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Risk and impact-centered non-stationary signal analysis based on fault signatures for Djibouti power system

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Abstract

Power system engineers’ intention is to produce power, transport it, and finally distribute it to customers under safe and reliable operating conditions to provide continuous and stable electrical energy. However, this goal is often hindered by unexpected faults that can lead to system breakdown. As a result, power system modeling is an effective technique which helps the analysis of power systems. Besides this technique, mathematical methods provide comprehensible information about the system's state. Furthermore, various studies have employed different techniques to detect electrical faults. In this paper, electrical faults are selected using a risk and impact approach, and fault characteristics are found using the Short-time Fourier transform. The case study is the Djibouti power network, and it is modeled with all real parameters using MATLAB-SIMULINK software. Following that, several fault scenarios were run, and the analysis was conducted using the proposed mathematical method. Ultimately, the simulation results indicate that the most critical faults are single-line-to-ground and double-line-to-ground. Hence, the extraction of signal features for these fault types is carried out. These faults have a high short circuit current, which can cause damage to the electrical network, and while clearing the fault, the oscillatory transient state appears with low-frequency components. These frequency components significantly affect power quality, thereby reducing the system’s performance.

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References

  1. Elmasry W, Wadi M (2022) Detection of faults in electrical power grids using an enhanced anomaly-based method. Arab J Sci Eng 47:14899–14914. https://doi.org/10.1007/s13369-022-07030-x

    Article  Google Scholar 

  2. Nardelli PHJ, Rubido N, Wang C, Baptista MS, Pomalaza-Raez C, Cardieri P, Latva-aho M (2014) Models for the modern power grid. Eur Phys J Spec Topics 223:2423–2437. https://doi.org/10.1140/epjst/e2014-02219-6

    Article  Google Scholar 

  3. Fahim SR, Sarker SK, Muyeen SM, Das SK, Kamwa I (2021) A deep learning based intelligent approach in detection and classification of transmission line faults. Int J Electr Power Energy Syst 133:107102. https://doi.org/10.1016/j.ijepes.2021.107102

    Article  Google Scholar 

  4. Tu’uau DY, Timaima M, Assaf MH (2020) Electric power system fault analysis. Wseas Trans Circuits Syst 19:19–27. https://doi.org/10.37394/23201.2020.19.3

    Article  Google Scholar 

  5. Alvarez GP (2020) Real-time fault detection and diagnosis using intelligent monitoring and supervision systems. In: Fault detection, diagnosis and prognosis. IntechOpen

    Google Scholar 

  6. Alshorman O, Alshorman A (2021) A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines. IJECE 11:2820. https://doi.org/10.11591/ijece.v11i4.pp2820-2829

    Article  Google Scholar 

  7. Khetarpal P, Tripathi MM (2020) A critical and comprehensive review on power quality disturbance detection and classification. Sustain Comput Inform Syst 28:100417. https://doi.org/10.1016/j.suscom.2020.100417

    Article  Google Scholar 

  8. Akinci TC, Ekren N, Seker S, Yildirim S (2013) Continuous wavelet transform for ferroresonance phenomena in electric power systems. Int J Electr Power Energy Syst 44:403–409. https://doi.org/10.1016/j.ijepes.2012.07.001

    Article  Google Scholar 

  9. Şengüler T, Seker S (2017) Continuous wavelet transform for ferroresonance detection in power systems. Electr Eng. https://doi.org/10.1007/s00202-016-0387-0

    Article  Google Scholar 

  10. Multi-resolution Wavelet Analysis for Ferroresonance Phenomena in Power Systems. https://www.tandfonline.com/doi/epdf/https://doi.org/10.1080/15325008.2014.880972?needAccess=true

  11. Jurado F, Saenz JR (2002) Comparison between discrete STFT and wavelets for the analysis of power quality events. Electr Power Syst Res 62:183–190. https://doi.org/10.1016/S0378-7796(02)00035-4

    Article  Google Scholar 

  12. Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18:199–221. https://doi.org/10.1016/S0888-3270(03)00075-X

    Article  Google Scholar 

  13. Ukil A, Yeap YM, Satpathi K (2020) Frequency-domain based fault detection: application of short-time Fourier transform. In: Ukil A, Yeap YM, Satpathi K (eds) Fault analysis and protection system design for DC grids. Springer, Singapore, pp 195–221

    Chapter  Google Scholar 

  14. Satpathi K, Yeap YM, Ukil A, Geddada N (2018) Short-time Fourier transform based transient analysis of VSC interfaced point-to-point DC system. IEEE Trans Industr Electron 65:4080–4091. https://doi.org/10.1109/TIE.2017.2758745

    Article  Google Scholar 

  15. Liu D, Cheng W, Wen W (2020) Rolling bearing fault diagnosis via STFT and improved instantaneous frequency estimation method. Procedia Manuf 49:166–172. https://doi.org/10.1016/j.promfg.2020.07.014

    Article  Google Scholar 

  16. Zhong D, Guo W, He D (2019) An intelligent fault diagnosis method based on STFT and convolutional neural network for bearings under variable working conditions. In: 2019 prognostics and system health management conference (PHM-Qingdao). pp 1–6

  17. Li D, Dong J, Peng K (2023) A novel adaptive STFT-SFA based fault detection method for nonstationary processes. IEEE Sens J 23:10748–10757. https://doi.org/10.1109/JSEN.2023.3264994

    Article  Google Scholar 

  18. Pillot B, Muselli M, Poggi P, Haurant P, Hared I (2013) Solar energy potential atlas for planning energy system off-grid electrification in the Republic of Djibouti. Energy Convers Manag 69:131–147. https://doi.org/10.1016/j.enconman.2013.01.035

    Article  Google Scholar 

  19. Dommisse J, Bouckaert J, Basso E, Hammou K (2022) Schéma Directeur du réseau National de Distribution électrique de Djibouti Sujet : Livrable 1A—Analyse de la demande et du réseau MT/BT existant Commentaires. ENGIE Impact, Belgique

  20. Kehtarnavaz N (2008) CHAPTER 7—frequency domain processing. In: Kehtarnavaz N (ed) Digital signal processing system design, Second Edition. Academic Press, Burlington, pp 175–196

    Chapter  Google Scholar 

  21. Gundewar SK, Kane PV (2022) Bearing fault diagnosis using time segmented Fourier synchrosqueezed transform images and convolution neural network. Measurement 203:111855. https://doi.org/10.1016/j.measurement.2022.111855

    Article  Google Scholar 

  22. Arikan K, Önal E, Şeker S (2020) Time-frequency analysis of partial discharge current pulses in different gas environment under lightning impulse. Meas Sci Rev 20:196–201. https://doi.org/10.2478/msr-2020-0024

    Article  Google Scholar 

  23. Aye FA (2009) Integration des energies Renouvelables pour Une Politique Energetique Durable a Djibouti. http://catalog.ihsn.org/citations/26402

  24. Nasser Mohamed Y, Seker S, Akinci TC (2023) Signal processing application based on a hybrid wavelet transform to fault detection and identification in power system. Information 14:540. https://doi.org/10.3390/info14100540

    Article  Google Scholar 

  25. Rakshit H, Ullah MA (2014) A comparative study on window functions for designing efficient FIR filter. In: 2014 9th international forum on strategic technology (IFOST). IEEE, pp 91–96

    Chapter  Google Scholar 

  26. Özhan O (2022) Short-time-Fourier transform. In: Özhan O (ed) Basic transforms for electrical engineering. Springer, Cham, pp 441–464

    Chapter  Google Scholar 

  27. Millette PA (2013) The Heisenberg uncertainty principle and the Nyquist–Shannon sampling theorem. Prog Phys 9:9–14

    Google Scholar 

  28. Nicola F (2023) The uncertainty principle for the short-time Fourier transform on finite cyclic groups: cases of equality. J Funct Anal 284:109924. https://doi.org/10.1016/j.jfa.2023.109924

    Article  MathSciNet  Google Scholar 

  29. Gautam P, Jhala AK (2015) Fault detection & classification of 3-phase transmission line. Int Res J Eng Technol (IRJET) 02:5

    Google Scholar 

  30. Sarwito S, Koenhardono ES, Martha KPT (2018) Analysis of transient response and harmonic disturbances on the Tanker’s electrical system based on simulation. Int J Mar Eng Innov Res. https://doi.org/10.12962/j25481479.v3i1.4134

    Article  Google Scholar 

  31. Ghorbani J, Atashpar S, Mehrafrooz A, Mokhtari H (2011) Nonlinear loads effect on harmonic distortion and losses of distribution networks. In: Proceedings of the international power system conference PSC. Tehran, Iran, pp 17–18

  32. Macii D, Petri D (2018) Harmonics estimation in transient conditions using static and dynamic frequency-domain techniques. In: 2018 IEEE 9th international workshop on applied measurements for power systems (AMPS). pp 1–6

  33. Sivaraman P, Sharmeela C (2021) Chapter 1—power quality and its characteristics. In: Sanjeevikumar P, Sharmeela C, Holm-Nielsen JB, Sivaraman P (eds) Power quality in modern power systems. Academic Press, Cambridge, pp 1–60

    Google Scholar 

  34. Bollen MHJ, Styvaktakis E, Gu IY-H (2005) Categorization and analysis of power system transients. IEEE Trans Power Deliv 20:2298–2306. https://doi.org/10.1109/TPWRD.2004.843386

    Article  Google Scholar 

  35. McGranaghan MF, Dugan RC, Beaty HW (2012) Electrical power systems quality. McGraw-Hill Education, New York

    Google Scholar 

  36. Bach NH, Vu LH, Nguyen VD, Pham DP (2023) Classifying marine mammals signal using cubic splines interpolation combining with triple loss variational auto-encoder. Sci Rep 13:19984. https://doi.org/10.1038/s41598-023-47320-4

    Article  Google Scholar 

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Acknowledgements

Two of the authors, Yasmin Nasser Mohamed and Oubah Isman Okieh, have received financial support from the University of Djibouti for their PhD studies at ITU, and the authors express their gratitude to the Djibouti university and the electricity company (EDD) for providing technical data.

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No funds, grants, or other support was received.

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Contributions

YNM contributed to conceptualization, methodology, software, formal analysis, writing—original draft, and visualization. OIO contributed to conceptualization and software. SS contributed to resources, writing—review & editing, and supervision.

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Correspondence to Yasmin Nasser Mohamed.

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Nasser Mohamed, Y., Isman Okieh, O. & Seker, S. Risk and impact-centered non-stationary signal analysis based on fault signatures for Djibouti power system. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02322-x

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