High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load


  • Mohammed Naisan Allawi Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
  • Ali Nasser Hussain Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
  • Mousa K. Wali Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
  • Daniel Augusto Pereira Universidade Federal de Lavras, Lavras, Brazil




Frequency Spectrum, Fast Fourier Transform, Artificial Neural Network, PSD, Distribution Feeder, High Impedance Fault


High Impedance Fault (HIF) detection in distribution networks is challenging for protection engineers, mainly because HIFs possess unique characteristics, including non-linearity, asymmetry, randomness, and relatively low fault current levels compared to the feeder load current. In this regard, the study proposes an approach to detect HIFs in a radial distribution feeder based on the spectrum analysis of current signals at the substation bus. The proposed method comprises two stages: signal decomposition and feature extraction. Fast Fourier Transform (FFT) is utilized for signal decomposition, followed by feature extraction. These features are subsequently used as input to an artificial neural network (ANN) to distinguish HIF from non-HIF events, such as linear and non-linear load switching, capacitor bank switching, and transformer energization. The proposed method's efficacy is rigorously evaluated under various dynamic conditions, demonstrating that the method can detect and differentiate HIFs from non-fault events with a high detection rate and high accuracy of 99.3%, irrespective of the HIF location and fault resistance.


Download data is not yet available.

Author Biographies

Mohammed Naisan Allawi, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.


Ali Nasser Hussain, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.


Mousa K. Wali, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.


Daniel Augusto Pereira, Universidade Federal de Lavras, Lavras, Brazil



Tengdin, John, Ron Westfall, and Kevin Stephan."High Impedance Fault Detection Technology," in "Report of PSRC Working Group D15," Mar. 1996, Available: https://grouper.ieee.org/groups/td/dist/documents/highz.pdf.

A. Soheili, J. Sadeh, H. Lomei, and K. Muttaqi, "A new high impedance fault detection scheme: Fourier based approach," in 2016 IEEE International Conference on Power System Technology (POWERCON), 2016, pp. 1-6. doi: https://doi.org/10.1109/POWERCON.2016.7754052.

S. Chakraborty and S. Das, "Application of Smart Meters in High Impedance Fault Detection on Distribution Systems," IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3465-3473, 2019. doi: https://doi.org/10.1109/TSG.2018.2828414.

H. Bai, B. Tang, T. Cheng, and H. Liu, "High impedance fault detection method in distribution network based on improved Emanuel model and DenseNet," Energy Reports, vol. 8, pp. 982-987, Nov 2022. doi: https://doi.org/10.1016/j.egyr.2022.05.199.

A. Aljohani and I. Habiballah, "High-Impedance Fault Diagnosis: A Review," vol. 13, no. 23, p. 6447, 2020. doi: https://doi.org/10.3390/en13236447.

B. Hao, "AI in arcing-HIF detection: a brief review," vol. 3, no. 4, pp. 435-444, 2020. doi: https://doi.org/10.1049/iet-stg.2019.0091.

S. Khavari, R. Dashti, H. R. Shaker, and A. Santos, "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers," vol. 13, no. 9, p. 2331, 2020. doi: https://doi.org/10.3390/en13092331.

S. Wang and P. Dehghanian, "On the Use of Artificial Intelligence for High Impedance Fault Detection and Electrical Safety," IEEE Transactions on Industry Applications, vol. 56, no. 6, pp. 7208-7216, 2020. doi: https://doi.org/10.1109/TIA.2020.3017698.

P. Obi, E. Amako, and C. Ezeonye, "High impedance fault arc analysis on 11 kV distribution networks," Nigerian Journal of Technological Development, vol. 19, no. 2, pp. 143-149, 2022. doi: https://doi.org/10.4314/njtd.v19i2.6.

D. A. Gadanayak and R. K. Mallick, "Interharmonics based high impedance fault detection in distribution systems using maximum overlap wavelet packet transform and a modified empirical mode decomposition," International Journal of Electrical Power & Energy Systems, vol. 112, pp. 282-293, Nov 2019. doi: https://doi.org/10.1016/j.ijepes.2019.04.050.

A. Soheili, J. Sadeh, and R. Bakhshi, "Modified FFT based high impedance fault detection technique considering distribution non-linear loads: Simulation and experimental data analysis," International Journal of Electrical Power & Energy Systems, vol. 94, pp. 124-140, Jan 2018. doi: https://doi.org/10.1016/j.ijepes.2017.06.035.

K. Sekar, N. K. Mohanty, and A. K. Sahoo, "High impedance fault detection using wavelet transform," in Technologies for Smart-City Energy Security and Power (ICSESP), 2018, pp. 1-6. doi: https://doi.org/10.1109/ICSESP.2018.8376740.

B. K. Chaitanya, A. Yadav, and M. Pazoki, "High Impedance Fault Detection Scheme for Active Distribution Network Using Empirical Wavelet Transform and Support Vector Machine," in 15th International Conference on Protection and Automation of Power Systems (IPAPS), 2020, pp. 149-152. doi: https://doi.org/10.1109/IPAPS52181.2020.9375620.

S. Silva, P. Costa, M. Gouvea, A. Lacerda, F. Alves, and D. Leite, "High impedance fault detection in power distribution systems using wavelet transform and evolving neural network," Electric Power Systems Research, vol. 154, pp. 474-483, Jan 2018. doi: https://doi.org/10.1016/j.epsr.2017.08.039.

T. Sirojan, S. Lu, B. T. Phung, D. Zhang, and E. Ambikairajah, "Sustainable Deep Learning at Grid Edge for Real-Time High Impedance Fault Detection," IEEE Transactions on Sustainable Computing, vol. 7, no. 2, pp. 346-357, 2022. doi: https://doi.org/10.1109/TSUSC.2018.2879960.

É. M. Lima, N. S. D. Brito, and B. A. d. Souza, "High impedance fault detection based on Stockwell transform and third harmonic current phase angle," Electric Power Systems Research, vol. 175, p. 105931, Oct 2019. doi: https://doi.org/10.1016/j.epsr.2019.105931.

D. Guillen, J. Olveres, V. Torres-García, and B. Escalante-Ramírez, "Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults," IEEE Access, vol. 10, pp. 79962-79973, 2022. doi: https://doi.org/10.1109/ACCESS.2022.3194525.

H. Lala and S. Karmakar, "Detection and Experimental Validation of High Impedance Arc Fault in Distribution System Using Empirical Mode Decomposition," IEEE Systems Journal, vol. 14, no. 3, pp. 3494-3505, 2020. doi: https://doi.org/10.1109/JSYST.2020.2969966.

K. Moloi and I. Davidson, "High Impedance Fault Detection Protection Scheme for Power Distribution Systems," vol. 10, no. 22, p. 4298, 2022. doi: https://doi.org/10.3390/math10224298.

H. Shu, Y. Deng, J. Dong, P. Cao, B. Yang, and Z. Bo, "A detection method of high impedance arcing fault for distribution network with distributed generation based on CEEMDAN and TEO algorithm," vol. 31, no. 8, p. e12926, 2021. doi: https://doi.org/10.1002/2050-7038.12926.

O. A. Gashteroodkhani, M. Majidi, and M. Etezadi-Amoli, "Fire hazard mitigation in distribution systems through high impedance fault detection," Electric Power Systems Research, vol. 192, p. 106928, Mar 2021. doi: https://doi.org/10.1016/j.epsr.2020.106928.

K. Rai, F. Hojatpanah, F. B. Ajaei, J. M. Guerrero, and K. Grolinger, "Deep learning for high-impedance fault detection and classification: transformer-CNN," Neural Computing and Applications, vol. 34, no. 16, pp. 14067-14084, Aug 2022. doi: https://doi.org/10.1007/s00521-022-07219-z.

H. G. Yeh, S. Sim, R. Yinger, and R. Bravo, "A comparative study of orthogonal algorithms for detecting the HIF in MDCs," in 2017 IEEE Green Energy and Smart Systems Conference (IGESSC), 2017, pp. 1-7. doi: https://doi.org/10.1109/IGESC.2017.8283456.

V. C. Nikolaidis, A. D. Patsidis, and A. M. Tsimtsios, "High impedance fault modelling and application of detection techniques with EMTP-RV," vol. 2018, no. 15, pp. 1120-1124, 2018. doi: https://doi.org/10.1049/joe.2018.0217.

S. Roy and S. Debnath, "PSD based high impedance fault detection and classification in distribution system," Measurement, vol. 169, p. 108366, Feb 2021. doi: https://doi.org/10.1016/j.measurement.2020.108366.

M. S. Hassan, K. Kamal, and T. A. H. Ratlamwala, "Fault classification of power plants using artificial neural network," Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 44, no. 3, pp. 7665-7680, Sep 2022. doi: https://doi.org/10.1080/15567036.2022.2113936.

N. B. Roy and K. Bhattacharya, Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults. CRC Press, 2021.

M. Mishra and R. R. Panigrahi, "Taxonomy of high impedance fault detection algorithm," Measurement, vol. 148, p. 106955, Dec 2019. doi: https://doi.org/10.1016/j.measurement.2019.106955.

K. Rai, F. Hojatpanah, F. Badrkhani Ajaei, and K. Grolinger, "Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders," vol. 14, no. 12, p. 3623, 2021. doi: https://doi.org/10.3390/en14123623.

P. Rai, N. D. Londhe, and R. Raj, "Fault classification in power system distribution network integrated with distributed generators using CNN," Electric Power Systems Research, vol. 192, p. 106914, Mar 2021. doi: https://doi.org/10.1016/j.epsr.2020.106914.

K. S. V. Swarna, A. Vinayagam, M. Belsam Jeba Ananth, P. Venkatesh Kumar, V. Veerasamy, and P. Radhakrishnan, "A KNN based random subspace ensemble classifier for detection and discrimination of high impedance fault in PV integrated power network," Measurement, vol. 187, p. 110333, Jan 2022. doi: https://doi.org/10.1016/j.measurement.2021.110333.

J. R. Macedo, J. W. Resende, C. A. Bissochi Jr, D. Carvalho, and F. C. Castro, "Proposition of an interharmonic-based methodology for high-impedance fault detection in distribution systems," vol. 9, no. 16, pp. 2593-2601, 2015. doi: https://doi.org/10.1049/iet-gtd.2015.0407.

AC/DC Converter




How to Cite

Allawi, M. N., Hussain, A. N., Wali, M. K., & Daniel Augusto Pereira. (2024). High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load. Journal of Techniques, 6(2), 36–47. https://doi.org/10.51173/jt.v6i2.1320



Electrical and Electronic Engineering

Most read articles by the same author(s)

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.