Research and Design of Online Monitoring Device for Operating State of GIS Isolating Switch Operationg Mechanism

Wang, Zezhou; Chen, Gang; Zhang, Jinbo; Huang, Qinwen; Ye, Jintao; Jiang, Yi

doi:10.1109/ei252483.2021.9713130

Cited by 4 publications

(2 citation statements)

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“…Chen et al [9] designed an intelligent isolating switch position monitoring system based on attitude sensing, with the aim of achieving high accuracy for the detection of the open/closed position of the isolating switch. For monitoring the working conditions of the GIS high-voltage isolating switch operating mechanism online, an online monitoring method for the working status of the operating mechanism of disconnector, according to the double analysis method of current and angular displacement, was proposed to realize online monitoring of the GIS disconnector by Wang et al [12]. With practicality in mind, Luo et al [10] proposed a "double-confirmation" method that combined a microswitch with auxiliary contacts to improve the accuracy of detecting the open/closed position of the isolation switch.…”

Section: Related Workmentioning

confidence: 99%

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Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning

Zhang

et al. 2023

Electronics

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The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as image recognition, are vulnerable to weather or light intensity, while microswitch, attitude sensing and other methods are unable to induce equipment power failure with sufficient speed, which brings many new challenges to the operation and maintenance of a GIS. Therefore, this research designs a GIS shell vibration detection system for knife switch state discrimination, introduces a deep learning algorithm for knife switch vibration signal analysis, and proposes a fast convolutional neural network (FCNN) to identify the knife switch state. For the designed FCNN, a normalization layer and a nonlinear activation layer are used after each convolution layer to obviously reduce feature quantity and increase algorithm efficiency. In order to test the recognition performance based on the vibration detection system, this study carried out two kinds of knife switch opening and closing experiments. One group with artificial noise was added, the other group did not include artifical noise, and a corresponding data set was constructed. The experimental results show that the recognition accuracy for both datasets reaches 100%, and the FCNN algorithm is better than the five classical algorithms in terms of prediction efficiency. This study shows that the vibration detection technology based on deep learning can be used to effectively identify the opening and closing state of a GIS knife switch, and is expected to be promoted and applied.

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Section: Related Workmentioning

confidence: 99%

“…In order to effectively detect the state of the GIS switch, researchers have proposed a variety of technologies, including image recognition [6][7][8], attitude sensing [9], microswitch [10], magnetic induction [11], current and angular displacement [12], vibration sensing [13] and so on.…”

Section: Introductionmentioning

confidence: 99%