A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network

• Autorzy: Fuada S. , Shiddieqy H. A. , Adiono T.

Identyfikatory

DOI

10.24425/ijet.2020.134024

• Języki publikacji: EN

Abstrakty

EN

To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.

Słowa kluczowe

EN

fault detection; fault classification; transmission lines; convolutional neural network; machine learning

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2020
• Tom: Vol. 66, No. 4
• Strony: 655--664
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Fuada S.

• program Studi sistem telekomunikasi, Universitas Pendidikan Indonesia, Indonesia

autor

Shiddieqy H. A.

• University Center of Excellence on Microelectronics, Instiut Teknologi Bandung, Indonesia

autor

Adiono T.

• University Center of Excellence on Microelectronics, Instiut Teknologi Bandung, Indonesia

Bibliografia

• [1] P. Kundur, et al., “Definition and classification of power system stability IEEE/CIGRE join task force on stability terms and definitions” 19(3), pp. 1387–1401, 2004.
• [2] IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines, Vol. 2014. [Online], Available at: https://doi.org/10.1109/IEEESTD.2015.7024095.
• [3] H.A. Shiddieqy, et al., “Implementations Fault Detection Phasor Measurement Unit Using Zybo SoC,” Proc. of ISESD, October 2017.
• [4] S. Upadhyay, et al., “Fault classification and detection in transmission lines using ANN,” Proc. of ICIRCA, 2018.
• [5] A. Yadav and Y. Dash, “An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination” Advances in Artificial Neural Systems, 2014.
• [6] K. Chen, et al., “Detection and Classification of Transmission Line Faults based on Unsupervised Feature Learning and Convolutional Sparse Autoencoder” IEEE Transactions on Smart Grid, Vol. 9(3), pp. 1748–58, 2016.
• [7] A. Abdullah, “Ultrafast Transmission Line Fault Detection using a DWT-based ANN.” IEEE Transactions on Industry Applications, Vol. 54(2), pp. 1182–93, 2015.
• [8] R.C. Mishra, et al., “Wavelet Based Transmission Line Fault Classification and Location,” Proc. of ISEG, 2014.
• [9] X.G. Magagula, et al., “Fault Detection and Classification Method Using DWT And SVM in A Power Distribution Network,” Proc. of IEEE PES Power Affrica, pp. 1–6, 2017.
• [10] A.G. Saik and R.R.V. Pulipaka, “A New Wavelet Based Fault Detection, Classification and Location in Transmission Lines.” Int. J. of Electrical Power and Energy Systems, Vol. 63, pp. 35–40, 2015.
• [11] K.M. Silva, et al., “Fault Detection and Classification in Transmission Lines based on Wavelet Transform and ANN,” Vol. 21(4), pp. 2058–63, 2006.
• [12] Y. Lecun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, 521, pp. 436-444, May 2015.
• [13] Standford, “CS231n Convolutional Neural Networks for Visual Recognition,” [Online]. Available at http://cs231n.stanford.edu/.
• [14] L.C. Lin, “A Tutorial of the Wavelet Transform,” [Online]. Available at http://disp.ee.ntu.edu.tw/tutorial/WaveletTutorial.pdf
• [15] P. Ray, and D.P. Mishra, “Support Vector Machine Based Fault Classification and Location of a Long Transmission Line” Engineering Science and Technology, an Int. J., Vol. 19(3), pp. 1368–80, 2016.
• [16] M. Patel and R.N. Patel, “Fault Detection and Classification on a Transmission Line Using Wavelet Multi Resolution Analysis and Neural Network.” Int. J. of Computer Applications, 47(22), Vol. 27–33, 2012.
• [17] H.A. Shiddieqy, F.I. Hariadi, and T. Adiono, “Effect of Sampling Variation in Accuracy for Fault Transmission Line Classification Application Based on Convolutional Neural Network,” Proc. of ISESD, 2018.
• [18] J. Klomjit, et al., “Comparison of Mother Wavelet for Classification Fault On Hybrid Transmission Line Systems,” Proc. of iCAST, pp. 527–32, 2018.
• [19] H.A. Shiddieqy, F.I. Hariadi, and T. Adiono, “Power Line Transmission Fault Modeling and Dataset Generation for AI based Automatic Detection,” Proc. of Int. Symp. On Electronics and Smart Devices (ISESD), 2019.