Fan J.,University of South China |
Fan J.,Beijing Huadian Yuntong Power Technical Co. |
Cao J.,University of South China |
Cao J.,Beijing Huadian Yuntong Power Technical Co. |
Ding J.,University of South China
Dianli Zidonghua Shebei/Electric Power Automation Equipment | Year: 2010
The status of transformer windings should be monitored in real-time to detect their potential faults. The mathematical model of transformer windings is established and its short-circuit impedance is on-line computed with the voltage and current signals at both sides. The signals mentioned above are sampled in real-time and denoised by the wavelet transformation according to the characteristics of voltage and current sensors. By precisely scaling the phase differences based on discreet Fourier transform, the short-circuit impedances of the transformer windings are identified under various load conditions. Experiments are carried out on the windings monitoring platform built with model transformer and the results show that, the difference between short-circuit impedance measurements under various load conditions does not surpass 0.64% when there is no winding status change, whereas if there is interturn short-circuit or winding deformation, change of short-circuit impedance reaches up to 5.6%, proving the effectiveness of offered on-line monitoring algorithm.
Cao J.,University of South China |
Fan J.-M.,University of South China |
An C.-G.,Beijing Huadian Yuntong Power Technical Co.
Dianwang Jishu/Power System Technology | Year: 2010
To remedy the defects in conventional chromatographic peak identification algorithms, the grey correlation analysis is applied in chromatographic peak identification of transmission oil. According to the plate theory in gas chromatography, firstly based on plenty of chromatographic experimental data a Gaussian data sequence, which matches with the experimental data, is fitted; then the Gaussian data sequence is slide along the chromatographic data and during the sliding the grey B-type correlation degree between Gaussian window and corresponding chromatographic data is calculated, in the data segment where the correlation coefficient is greater than the predetermined threshold value the chromatographic peak exists and the position of the chromatographic peak in this data segment corresponds to the peak position of Gaussian window that moves to the place. Experimental results show that the proposed algorithm can identify chromatographic peaks accurately and is insensitive to both noise and change of the width of chromatographic peak, in addition, it is also independent of retention time of chromatographic peak, so it possesses excellent noise immunity and adaptability.
Yu W.,China Southern Power Grid Co. |
Wang Z.,Changsha University |
Wang Z.,Guangxi Power Grid Corporation Chongzuo Power Supply Bureau |
Zeng X.,Changsha University |
Liu C.,Beijing Huadian Yuntong Power Technical Co.
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | Year: 2015
Large power transformer is the key equipment of power transmission systems, and its reliability is directly related to the safety and stability of power grids operation. In order to improve the efficiency of the transformer reliability analysis, a reliability tracing analysis method for multi-state power transformers using Bayesian Network is proposed. The unreliability tracking algorithm for multi-state transformer is proposed. According to the relevant references, expert experience and data collection, the components based power transformer Bayesian network model is built. Combined with the reliability index statistics, the key components impacting reliability and the weak parts of transformer are recognized by the proposed technique. Through the use of Bayesian network to track the transformer reliability analysis, the data to support the condition based maintenance and all-life management for the transformer is provided. ©, 2015, Power System Protection and Control Press. All right reserved.
Ou L.,Central South University |
Cao J.,Central South University |
Cao J.,Beijing Huadian Yuntong Power Technical Co.
Chinese Journal of Chromatography (Se Pu) | Year: 2014
In the field of the chromatographic peak identification of the transformer oil, the traditional first-order derivative requires slope threshold to achieve peak identification. In terms of its shortcomings of low automation and easy distortion, the first-order derivative method was improved by applying the moving average iterative method and the normalized analysis techniques to identify the peaks. Accurate identification of the chromatographic peaks was realized through using multiple iterations of the moving average of signal curves and square wave curves to determine the optimal value of the normalized peak identification parameters, combined with the absolute peak retention times and peak window. The experimental results show that this algorithm can accurately identify the peaks and is not sensitive to the noise, the chromatographic peak width or the peak shape changes. It has strong adaptability to meet the on-site requirements of online monitoring devices of dissolved gases in transformer oil.