Lightning overvoltage identification method for transmission lines based on deep learning
YANG Zhibo
WANG Jiachen
Abstract：[Objective]Transmission lines operating in regions with strong lightning activity are highly vulnerable to lightning strikes.Double-circuit lines on the same tower feature compact structures and significant electromagnetic coupling effects,resulting in consistently high lightning fault rates.Existing lightning protection measures rely heavily on statistical experience and cannot effectively distinguish different types of lightning faults such as shielding failures and back flashovers,making precise protection difficult.Consequently,line tripping accidents remain a recurring problem,posing a serious threat to the safe and stable operation of the power grid.To address this issue,this study proposed a deep learning-based lightning fault identification method with high-accuracy automatic identification of shielding failure and back flashover,providing effective technical support for differentiated lightning protection in transmission lines.[Methods]A lightning fault simulation model for 220 kV double-circuit transmission lines on the same tower was developed using the electromagnetic transient simulation software ATP-EMTP to obtain overvoltage response data under different lightning current amplitudes and grounding resistance conditions.To address the non-stationarity and mode mixing of lightning signals,ensemble empirical mode decomposition(EEMD)was introduced,where Gaussian white noise was added to suppress mode mixing.The first four intrinsic mode functions(IMFs)were extracted to preserve the major characteristic components.Subsequently,frequency slice wavelet transform(FSWT)was applied to compute multi-band energy ratios,which,together with lightning current amplitude and grounding resistance,formed a multidimensional feature set.In terms of classification modeling,a CNN-LSTM-Attention deep learning architecture was proposed:CNN extracted spatial features,LSTM modeled temporal dependencies,and the Attention mechanism focused on critical information,thus enabling effective fusion and identification of complex signal features.[Results]Experimental results demonstrate the excellent performance of the proposed method in distinguishing shielding failure from back flashover.The overall identification accuracy reaches 98.6%,with both precision and recall exceeding 98.5%,and an F1-score of 0.99.Compared with benchmark models such as SVM and CNN,the proposed method exhibits a clear advantage in identification accuracy.Results from 10 independent comparative experiments show an average accuracy of 99.7%and a variance of 0.00093,fully verifying its stability and reliability.[Conclusions]The lightning fault identification method based on EEMD-FSWT feature extraction and the CNN-LSTM-Attention fusion model effectively characterizes the time-frequency features of lightning signals of double-circuit transmission lines on the same tower,achieving high-accuracy differentiation between lightning shielding failure and back flashover.This method not only improves the accuracy and timeliness of fault diagnosis but also provides important data support for formulating differentiated lightning protection strategies.The research results have significant engineering application value and strong potential for wide application in reducing lightning-induced line tripping and ensuring the safe and stable operation of power systems.
Keywords：double-circuit transmission lineshielding failure and back flashover identificationensemble empirical mode decompositionfrequency slice wavelet transformCNN-LSTM-Attention modelenergy ratio characteristicATP-EMTP simulationfault diagnosis
Publication Date：2026-01-25
Online Publishing Date：2026-03-17(First online date of this platform, not the publication date of the document)
Pages：10( 19-28 )