Abstract
High-impedance faults (HIFs) detection with high reliability has been a prominent challenge for protection engineers over the years. This is mainly because of the nature and characteristics this type of fault has. Although HIFs do not directly pose danger to the power system equipment, they pose a serious threat to the public and agricultural environment. In this paper, a technique which comprises of a signal decomposition technique, feature extraction, feature selection and fault classification is proposed. A practical experiment was conducted to validate the proposed method. The scheme is implemented in MATLAB and tested on the machine intelligence platform WEKA. The scheme was tested on different classifiers and showed impressive results for both simulations and practical cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sedighizadeh, M., Rezazadeh, A., Elkalashy, N.I.: Approaches in high impedance fault detection detection - a chronological review. Adv. Electr. Comput. Eng. 10, 114–118 (2010)
Laaksonen, H., Hovila, P.: Straightforward detection method for high-impedance faults. Int. Rev. Electr. Eng. (IREE). 12(2) (2017)
Mamishev, A.V., Russell, B.D., Benner, C.L.: Analysis of high impedance faults using fractal techniques. IEEE Trans. Power Sys. 11(1), 435–440 (1996)
Lai, T.M., Snider, L.A., Lo, E.: High impedance fault detection using discrete wavelet transform and frequency range and RMS conversion. IEEE Trans. Power Deliver 20(1), 397–407 (2003)
Michalik, M., Rebizant, W., Lukowicz, M., Lee, S.J., Kang, S.H.: High-Impedance fault detection in distribution networks with use of wavelet-based algorithm. IEEE Trans. Power Deliver. 21(4), 1793–1802 (2006)
Ebron, S.: A neural network processing strategy for the detection of high impedance fault. Masters’s Thesis, Electrical and Computer Engineering Department, NCSU (1988)
Ebron, S., Lubkeman, D.L., White, M.: A neural network approach to the detection of incipient fault on power distribution feeders. In: IEEE/PES Transmission and Distribution Conference, New Orleans, LA (1989)
Jota, F.G., Jota, P.S.: High-impedance fault identification using a fuzzy reasoning system. IEEE Prot., Gener., Transm., Distrib. 45(6), 656–661 (1998)
Macedo, J.R., Resende, J.W., Bissochi, C.A.: Proposition of an Interharmonic-based methodology for high-impedance fault detection in distribution systems. IET Gener. Transm. Distrib. 9(16), 2293–2601 (2015)
Costa, F.B., Souza, B.A., Brito, N.S.D., Silver, J.A.C.B., Santos, W.C.: Real-time detection of transients induced by high-impedance faults based on the boundary wavelet transform. IEEE Trans. Ind. Appl. 51(6), 5312–5323 (2015)
Sarwagya, K., De, S., Nayak, P.K.: High-impedance fault detection in electrical power distribution systems using moving sum approach. IET The Institution of Engineering and Technology. 1–8 (2017)
Vieira, F.L., Filho, J.M.C., Silveira, P.M., Guerrero, C.A.V., Leite, M.P.: High impedance fault detection and location in distribution networks using smart meters. In: 18th International Conference on Harmonics and Quality of Power (ICHQP), Ljublijana, Slovenia (2018)
Silver, S., Costa, P., Santana, M., Leite, D.: Evoloving neuro-fuzzy network for real-time high impedance fault detection and classification. Neural Comput. Appl. 40(175), 1–14 (2018)
Magagula, X.G., Hamam, Y., Jordaan, J.A., Yusuff, A.A.: Fault Detection and classification method using DWT and SVM in a power distribution network. In: IEEE PES-/IAS, Ghana, Accra (2017)
Yusuff, A.A., Jimoh, A.A., Munda, J.L.: Determinant-based feature extraction for fault detection and classification for power transmission lines. IET Gener. Transm. Distrib. 5(12), 1259–1267 (2011)
Sekar, K., Mohanty, N.K.: Combined mathematics morphology and data mining based high impedance fault detection. In: 1st International Conference on Power Engineering, Computing and Control. VIT University, Chennai Campus (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Moloi, K., Jordaan, J., Hamam, Y. (2019). Application of Machine Learning Based Technique for High Impedance Fault Detection in Power Distribution Network. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-22808-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22807-1
Online ISBN: 978-3-030-22808-8
eBook Packages: Computer ScienceComputer Science (R0)