Realization of a Power Transformer on-line Monitoring and Diagnosis System Based on DGA and PNN
A novel on-line monitoring and fault diagnosis system of power transformer using basic principle of three ratio method based on DGA (Dissolved Gases Analysis) and PNN(Probabilistic Neural Networks) for engineering application is proposed. According to tested dissolved gases contents changing characteristic of transformer oils under different transformer working conditions, the five characteristics of gases (C2H2 C2H4,CH4,H2,C2H6) in transformer insulating oil will be as the objects of monitoring, through the continuous detection of these gases,the three pairs of gases content ratio in these key/characteristic gases are got as one of extracted features. Then, the extracted feature parameters are used as inputs to classifiers based on a Probabilistic Neural Networks model for six-class fault recognition. The system realization platform is based on PXI and cRIO in Virtual Instruments technology. The software adopts Labview and the hardware platform is PXI for development and management and cRIO for working. The simulation results show that the system can on-line provide and display the changing process of dissolved gases, the corresponding diagnosis and discover the hidden faults timely during operation. It proposes a new way for power transformer on-line monitoring and fault diagnosis.
transformer monitoring DGA PNN Vitual Instruments
Qun-Feng Niu Li Wang Ya-Ping Shi
School of Electrical Engineering Henan University of Technology Zhengzhou, China
国际会议
长春
英文
210-213
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）