Fault diagnosis of power grid based on variational mode decomposition and convolutional neural network

Zhang, Qian; Ma, Wenhao; Li, Guoli; Ding, Jinjin; Xie, Mei

doi:10.1016/j.epsr.2022.107871

Cited by 46 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to verify the advantages of the proposed method, the fault location method based on local features (VMD-CNN) [11] is compared. In order to ensure objectivity, both methods use the same fault sample set.…”

Section: The Comparison Of Fault Location Methodsmentioning

confidence: 99%

Research on fault location method for distribution network based on traveling wave full waveform matching

Feng,

Deng,

2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

View full text Add to dashboard Cite

Because of the complex structure of distribution network, the traveling wave at different fault sections shows different energy distribution in the time and frequency domain. In this paper, a fault location method for distribution network based on traveling wave full waveform (TWFW) matching is proposed. First, transmission processes of traveling wave are analysed at different fault sections theoretically. The time and frequency distribution of TWFW is quantified by wavelet energy entropy. The mapping relationship between the fault section and the energy distribution of traveling wave is discussed, whereby the matching relationship is established. Then, TWFW is taken as the input sample to build a convolutional neural network (CNN) with a category classifier. CNN extracts the energy distribution of TWFW in the time and frequency domain to mine the fault section information. The proposed method requires less location device and makes full use of fault information in the full frequency band. Extensive numerical experiments show that this method achieves accurate fault section location on the test set containing a large number of fault samples with the fault impedance greater than 1kΩ.

show abstract

Section: The Comparison Of Fault Location Methodsmentioning

confidence: 99%

Research on fault location method for distribution network based on traveling wave full waveform matching

Feng,

Deng,

2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

View full text Add to dashboard Cite

show abstract

“…The voltage signal is processed after the classification process with EMDT to identify the TW's arrival times at the terminal. The results are compared with the standard approaches from the EMD to prove the influence of the TEO on accuracy [10,23].…”

Section: Case Studymentioning

confidence: 99%

Traveling Wave Fault Location for AC Transmission Lines: An Approach Based on the Application of EMD and Teager Energy Operator

Oliveira,

Moreira,

Picanço

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper proposes an approach for the single-ended and the double-ended traveling wave based fault location algorithm using the empirical mode decomposition associated with the Teager energy operator to extract characteristic data from the faulted voltage signals of an overhead transmission line. The simulation of the power system uses the JMarti line model, with an ideally transposed transmission line, and it was carried out using the Alternative Transients Program (ATP) software. After, the MATLAB® software was used for extracting the traveling wave arrival times and to perform the single-ended and the double-ended fault location algorithms for all simulated scenarios in ATP. The numerical and graphical results prove that the proposed methodology with the Teager Energy operator and the double-ended analysis can better extract the characteristic data of the voltage signals and estimate the fault location with good accuracy, with percentage error of 0.034% for the best results, depending only on the fault type and the sampling rate adopted.

show abstract

“…[3] The key problem of the frequency domain analysis is the extraction of the principal component of the intrinsic frequency of the faulty traveling wave, due to the propagation process of the faulty traveling wave at the connection point will occur in the folding reflection phenomenon, resulting in spectral aliasing, thus increasing the difficulty of the principal component of the intrinsic frequency extraction. Literature [4][5][6] Empirical modal decomposition algorithm (EMD), ensemble empirical modal decomposition (EEMD), variational modal decomposition (VMD) and other methods are proposed, which inhibit the spectral aliasing to a certain extent, but the effect is not ideal. In this paper, we propose to apply the improved (VMD) to decompose the faulty traveling wave signal, after multi-signal classification (MUSIC) The principal component of the traveling wave intrinsic frequency is extracted; then the distance of each end bus from the fault point is calculated using the intrinsic frequency method.…”

Section: Introductionmentioning

confidence: 99%

New energy grid-connected line fault localization method based on improved VMD-MUSIC

Qi,

Song,

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In view of the limitations of the traditional double-ended traveling wave ranging method in new energy grid-connected transmission lines and the inability to effectively extract the deep characteristics of the traveling wave intrinsic frequency. This paper proposes a new energy grid-connected line fault localization method based on the fusion of Variational Mode Decomposition (VMD) and Multiple Signal Classification (MUSIC). Firstly, the fault traveling wave signal is decomposed into multiple intrinsic modal components by improving VMD; then, the spectrograms under the corresponding modes are obtained by using MUSIC to realize the accurate extraction of the intrinsic frequency of the traveling wave; Finally, the fault distance is calculated based on the relationship between the fault distance at the monitoring points at each end and the known line length. Simulation results show that the method can overcome the influence of spectral aliasing on the extraction of the principal component of the intrinsic frequency, and can accurately determine the fault interval, and the localization results are not affected by the fault distance, the fault type, and the fault resistance, which has good adaptability and high localization accuracy.

show abstract

Fault diagnosis of power grid based on variational mode decomposition and convolutional neural network

Cited by 46 publications

References 17 publications

Research on fault location method for distribution network based on traveling wave full waveform matching

Research on fault location method for distribution network based on traveling wave full waveform matching

Traveling Wave Fault Location for AC Transmission Lines: An Approach Based on the Application of EMD and Teager Energy Operator

New energy grid-connected line fault localization method based on improved VMD-MUSIC

Contact Info

Product

Resources

About