短路
变压器
配电变压器
旋转式可变差动变压器
工程类
电气工程
节能变压器
断层(地质)
电子工程
漏感
电压
地震学
地质学
作者
Richang Xian,Lele Wang,Bingqian Zhang,Jiayang Li,Riming Xian,Jiaqi Li
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-07-14
卷期号:20 (2): 2444-2454
被引量:9
标识
DOI:10.1109/tii.2023.3292972
摘要
Interturn short circuit fault of distribution transformer winding occurs frequently and is difficult to accurately real-time monitoring, which seriously affects the reliability of the distribution network power supply. Therefore, the identification method of interturn short circuit faults for distribution transformers based on power loss variation is proposed to realize the online monitoring of winding interturn short circuit faults and early warning of insulation deterioration. First, the “field-circuit” coupling three-dimensional simulation model is established as consistent with the actual transformer. On the premise of verifying the reliability and accuracy of the model, the variation characteristics of each physical quantity are simulated and analyzed when a single-turn short circuit occurs in different positions in the secondary side inner and outer winding of the distribution transformer. The winding power loss with the most significant change rate and easy to detect is obtained as the sensitive state variable. Then, by changing the winding interturn insulation resistance and the number of short-circuit turns, the gradual altering process of interturn insulation deterioration is simulated, and the variation law of winding current and power loss in the main circuit is explored. Moreover, the relationship between the interturn insulation state and influence factors including the critical resistance value of interturn insulation deterioration and insulation collapse, the power loss rate of change is analyzed. Finally, a method of interturn short circuit's fault identification of distribution transformer is proposed, which can diagnose the interturn insulation state of transformer winding in real-time and predict the interturn short-circuit fault.
科研通智能强力驱动
Strongly Powered by AbleSci AI