Affiliation:
1. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
2. State Grid Beijing Electric Power Company, Beijing 100031, China
Abstract
The power transformer is one of the most critical core devices for energy exchange in power systems, and its safe and stable operation is directly related to the reliability of the power grid. Partial discharge is the main cause of insulation degradation and failure of high-voltage electrical equipment. Online monitoring and accurate localization of partial discharge can provide information on the aging of power equipment, which is of great value for improving the safe operation and maintenance of the power grid. Internal dual partial discharge in transformer windings is a more complex type of fault. Since it is located inside the windings, the signal is attenuated and distorted, making it difficult for traditional monitoring methods to capture such partial discharge signal The Fabry–Perot optical fiber sensor is an ultrasonic detection method that can be built into the transformer interior. This sensor has high sensitivity and a small size, enabling flexible placement at different locations inside the transformer for precise partial discharge detection. Especially for the narrow space inside the high and low voltage windings, F–P sensors can form an array, utilizing the array’s directivity to locate the fault points. In this study, an ultrasonic detection system based on the F–P optical fiber sensor array was developed. The system utilizes a directional cross-localization algorithm based on the multiple signal classification (MUSIC) algorithm to accurately locate dual partial discharge sources. This partial discharge detection system was applied to a 35 kV single-phase transformer, enabling the localization of dual partial discharge sources within the high and low voltage windings. Combined with experimental results, this method exhibits high localization accuracy and is particularly suitable for detecting partial discharge phenomena that occur within or between transformer windings.
Funder
National Natural Science Foundation of China
Reference23 articles.
1. Identification and localization of PD-sources in power-transformers and power-generators;Fuhr;IEEE Trans. Dielectr. Electr. Insul.,2017
2. Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review;Lu;IEEE Trans. Dielectr. Electr. Insul. Publ. IEEE Dielectr. Electr. Insul. Soc.,2020
3. Shang, H., Li, Y., Xu, J., Qi, B., and Yin, J. (2020). A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy. Entropy, 22.
4. Interaction Between Partial Discharge and Generated Bubbles Under Repeated Lightning Impulses in Transformers Using a Complex Structure Model;Lu;IEEE Trans. Dielectr. Electr. Insul.,2021
5. Multispectral Optical Partial Discharge Detection, Recognition, and Assessment;Xia;IEEE Trans. Instrum. Meas.,2022