to-noise ratio (SNR) and reliability of the monitoring. Simulation and experimental results validate the effectiveness of this technology, demonstrating that it has no impact on the internal electric and magnetic field environment of the transformer and can effectively filter noise from PD signals. This provides a new, effective method for transformer maintenance and health management.