Version 1
: Received: 16 January 2024 / Approved: 16 January 2024 / Online: 16 January 2024 (17:01:13 CET)
How to cite:
Chen, F.; Shieh, H. An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks. Preprints2024, 2024011240. https://doi.org/10.20944/preprints202401.1240.v1
Chen, F.; Shieh, H. An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks. Preprints 2024, 2024011240. https://doi.org/10.20944/preprints202401.1240.v1
Chen, F.; Shieh, H. An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks. Preprints2024, 2024011240. https://doi.org/10.20944/preprints202401.1240.v1
APA Style
Chen, F., & Shieh, H. (2024). An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints202401.1240.v1
Chicago/Turabian Style
Chen, F. and Horng-Lin Shieh. 2024 "An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks" Preprints. https://doi.org/10.20944/preprints202401.1240.v1
Abstract
The operation of transformers suffers from natural or man-made disasters, subjecting the equipment to prolonged exposure to temperature, lightning strikes, transient over-voltages, and over-current, gradually producing insulation aging phenomenon, which results in partial discharges within the transformer. Without dealing with it beforehand properly, it may lead to insulation breakdown. Nowadays, with the mature development of microchip control technology and wireless communication technology, which is helpful for realizing the research for online real-time and long-term monitoring capabilities for power equipment. A transformer operating condition monitoring and diagnostic system that can rapidly and effectively prevent the breakdown of transformer insulation, which can impact the overall power supply system is developed in this paper. The system is divided into two parts based on its function. The first part is the operating condition monitoring and real-time alert unit for the transformer, primarily composed of an embedded computing module, a sensor module, and a transmission module. Its work content is to capture real-time online partial discharge ultrasonic signals and high frequency current comparator pulse signals, and analyze and determine the condition of the internal insulation and operational status whether it is fault or not. The second part is the central control unit, consisting mainly of an industrial computer and a transmission module. Its work content is to store the ultrasonic and high frequency current comparator pulse signals from the transformer operation monitoring and real-time alert unit. It uses the software to eliminate signal noise and interference, and then utilizes artificial neural networks to identify the type of fault. In this paper, the research for the phenomenon of insulation degradation in transformer equipment aims to establish aging trend profiles through the dual malfunctioning alert and aging process analysis, as well as the long-term measurement of ultrasonic signals from partial discharges and high-frequency current comparator pulse signals. This approach provides crucial reference data and a foundation for disposable on-site measurement, assessing the phenomenon of insulation aging stages, defects and damage of the transformers effectively. It allows for timely replacement or maintenance in advance, achieving the prevention of malfunctioning and predictive diagnosis of aging.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
