Top

Neural Computing and Applications

Published in:

06-06-2021 | Original Article

A CNN-LSTM-based fault classifier and locator for underground cables

Authors: Ruphan Swaminathan, Sanhita Mishra, Aurobinda Routray, Sarat Chandra Swain

Published in: Neural Computing and Applications | Issue 22/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This paper presents a data-driven approach to classify and locate the faults occurring in underground distribution cables using a CNN-LSTM-based deep learning architecture. A sliding window method is adopted, using the current and voltage signal patches as inputs. The combination of various system parameters is considered to generalize the performance. Additionally, Gaussian noise is added to resemble and extend the approach to practical scenarios. The trained model is evaluated with the data from a new simulation, and the results confirm the generalizability of the adopted method. A comparison is made with other feasible approaches, which show that the adopted method is preferable to achieve better performance. Further, to ease the extension of the proposed method for different systems with similar configurations, parameters of the trained deep learning networks are exploited with the help of transfer learning.

previous article Dual temporal convolutional network for single-lead fibrillation waveform extraction

next article Use of GMDH-type neural network to model the mechanical behavior of a cement-treated sand

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Livieris IE, Pintelas E, Pintelas P (2020) A CNN–LSTM model for gold price time-series forecasting. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04867-xCrossRefMATH

Chen K, Hu J, He J (2016) Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder. IEEE Trans Smart Grid 9(3):1748–1758

Patel B, Bera P (2018) Detection of power swing and fault during power swing using Lissajous figure. IEEE trans power deliv 33(6):3019–3027CrossRef

Mishra DP, Ray P (2018) Fault detection, location and classification of a transmission line. Neural Comput Appl 30(5):1377–1424CrossRef

Pandey A, Younan NH (2010) Underground cable fault detection and identification via fourier analysis. In: International conference on high voltage engineering and application. IEEE, pp 618–621

Silva KM, Souza BA, Brito NSD (2006) Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE trans power deliv 21(4):2058–2063CrossRef

K. R. K, Dash PK, (2013) A new real-time fast discrete S-transform for cross-differential protection of shunt-compensated power systems. IEEE Trans Power Deliv 28(1):402–410CrossRef

Jamehbozorg A, Shahrtash SM (2010) A decision-tree-based method for fault classification in single-circuit transmission lines. IEEE Trans Power Deliv 25(4):2190–2196CrossRef

Mohd Amiruddin AAA, Zabiri H, Taqvi SAA, Tufa LD (2020) Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neural Comput Appl 32(2):447–472CrossRef

10.

Zimmerman K, Costello D (2005) Impedance-based fault location experience. In: 58th annual conference for protective relay engineers, 2005. IEEE, pp 211–226

11.

Filomena AD, Resener M, Salim RH, Bretas AS (2009) Fault location for underground distribution feeders: an extended impedance-based formulation with capacitive current compensation. Int J Electr Power Energy Syst 31(9):489–496CrossRef

12.

Provoost F, van Buijtenen W (2009) Practical experience with fault location in MV cable networks. In: IET conference publications. IET, pp 1–4

13.

Sabin DD, Dimitriu C, Santiago D, Baroudi G (2009) Overview of an automatic underground distribution fault location system. In: IEEE power & energy society general meeting. IEEE, pp 1–5

14.

Liang J, Jing T, Niu H, Wang J (2020) Two-terminal fault location method of distribution network based on adaptive convolution neural network. IEEE Access 8:54035–54043CrossRef

15.

Zhang F, Liu Q, Liu Y, Tong N, Chen S, Zhang C (2020) Novel fault location method for power systems based on attention mechanism and double structure GRU neural network. IEEE Access 8:75237–75248CrossRef

16.

Mirzaei M, Vahidi B, Hosseinian SH (2019) Accurate fault location and faulted section determination based on deep learning for a parallel-compensated three-terminal transmission line. IET Gener Transm Distrib 13(13):2770–2778CrossRef

17.

Chi P, Zhang Z, Liang R, Cheng C, Chen S (2020) A CNN recognition method for early stage of 10 kV single core cable based on sheath current. Electric Power Syst Res 184(106292):106292CrossRef

18.

Lee C-K, Shin Y-J (2021) Detection and assessment of I&C cable faults using time–frequency R-CNN-based reflectometry. IEEE Trans Ind Electron 68:1581–1590CrossRef

19.

Qiao M, Yan S, Tang X, Xu C (2020) Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads. IEEE Access 8:66257–66269CrossRef

20.

Da Silva FF, Bak CL (2013) Electromagnetic transients in power cables, 2013th edn. Springer, LondonCrossRef

21.

Kingma. DP, Jimmy B (2015) Adam: a method for stochastic optimization. In: 3rd international conference for learning representations, pp 1–15

22.

Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, pp 1–15

23.

Koley E, Verma K, Ghosh S (2015) An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only. Springerplus 4:551. https://doi.org/10.1186/s40064-015-1342CrossRef

24.

Ray P, Mishra DP (2016) Support vector machine based fault classification and location of a long transmission line. Eng Sci Technol Int J 19(3):1368–1380

25.

Terzija VV, Ciric R, Nouri H (2011) Improved fault analysis method based on a new arc resistance formula. IEEE Trans Power Deliv 26(1):120–126CrossRef

26.

Dargan S, Kumar M, Ayyagari MR, Kumar G (2020) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27(4):1071–1092MathSciNetCrossRef

Title: A CNN-LSTM-based fault classifier and locator for underground cables
Authors: Ruphan Swaminathan
Sanhita Mishra
Aurobinda Routray
Sarat Chandra Swain
Publication date: 06-06-2021
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 22/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-06153-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 22/2021

Human object detection: An enhanced black widow optimization algorithm with deep convolution neural network

A novel classification framework using multiple bandwidth method with optimized CNN for brain–computer interfaces with EEG-fNIRS signals

Dynamic mobile charger scheduling with partial charging strategy for WSNs using deep-Q-networks

Improvement of grey wolf optimizer with adaptive middle filter to adjust support vector machine parameters to predict diabetes complications

Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms

Neural network method for automatic data generation in adaptive information systems

Premium Partner