Response Characteristics Prediction of Surge Protective Device Based on NARX Neural Network

Du, Lin; Zhang, Qi; Gao, Cheng; Chen, Hailin; Yin, Qiang; Ding, Kan; Yapeng, Fu; Qu, Dexin; Guo, Fei

doi:10.1109/temc.2018.2881216

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Considering that both the data of HSV and CBC are essentially the discrete‐time signals, the implicit nonlinear relation between them can be described by using artificial neural network model. The NARX with exogenous inputs NARX is a shallow recurrent dynamic network (Tong et al., 2018), and is widely adopted to describe the nonlinear input‐output relationship of an unknown system (Du et al., 2020). Based on this, a NARX model is built to infer CBC by inputting the measured HSV images under an artificially triggered negative lightning (ATNL).…”

Section: Introductionmentioning

confidence: 99%

Applying High‐Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network

Zhang,

Luo,

Shi

et al. 2024

Earth and Space Science

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The high‐speed video (HSV) images were taken as the input data of the nonlinear autoregressive network (NARX) to reconstruct the return‐stroke channel base current (CBC). The integrated luminosity (IL) of discharge channel is firstly obtained by summing the pixel values in HSV images, and the correlation between the IL and CBC is analyzed as well. Then, a NARX model is trained by taking the IL and CBC as the input and output data, respectively. With the input of the IL of another discharge channel, the trained NARX model is applied to inverse the corresponding CBC. The inversed result suggests that the proposed method can be used to infer the CBC that was not yet measured.

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Section: Introductionmentioning

confidence: 99%

Applying High‐Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network

Zhang,

Luo,

Shi

et al. 2024

Earth and Space Science

Self Cite

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“…If the transfer function represents the stronger non-linear frequency regulation response, the order of the model will increase and the accuracy will decrease. The NARXNN model represents the non-linear time series system well [19][20][21][22][23]. The authors in [22] used the field data about battery energy storage system to build the NARXNN model.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the battery cell temperature was estimated by that model. The response characteristics of surge protective devices with the fast rising time electromagnetic pulse were analysed by utilising the NARXNN model in [23]. However, there is little study on the WFFR response using NARXNN modelling methods according to the available papers.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven lumped dynamic modelling of wind farm frequency regulation characteristics

Qin

et al. 2022

IET Cyber-Phy Sys Theory & Ap

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High proportion of wind power in the power grid leads to the problem of power system frequency instability, which requires the wind farm itself to have the ability of frequency adjustment; therefore, it is particularly important to conduct modelling of wind farm frequency regulation (WFFR) response characteristics. During the modelling process, it is generally necessary to establish a model for each working condition separately, which will bring huge workload. In addition, the accuracy of the model decreases when the frequency response is non‐linear. Therefore, this paper investigates the modelling of WFFR response characteristics in different working conditions. A data preprocessing method based on WFFR strategy and modelling methods is introduced. Then, data‐based transfer function models of WFFR response characteristics for different working conditions are constructed. After that, the gaps between different models are measured using a gap metric technique to analyse dynamic similarity between models. Finally, in order to make up for the defect of transfer function models, a non‐linear autoregressive with exogenous input neural networks (NARXNN) model of WFFR response characteristics is constructed utilising lumped data of all working conditions; then, the trained model is tested by the data of each working condition to verify the accuracy and universality.

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Classification of Gas Discharge Tube’s Electromagnetic Pulse Response Based on Kmeans Method

Wang,

Cui,

Dong

et al. 2024

Lecture Notes in Electrical Engineering

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Response Characteristics Prediction of Surge Protective Device Based on NARX Neural Network

Cited by 8 publications

References 17 publications

Applying High‐Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network

Applying High‐Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network

Data‐driven lumped dynamic modelling of wind farm frequency regulation characteristics

Classification of Gas Discharge Tube’s Electromagnetic Pulse Response Based on Kmeans Method

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