Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Intelligent fault diagnosis for distribution grid considering renewable energy intermittency

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a versatile intelligent fault diagnosis (IFD) scheme for a distribution grid integrated with intermittent renewable energy resources (RER). Renewable generation parameters (wind speed and solar irradiation) and load demand intermittency along with fault information (fault inception angle and resistance) uncertainty are modeled by employing different probability density functions. Then, advanced signal processing techniques are used to extract useful features from the recorded signals. The proposed approach sends the extracted features as inputs to feedforward neural networks (FF-NNs) to diagnose (detect, classify, and identify faulty sections) and locate the faults. The presented results confirm the efficacy of the developed IFD scheme and show that it is independent of renewable generation and load demand intermittency along with fault information uncertainty. Additionally, the proposed scheme is independent of the presence of measurement noises. Furthermore, this work investigates the effectiveness of the developed IFD scheme under various contingency cases (branch outages and RER generation outages). Finally, a laboratory prototype IFD scheme is built by integrating a physical phasor measurement unit (PMU) with a real-time digital simulator (RTDS) rack to diagnose faults in the distribution grid. The results confirm the effectiveness of the prototype IFD scheme, as they show good agreement with the simulation results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

\(\alpha\), \(\beta\) :

Scale and shape parameters.

\(\sigma\), \(\mu\) :

Standard deviation and mean value.

C v :

Coefficient of variation.

G, \(G_{r}\) :

Weibull PDF-predicted solar radiation and rated solar radiation.

R F :

Fault resistance.

R min :

Minimum value of the fault resistance.

R max :

Maximum value of the fault resistance.

U :

Uniform distribution.

\(v\), \(v_{r}\) :

Weibull PDF-predicted and rated wind speeds.

\(v_{ci}\), \(v_{co}\) :

Cutin and cutoff wind speeds.

References

  1. Southwest Power Pool, “A Robust Transmission Grid Benefits Everyone,” 2021. Accessed: May 04, 2021. [Online]. Available: https://www.spp.org/documents/10047/benefits_of_robust_transmission_grid.pdf.

  2. U.S. Energy Information Administration (EIA), “Delivery to consumers,” U.S. Energy Information Administration (EIA), Oct. 22, 2020. https://www.eia.gov/energyexplained/electricity/delivery-to-consumers.php (Accesssed May 21, 2021)

  3. Shafiullah M, Abido MA, Al-Mohammed AH (2022) Power system fault diagnosis: a wide area measurement based intelligent approach, 1st edn. Elsevier, Amsterdam, Netherlands

    Google Scholar 

  4. Gururajapathy SS, Mokhlis H, Illias HA (2017) Fault location and detection techniques in power distribution systems with distributed generation: A review, Renew Sustain Energy Rev, vol. 74. Elsevier Ltd, pp. 949–958, Jul. 01, 2017, https://doi.org/10.1016/j.rser.2017.03.021

  5. Farughian A, Kumpulainen L, Kauhaniemi K (2018) Review of methodologies for earth fault indication and location in compensated and unearthed MV distribution networks. Electr Power Syst Res 154:373–380. https://doi.org/10.1016/J.EPSR.2017.09.006

    Article  Google Scholar 

  6. Shafiullah M, Abido MA (2017) A review on distribution grid fault location techniques. Electr Power Components Syst 45(8):807–824. https://doi.org/10.1080/15325008.2017.1310772

    Article  Google Scholar 

  7. Rui L, Nan P, Zhi Y, Zare F (2018) A novel single-phase-to-earth fault location method for distribution network based on zero-sequence components distribution characteristics. Int J Electr Power Energy Syst 102:11–22. https://doi.org/10.1016/J.IJEPES.2018.04.015

    Article  Google Scholar 

  8. Das S, Karnik N, Santoso S (2012) Distribution fault-locating algorithms using current only. IEEE Trans Power Deliv 27(3):1144–1153. https://doi.org/10.1109/TPWRD.2012.2191422

    Article  Google Scholar 

  9. Dashti R, Ghasemi M, Daisy M (2018) Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying Π line model. Energy 159:344–360. https://doi.org/10.1016/j.energy.2018.06.111

    Article  Google Scholar 

  10. Bahmanyar A, Jamali S, Estebsari A, Bompard E (2017) A comparison framework for distribution system outage and fault location methods. Electr Power Syst Res 145:19–34. https://doi.org/10.1016/J.EPSR.2016.12.018

    Article  Google Scholar 

  11. Aftab MA, Hussain SMS, Ali I, Ustun TS (2020) Dynamic protection of power systems with high penetration of renewables: A review of the traveling wave based fault location techniques, Int J Electr Power Energy Syst, 114, https://doi.org/10.1016/j.ijepes.2019.105410

  12. Dutta R, Samantaray SR (2018) Assessment of impedance based fault locator for AC micro-grid. Renew Energy Focus 26:1–10. https://doi.org/10.1016/J.REF.2018.05.001

    Article  Google Scholar 

  13. Shafiullah M, Abido M, Abdel-Fattah T (2018) Distribution grids fault location employing ST based optimized machine learning approach. Energies 11(9):2328. https://doi.org/10.3390/en11092328

    Article  Google Scholar 

  14. Marín-Quintero J, Orozco-Henao C, Percybrooks WS, Vélez JC, Montoya OD, Gil-González W (2021) Toward an adaptive protection scheme in active distribution networks: intelligent approach fault detector. Appl Soft Comput 98:106839. https://doi.org/10.1016/j.asoc.2020.106839

    Article  Google Scholar 

  15. Acacio LC, Guaracy PA, Diniz TO, Araujo DRRP, Araujo LR (2017) Evaluation of the impact of different neural network structure and data input on fault detection, 2017 IEEE PES Innovative Smart Grid Technologies Conference—Latin America (ISGT Latin America). IEEE, pp. 1–5, Sep., https://doi.org/10.1109/ISGT-LA.2017.8126699

  16. Zhang J, He ZY, Lin S, Zhang YB, Qian QQ (2013) An ANFIS-based fault classification approach in power distribution system. Int J Electr Power Energy Syst 49:243–252. https://doi.org/10.1016/j.ijepes.2012.12.005

    Article  Google Scholar 

  17. Wang N, Aravinthan V, Ding Y (2014) Feeder-level fault detection and classification with multiple sensors: a smart grid scenario, 2014 IEEE Workshop on Statistical Signal Processing (SSP). IEEE, pp. 37–40, https://doi.org/10.1109/SSP.2014.6884569

  18. Reche EA, de Sousa JV, Coury DV, Fernandes RAS (2018) Data Mining-Based Method to Reduce Multiple Estimation for Fault Location in Radial Distribution Systems, IEEE Trans Smart Grid, p. 1, https://doi.org/10.1109/TSG.2018.2832840.

  19. Mokhlis H, Li H (2011) Non-linear representation of voltage sag profiles for fault location in distribution networks. Int J Electr Power Energy Syst 33(1):124–130. https://doi.org/10.1016/j.ijepes.2010.06.020

    Article  Google Scholar 

  20. Lotfifard S, Kezunovic M, Mousavi MJ (2011) Voltage Sag Data Utilization for Distribution Fault Location. IEEE Trans Power Deliv 26(2):1239–1246. https://doi.org/10.1109/TPWRD.2010.2098891

    Article  Google Scholar 

  21. Tremblay M, Fazio B, Valiquette D (2017) Using voltage sag measurements for advanced fault location and condition-based maintenance. CIRED—Open Access Proce J 2017(1):893–896. https://doi.org/10.1049/oap-cired.2017.0066

    Article  Google Scholar 

  22. Dehghani M, Khooban MH, Niknam T (2016) Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations. Int J Electr Power Energy Syst 78:455–462. https://doi.org/10.1016/j.ijepes.2015.11.048

    Article  Google Scholar 

  23. Jalayer M, Orsenigo C, Vercellis C (2021) Fault detection and diagnosis for rotating machinery: a model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms. Comput Ind 125:103378. https://doi.org/10.1016/j.compind.2020.103378

    Article  Google Scholar 

  24. Shafiullah M, Abido MA, Al-Hamouz Z (2017) Wavelet-based extreme learning machine for distribution grid fault location. IET Gener Transm Distrib 11(17):4256–4263. https://doi.org/10.1049/iet-gtd.2017.0656

    Article  Google Scholar 

  25. Shafiullah M, Rana MJ, Abido MA (2017) Power system stability enhancement through optimal design of PSS employing PSO,” In 4th International Conference on Advances in Electrical Engineering, ICAEE 2017, vol. 2018-Janua, pp. 26–31, https://doi.org/10.1109/ICAEE.2017.8255321

  26. Shafiullah M, Abido MA (2018) S-Transform based FFNN approach for distribution grids fault detection and classification. IEEE Access 6:8080–8088. https://doi.org/10.1109/ACCESS.2018.2809045

    Article  Google Scholar 

  27. Shafiullah M, Abido MA, Al-Mohammed AH (2022) Intelligent fault diagnosis technique for distribution grid, Power Syst Fault Diagnosis, pp. 249–292, https://doi.org/10.1016/B978-0-323-88429-7.00005-9

  28. Jana S, Dutta G (2012) Wavelet entropy and neural network based fault detection on a non radial power system network. IOSR J Electr Electron Eng 2(3):26–31

    Article  Google Scholar 

  29. Manassero G, Di Santo SG, Souto L (2017) Heuristic method for fault location in distribution feeders with the presence of distributed generation. IEEE Trans Smart Grid 8(6):2849–2858. https://doi.org/10.1109/TSG.2016.2598487

    Article  Google Scholar 

  30. Jia K, Bi T, Ren Z, Thomas DWP, Sumner M (2018) High frequency impedance based fault location in distribution system with DGs. IEEE Trans Smart Grid 9(2):807–816. https://doi.org/10.1109/TSG.2016.2566673

    Article  Google Scholar 

  31. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 36(2):1627–1637. https://doi.org/10.3233/JIFS-18807

    Article  Google Scholar 

  32. Chen R, Lin T, Bi R, Xu X (2017) Novel strategy for accurate locating of voltage sag sources in smart distribution networks with inverter-interfaced distributed generators, Energies, 10(11), https://doi.org/10.3390/en10111885.

  33. Jannat MB, Savić AS (2016) Optimal capacitor placement in distribution networks regarding uncertainty in active power load and distributed generation units production. IET Gener Transm Distrib 10(12):3060–3067. https://doi.org/10.1049/iet-gtd.2016.0192

    Article  Google Scholar 

  34. Deng X, Lv T (2020) Power system planning with increasing variable renewable energy: a review of optimization models, J Cleaner Prod, 246. Elsevier Ltd, p. 118962, Feb. 10, https://doi.org/10.1016/j.jclepro.2019.118962

  35. Zare Oskouei M, Mohammadi-Ivatloo B, Abapour M, Shafiee M, Anvari-Moghaddam A (2021) Techno-economic and environmental assessment of the coordinated operation of regional grid-connected energy hubs considering high penetration of wind power. J Clean Prod 280:124275. https://doi.org/10.1016/j.jclepro.2020.124275

    Article  Google Scholar 

  36. Saha S, Johnson N (2019) Modeling and Simulation in XENDEE IEEE 34 Node Test Feeder, Tempe, Arizona, USA, 2016. Accessed: Oct. 01, 2019. [Online]. Available: https://www.xendee.com/IEEE/Xendee_ASU_IEEE_34_BUS.pdf.

  37. IEEE PES AMPS DSAS Test Feeder Working Group, “Resources | PES Test Feeder,” IEEE Power and Energy Society, 2017. https://site.ieee.org/pes-testfeeders/resources/ (Accessed Oct. 01, 2019)

  38. Abd-rabou AM, Soliman AM, Mokhtar AS (2015) Impact of DG different types on the grid performance. J Electr Syst Inf Technol 2(2):149–160. https://doi.org/10.1016/J.JESIT.2015.04.001

    Article  Google Scholar 

  39. Zakariazadeh A, Jadid S, Siano P (2014) Smart microgrid energy and reserve scheduling with demand response using stochastic optimization. Int J Electr Power Energy Syst 63:523–533. https://doi.org/10.1016/j.ijepes.2014.06.037

    Article  Google Scholar 

  40. Mojtahedzadeh S, Ravadanegh SN, Haghifam M-R (2017) Optimal multiple microgrids based forming of greenfield distribution network under uncertainty. IET Renew Power Gener 11(7):1059–1068. https://doi.org/10.1049/iet-rpg.2016.0934

    Article  Google Scholar 

  41. Atwa YM, El-Saadany EF, Salama MMA, Seethapathy R (2010) Optimal Renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 25(1):360–370. https://doi.org/10.1109/TPWRS.2009.2030276

    Article  Google Scholar 

  42. Liu Z, Wen F, Ledwich G (2011) Optimal siting and sizing of distributed generators in distribution systems considering uncertainties. IEEE Trans Power Deliv 26(4):2541–2551. https://doi.org/10.1109/TPWRD.2011.2165972

    Article  Google Scholar 

  43. Wang Y, Wu W, Zhang B, Li Z, Zheng W (2015) Robust voltage control model for active distribution network considering PVs and loads uncertainties, In 2015 IEEE Power and Energy Society General Meeting, pp. 1–5, https://doi.org/10.1109/PESGM.2015.7286317.

  44. Chen X, Wu W, Zhang B, Shi X (2016) A robust approach for active distribution network restoration based on scenario techniques considering load and DG uncertainties, In 2016 IEEE Power and Energy Society General Meeting (PESGM), Jul. 2016, pp. 1–5, https://doi.org/10.1109/PESGM.2016.7741591

  45. Xu B et al (2019) Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming. Appl Energy 253:113535. https://doi.org/10.1016/J.APENERGY.2019.113535

    Article  Google Scholar 

  46. Wang X, Chang J, Meng X, Wang Y (2019) Hydro-thermal-wind-photovoltaic coordinated operation considering the comprehensive utilization of reservoirs. Energy Convers Manag 198:111824. https://doi.org/10.1016/J.ENCONMAN.2019.111824

    Article  Google Scholar 

  47. Zhang H, Lu Z, Hu W, Wang Y, Dong L, Zhang J (2019) Coordinated optimal operation of hydro–wind–solar integrated systems. Appl Energy 242:883–896. https://doi.org/10.1016/J.APENERGY.2019.03.064

    Article  Google Scholar 

  48. Feng Z, Niu W, Cheng C, Zhou J (2017) Peak shaving operation of hydro-thermal-nuclear plants serving multiple power grids by linear programming. Energy 135:210–219. https://doi.org/10.1016/J.ENERGY.2017.06.097

    Article  Google Scholar 

  49. Rajan R, Fernandez FM (2019) Power control strategy of photovoltaic plants for frequency regulation in a hybrid power system. Int J Electr Power Energy Syst 110:171–183. https://doi.org/10.1016/J.IJEPES.2019.03.009

    Article  Google Scholar 

  50. Ijaz M, Shafiullah M, Abido MA (2015) Classification of power quality disturbances using Wavelet Transform and Optimized ANN,” 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP), Proceedings of the Conference on. pp. 1–6, https://doi.org/10.1109/ISAP.2015.7325522

  51. Borghetti A, Corsi S, Nucci CA, Paolone M, Peretto L, Tinarelli R (2006) On the use of continuous-wavelet transform for fault location in distribution power systems. Int J Electr Power Energy Syst 28(9):608–617. https://doi.org/10.1016/j.ijepes.2006.03.001

    Article  Google Scholar 

  52. Wang Y (2011) Efficient stockwell transform with applications to image processing. University of Waterloo, Canada

    Google Scholar 

  53. Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44(4):998–1001. https://doi.org/10.1109/78.492555

    Article  Google Scholar 

  54. Mansinha L, Stockwell RG, Lowe RP (1997) Pattern analysis with two-dimensional spectral localisation: applications of two-dimensional S transforms. Phys A Stat Mech its Appl 239(1–3):286–295. https://doi.org/10.1016/S0378-4371(96)00487-6

    Article  Google Scholar 

  55. Rana MJ, Shahriar MS, Shafiullah M (2019) Levenberg–Marquardt neural network to estimate UPFC-coordinated PSS parameters to enhance power system stability, Neural Comput Appl, 31(4), https://doi.org/10.1007/s00521-017-3156-8

  56. Sun Y, Li S, Lin B, Fu X, Ramezani M, Jaithwa I (2017) Artificial neural network for control and grid integration of residential solar photovoltaic systems. IEEE Trans Sustain Energy 8(4):1484–1495. https://doi.org/10.1109/TSTE.2017.2691669

    Article  Google Scholar 

  57. Masiur Rahman S, Khondaker AN, Imtiaz Hossain M, Shafiullah M, Hasan MA (2017) Neurogenetic modeling of energy demand in the United Arab Emirates, Saudi Arabia, and Qatar, Environ Prog Sustain Energy, 36(4), https://doi.org/10.1002/ep.12558

  58. Wong YJ, Arumugasamy SK, Jewaratnam J (2018) Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization. Clean Technol Environ Policy 20(9):1971–1986. https://doi.org/10.1007/s10098-018-1577-4

    Article  Google Scholar 

  59. Yang J, Ma J (2019) Feed-forward neural network training using sparse representation. Expert Syst Appl 116:255–264. https://doi.org/10.1016/J.ESWA.2018.08.038

    Article  Google Scholar 

  60. Klomjit J, Ngaopitakkul A (2016) Selection of proper input pattern in fuzzy logic algorithm for classifying the fault type in underground distribution system, 2016 IEEE Region 10 Conference (TENCON). IEEE, pp. 2650–2655, https://doi.org/10.1109/TENCON.2016.7848519

  61. Choi M-S, Lee S-J, Lee D-S, Jin B-G (2004) A new fault location algorithm using direct circuit analysis for distribution systems. IEEE Trans Power Deliv 19(1):35–41. https://doi.org/10.1109/TPWRD.2003.820433

    Article  Google Scholar 

  62. Choi MS, Lee SJ, S. Il Lim, Lee DS, Yang X (2007) A direct three-phase circuit analysis-based fault location for line-to-line fault, IEEE Trans Power Deliv, 22(4): 2541–2547, https://doi.org/10.1109/TPWRD.2007.905535

  63. Salim R, Resener M, Filomena AD, de Oliveira KRC, Bretas AS (2009) Extended fault-location formulation for power distribution systems, IEEE Trans Power Deliv, 24(2): 508–516, Accessed: May 23, 2016. [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4797797.

  64. Lee SJ et al (2004) An intelligent and efficient fault location and diagnosis scheme for radial distribution systems. IEEE Trans Power Deliv 19(2):524–532. https://doi.org/10.1109/TPWRD.2003.820431

    Article  Google Scholar 

  65. Bahmanyar A, Jamali S (2017) Fault location in active distribution networks using non-synchronized measurements. Int J Electr Power Energy Syst 93:451–458. https://doi.org/10.1016/J.IJEPES.2017.06.018

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the support provided by the Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) at King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia, under Project No. # INRE2104.

Author information

Authors and Affiliations

  1. Interdisciplinary Research Center for Renewable Energy and Power Systems, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    Md Shafiullah & M. A. Abido

  2. Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    M. A. Abido

  3. K.A.CARE Energy Research and Innovation Center, Dhahran, 31261, Saudi Arabia

    M. A. Abido

  4. Engineering and Design Department, Saudi Electricity Company, Dammam, Saudi Arabia

    A. H. Al-Mohammed

Authors
  1. Md Shafiullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. A. Abido
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. H. Al-Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md Shafiullah.

Ethics declarations

Conflict of interest

No potential conflict of interest was reported by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafiullah, M., Abido, M.A. & Al-Mohammed, A.H. Intelligent fault diagnosis for distribution grid considering renewable energy intermittency. Neural Comput & Applic 34, 16473–16492 (2022). https://doi.org/10.1007/s00521-022-07155-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07155-y

Keywords

Search

Navigation