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RISS 인기검색어
- 검색키워드
- 저자 : Zhibin Qiu (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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A Numerical Approach for Lightning Impulse Flashover Voltage Prediction of Typical Air Gaps
Zhibin Qiu,Jiangjun Ruan,Congpeng Huang,Wenjie Xu,Daochun Huang 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.3
This paper proposes a numerical approach to predict the critical flashover voltages of air gaps under lightning impulses. For an air gap, the impulse voltage waveform features and electric field features are defined to characterize its energy storage status before the initiation of breakdown. These features are taken as the input parameters of the predictive model established by support vector machine (SVM). Given an applied voltage range, the golden section search method is used to compute the prediction results efficiently. This method was applied to predict the critical flashover voltages of rod-rod, rod-plane and sphere-plane gaps over a wide range of gap lengths and impulse voltage waveshapes. The predicted results coincide well with the experimental data, with the same trends and acceptable errors. The mean absolute percentage errors of 6 groups of test samples are within 4.6%, which demonstrates the validity and accuracy of the predictive model. This method provides an effectual way to obtain the critical flashover voltage and might be helpful to estimate the safe clearances of air gaps for insulation design.
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A Numerical Approach for Lightning Impulse Flashover Voltage Prediction of Typical Air Gaps
Qiu, Zhibin,Ruan, Jiangjun,Huang, Congpeng,Xu, Wenjie,Huang, Daochun The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.3
This paper proposes a numerical approach to predict the critical flashover voltages of air gaps under lightning impulses. For an air gap, the impulse voltage waveform features and electric field features are defined to characterize its energy storage status before the initiation of breakdown. These features are taken as the input parameters of the predictive model established by support vector machine (SVM). Given an applied voltage range, the golden section search method is used to compute the prediction results efficiently. This method was applied to predict the critical flashover voltages of rod-rod, rod-plane and sphere-plane gaps over a wide range of gap lengths and impulse voltage waveshapes. The predicted results coincide well with the experimental data, with the same trends and acceptable errors. The mean absolute percentage errors of 6 groups of test samples are within 4.6%, which demonstrates the validity and accuracy of the predictive model. This method provides an effectual way to obtain the critical flashover voltage and might be helpful to estimate the safe clearances of air gaps for insulation design.
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Qiu Zhibin,Wang Haixiang,Liao Caibo,Lu Zuwen,Kuang Yanjun 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species.
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