Optical Characteristics and Fault Diagnosis of Partial Discharge in C4F7N/CO2 Gas mixture and SF6 Based on Novel Multispectral Microarray Detection
Yiming Zang (Shanghai Jiao Tong University)
Yong Qian (Shanghai Jiao Tong University)
Xiaoli Zhou (Fudan University)
M. Ghaffarian Niasar (TU Delft - DC systems, Energy conversion & Storage)
Gehao Sheng (Shanghai Jiao Tong University)
Xiuchen Jiang (Shanghai Jiao Tong University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Optical partial discharge (PD) detection is an efficient means of diagnosing the insulation status of power equipment. C4F7N/CO2 gas mixture is a very potential environmentally-friendly SF6 substitute gas, and its PD optical characteristics need to be studied to guide the PD diagnosis of novel C4F7N/CO2 equipment. Therefore, this article proposes a multispectral microarray detection technology, which can achieve high-sensitivity detection and PD diagnosis by simultaneously collecting the spectral characteristics of multiple bands. By setting up an experimental platform, the PD experiments of four typical defects in the C4F7N/CO2 gas mixture with five different proportions and pure SF6 are carried out. Based on the analysis of PD multispectral features, the correlation between different gases and the difference between different defects are obtained. Finally, by combining multispectral detection with a t-distributed stochastic neighbor embedding (T-SNE) feature extraction algorithm, a PD diagnosis method that can adapt to both C4F7N/CO2 gas mixture and SF6 is proposed, which provides a reference for the PD detection of novel C4F7N/CO2 equipment application.