Automatic identification method for lightning strike faults in 500kV ultra-high-voltage direct current transmission lines
WU Rongrong
HUANG Zhidu
XU Wenping
TANG Jie
HUANG Wei
Abstract：[Objective]Transmission lines are an important component of the power system,and most line faults are caused by lightning strikes.Lightning interference identification is an important basis for ensuring the correctness of traveling wave fault analysis.To quickly identify lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines and ensure the stability of the power system,an automatic identification method for lightning strike faults was proposed.[Methods]The dictionary learning algorithm was used to denoise the transmission line signal,and the minimum error objective function was established for signal amplitude fluctuation.The dictionary matrix was optimized by dictionary update through hot start and Newton iteration to obtain the denoised lightning strike fault signals of transmission lines.This effectively reduced noise interference and improved identification accuracy.The wavelet time entropy method was used to extract key features from the denoised lightning strike fault signals of transmission lines.The wavelet coefficients formed by wavelet transform were used to reconstruct the coefficients in a specific layer.A sliding time window was defined to calculate entropy and information content,and features were extracted from the transient signal of lightning current in transmission lines to provide data support for lightning strike fault identification.Different characteristic signals of lightning strikes were collected,and features were trained using ensemble learning algorithms.Multiple weak classifiers were generated and fused into a strong classifier through weights,which was used to classify each transient signal sample of lightning current.The generalization ability of the classifier was improved,and it was enabled to cope with different types of lightning strike fault signals.The classifier was optimized using the sparrow algorithm,and the optimal parameters of the classifier were obtained by randomly initializing the sparrow population,calculating fitness values,screening sparrows,updating sparrow discoverers and joiners,and performing mutation operations.The optimal parameters were input into the optimized classifier to achieve automatic identification of lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines.The sparrow algorithm,as a heuristic optimization algorithm,has the characteristics of adaptability and a strong global search ability.It can quickly find the optimal parameters of the classifier in a complex search space,improving optimization efficiency and identification speed.[Results]The experimental results show that the proposed method has a signal-to-noise ratio(SNR)of over 40 dB,a mean square error(MSE)of identification to be less than 1.5,an identification efficiency of over 90％,and identification time of about 2.5 s after denoising.It can accurately and efficiently identify lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines.[Conclusion]This method provides a new technical means for automatic identification of lightning strike faults in 500 kV ultra-high-voltage direct current transmission lines.It significantly enhances identification accuracy and efficiency,providing strong support for the safe and stable operation of the power system.At the same time,this method can also be extended to identification of other fault types of transmission lines,which has a wide application value.
Keywords：500 kV ultra-high-voltagedirect current transmission linelightning strike faultdictionary learning algorithmwavelet time entropysparrow optimization algorithm
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 160-167 )