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| Optical Signal Detection Technology and Optical Image Diagnostic Method of Partial Discharge in GIS |
| Li Ze1,2, Qian Yong1, Liu Wei3, Zang Yiming1, Sheng Gehao1, Jiang Xiuchen1 |
1. Department of Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China;
2. State Grid Shanghai Electric Power Research Institute Shanghai 200437 China;
3. Key Laboratory for Sulfur Hexafluoride Gas Analysis and Purification of SGCC State Grid Anhui Electric Power Research Institute Hefei 230022 China |
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Abstract Partial discharge (PD) detection and fault diagnosis are of great significance to the stable operation of GIS. The optical detection method is a new method for measuring and analyzing PD signals, which has received wide attention due to its high detection confidence and strong anti-interference. The light guide rod is a kind of optical sensor with high transmittance, high hardness, and easy to seal, which can be used for PD detection in GIS. Currently, there is a lack of research on the characteristics of PD optical pulse in GIS using a light guide rod. In PD fault diagnosis, fewer studies based on optical data, especially optical images. Therefore, in this paper, optical detection and image diagnostic research of PD in GIS is carried out using the light guide rod.
Firstly, a test platform for electrical and optical detection of PD in GIS was built, achieving synchronous collection of optical and current signals. The typical PD defects were designed. Then, the PD optical signals were measured by the light guide rod and the fluorescent fiber. After that, the optical parameters of the two methods were compared and analyzed, including the PD inception voltage, the phase distribution, the maximum amplitude of the optical pulse, and the average amplitude and number of the optical pulse.
For optical image fault diagnosis, the PRPS patterns of optical PDs were constructed based on the data collected by the light guide rod. The pyramid histogram of oriented gradients (PHOG) features of the optical images were extracted and input into the classifier of the beetle antennae search (BAS) and support vector machine (SVM) for fault diagnosis. Finally, the method in this paper was compared with other image feature extractions and support vector machine optimization algorithms.
The conclusions are as follows: (1) The light guide rod can measure the PD in GIS and reflect the signal characteristics of typical PDs. The discharge phase distribution of the optical signals is similar to that of the discharge phase distribution collected by the pulse current method, and the discharge pulse amplitude increases with the increase of the applied voltage. (2) Typical PD signals were collected by the light guide rod and the fluorescent fiber, respectively. The defects were placed in the center of the tank, and the relative distance between the discharge source and the two sensors was kept the same. The results show that the starting detection sensitivity of the light guide rod was higher. For the floating discharge and particle discharge, the optical signals obtained by the two methods are basically the same in phase distribution, amplitude and number due to the large intensity and small number of discharge pulses. For the surface discharge and needle tip discharge, the detection effect of the light guide rod was better. (3) An optical image fault diagnosis method based on PHOG-BAS-SVM is proposed. The PHOG features can well obtain the local and overall feature information of images. The results show that the accuracy of fault diagnosis based on this model reaches 90.4%.
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Received: 04 December 2023
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2025, Vol. 40 
