Skip to main content

Advertisement

SpringerLink
Log in

A review of power system protection and asset management with machine learning techniques

  • Review Article
  • Published:
Energy Systems Aims and scope Submit manuscript

Abstract

Power system protection and asset management have drawn the attention of researchers for several decades; but they still suffer from unresolved and challenging technical issues. The situation has been recently exacerbated in the wake of the ever-changing landscape of power systems driven by the growing uncertainty and volatility subsequent to the vast renewable energy integration, more frequent natural extreme events due to climate changes, increasing malicious cyberattacks, and more constrained transmission systems as the result of load growth and limited investments. On the opposite side, the proliferation of advanced measuring devices such as phasor measurement units, emerging electric and non-electric sensors, and Internet of Thing (IoT)-enabled data gathering platforms continually expand/nourish the databases; they hence offer unprecedented opportunities to take the advantage of data-driven techniques. Machine learning (ML) as a principal class of artificial intelligence is the perfect match solution to this need and has newly revoked many researchers’ interests to tackle the problems excluding their exact/detailed models. This paper aims to provide an overview on applications of ML techniques in power system protection and asset management. This paper elaborates on issues pertaining to (1) synchronous generators, (2) power transformers, (3) transmission lines, and (4) special and system-integrity protection schemes. In addition to the opportunities offered by the ML techniques, this paper discourses on the barriers and challenges to the wide-spread application of ML techniques in real-world practices.

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

Access this article

Log in via an institution

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Li, Z., Shahidehpour, M., Aminifar, F., Alabdulwahab, A., Al-Turki, Y.: Networked microgrids for enhancing the power system resilience. Proc. IEEE 105(7), 1289–1310 (2017)

    Article  Google Scholar 

  2. Li, Z., Shahidehpour, M., Aminifar, F.: Cybersecurity in distributed power systems. Proc. IEEE 105(7), 1367–1388 (2017)

    Article  Google Scholar 

  3. Gholami, A., Shekari, T., Amirioun, M.H., Aminifar, F., Amini, M.H., Sargolzaei, A.: Toward a consensus on the definition and taxonomy of power system resilience. IEEE Access 6, 32035–32053 (2018)

    Article  Google Scholar 

  4. Gholami, A., Aminifar, F., Shahidehpour, M.: Front lines against the darkness: enhancing the resilience of the electricity grid through microgrid facilities. IEEE Electr. Mag. 4(1), 18–24 (2016)

    Article  Google Scholar 

  5. Aminifar, F., Shahidehpour, M., Alabdulwahab, A., Abusorrah, A., Al-Turki, Y.: The proliferation of solar photovoltaics: their impact on widespread deployment of electric vehicles. IEEE Electr. Mag. 8(3), 79–91 (2020)

    Article  Google Scholar 

  6. Tohidi, Y., Aminifar, F., Fotuhi-Firuzabad, M.: Generation expansion and retirement planning based on the stochastic programming. Electric Power Syst. Res. 104, 138–145 (2013)

    Article  Google Scholar 

  7. Farhoumandi, M., Aminifar, F., Shahidehpour, M.: Generation expansion planning considering the rehabilitation of aging generating units. IEEE Trans. Smart Grid 11(4), 3384–3393 (2020)

    Article  Google Scholar 

  8. “Asset management,” Wikipedia, 30-Sep-2020. [Online]. https://en.wikipedia.org/wiki/Asset_management. Accessed 24 Oct 2020

  9. Tjernberg, L.B.: Infrastructure Asset Management with Power System Applications. CRC Press (2018)

    Book  Google Scholar 

  10. Dehghanian, P., Fotuhi-Firuzabad, M., Aminifar, F., Billinton, R.: A comprehensive scheme for reliability centered maintenance in power distribution systems—Part I: methodology. IEEE Trans. Power Delivery 28(2), 761–770 (2013)

    Article  Google Scholar 

  11. Dehghanian, P., Fotuhi-Firuzabad, M., Aminifar, F., Billinton, R.: A comprehensive scheme for reliability-centered maintenance in power distribution systems—part II: numerical analysis. IEEE Trans. Power Deliv. 28(2), 771–778 (2013)

    Article  Google Scholar 

  12. Farhoumandi, M., Zhou, Q., Shahidehpour, M.: A review of machine learning applications in IoT-integrated modern power systems. Electric. J. 34(1), 106879 (2021)

    Article  Google Scholar 

  13. Alimi, O.A., Ouahada, K., Abu-Mahfouz, A.M.: A review of machine learning approaches to power system security and stability. IEEE Access 8, 113512–113531 (2020)

    Article  Google Scholar 

  14. Mandic, D.P., Kanna, S., Xia, Y., Moniri, A., Junyent-Ferre, A., Constantinides, A.G.: A data analytics perspective of power grid analysis-part 1: the Clarke and related transforms [Lecture Notes]. IEEE Signal Process. Mag. 36(2), 110–116 (2019)

    Article  Google Scholar 

  15. Ma, Z., et al.: The role of data analysis in the development of intelligent energy networks. IEEE Netw. 31(5), 88–95 (2017)

    Article  Google Scholar 

  16. Chawla, G., Sachdev, M.S., Ramakrishna, G.: Artificial neural network applications for power system protection. In: Canadian Conference on Electrical and Computer Engineering, 2005., Saskatoon, SK, Canada, 2005, pp. 1954–1957 (2005)

  17. Hasan, A.N., Eboule, P S.P., Twala, B.: The use of machine learning techniques to classify power transmission line fault types and locations. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), Brasov, Romania, 2017, pp. 221–226.

  18. Elaidi, H., Elhaddar, Y., Benabbou, Z., Abbar, H.: An idea of a clustering algorithm using support vector machines based on binary decision tree. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 2018, pp. 1–5 (2015)

  19. Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. IEEE Trans. Neural Netw. 9(5), 1054–1054 (1998)

    Article  Google Scholar 

  20. Glavic, M., Fonteneau, R., Ernst, D.: Reinforcement learning for electric power system decision and control: past considerations and perspectives. In: IFAC-PapersOnLine, vol. 50, no. 1, (2017)

  21. Zhang, Z., Zhang, D., Qiu, R.C.: Deep reinforcement learning for power system applications: an overview. CSEE J. Power Energy Syst. 6(1), 213–225 (2020)

    Google Scholar 

  22. Couso, I., Borgelt, C., Hullermeier, E., Kruse, R.: Fuzzy sets in data analysis: from statistical foundations to machine learning. IEEE Comput. Intell. Mag. 14(1), 31–44 (2019)

    Article  Google Scholar 

  23. Shaker, H., Fotuhi-Firuzabad, M., Aminifar, F.: Fuzzy dynamic thermal rating of transmission lines. IEEE Trans. Power Delivery 27(4), 1885–1892 (2012)

    Article  Google Scholar 

  24. Hagras, H.: Toward human-understandable, explainable AI. Computer 51(9), 28–36 (2018)

    Article  Google Scholar 

  25. Madani, V., et al.: Distribution automation strategies challenges and opportunities in a changing landscape. IEEE Trans. Smart Grid 6(4), 2157–2165 (2015)

    Article  Google Scholar 

  26. Das, R., et al.: Distribution automation strategies: evolution of technologies and the business case. IEEE Trans. Smart Grid 6(4), 2166–2175 (2015)

    Article  Google Scholar 

  27. Raza, M.Q., Khosravi, A.: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sust. Energy Rev. 50, 1352–1372 (2015)

    Article  Google Scholar 

  28. Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841–851 (2019)

    Article  Google Scholar 

  29. Dalal, G., Gilboa, E., Mannor, S., Wehenkel, L.: Chance-constrained outage scheduling using a machine learning proxy. IEEE Trans. Power Syst. 34(4), 2528–2540 (2019)

    Article  Google Scholar 

  30. Das, U.K., et al.: Forecasting of photovoltaic power generation and model optimization: a review. Renew. Sust. Energy Rev. 81, 912–928 (2018)

    Article  Google Scholar 

  31. Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sust. Energy Rev. 74, 902–924 (2017)

    Article  Google Scholar 

  32. Du, Y., Li, F.: Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning. IEEE Trans. Smart Grid 11(2), 1066–1076 (2020)

    Article  Google Scholar 

  33. Lago, J., De Ridder, F., De Schutter, B.: Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 221, 386–405 (2018)

    Article  Google Scholar 

  34. Ye, Y., Qiu, D., Sun, M., Papadaskalopoulos, D., Strbac, G.: Deep reinforcement learning for strategic bidding in electricity markets. IEEE Trans. Smart Grid 11(2), 1343–1355 (2020)

    Article  Google Scholar 

  35. Wu, L., Gao, J., Venayagamoorthy, G. K.. Harley, R.G.: On artificial intelligence approaches for contingency analysis in power system security assessment. In: 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, 2018, pp. 1–5 (2018)

  36. Zhang, D., Xu, Z., Li, C., Yang, R., Shahidehpour, M., Wu, Q., Yan, M.: Economic and sustainability promises of wind energy considering the impacts of climate change and vulnerabilities to extreme conditions. Electr. J. 32, 7–12 (2019)

    Article  Google Scholar 

  37. Huang, Q., Huang, R., Hao, W., Tan, J., Fan, R., Huang, Z.: Adaptive power system emergency control using deep reinforcement learning. IEEE Trans. Smart Grid 11(2), 1171–1182 (2020)

    Article  Google Scholar 

  38. Power Plant and Transmission System Protection Coordination, NERC System Protect ion and Control Subcommittee, Rev. 1, Jul. 2010. http://www.nerc.com/docs. Accessed 1 Nov 2020

  39. Abedini, M., Davarpanah, M., Sanaye-Pasand, M.: Appropriate grounding system for grid-connected small-scale synchronous generators. IEEE Trans. Ind. Appl. 51(6), 5390–5397 (2015)

    Article  Google Scholar 

  40. Reimert, D.: Protective Relaying for Power Generation Systems, pp. 117–163. CRC Press (2005)

    Book  Google Scholar 

  41. Taalab, A., Darwish, H.A., Kawady, T.A.: ANN-based novel fault detector for generator windings protection. IEEE Trans. Power Deliv. 14(3), 824–830 (1999)

    Article  Google Scholar 

  42. Megahed, A.I., Malik, O.P.: An artificial neural network based digital differential protection scheme for synchronous generator stator winding protection. IEEE Trans. Power Deliv. 14(1), 86–93 (1999)

    Article  Google Scholar 

  43. Segatto, E.C., Coury, D.V.: A differential relay for power transformers using intelligent tools. IEEE Trans. Power Syst. 21(3), 1154–1162 (2006)

    Article  Google Scholar 

  44. Tripathy, M., Maheshwari, R.P., Verma, H.K.: Power transformer differential protection based on optimal probabilistic neural network. IEEE Trans. on Power Deliv. 25(1), 102–112 (2010)

    Article  Google Scholar 

  45. Darwish, H.A.H.A., Taalab, A.-M.I.A.I., Kawady, T.A.T.A.: Development and implementation of an ANN-based fault diagnosis scheme for generator winding protection. IEEE Trans. Power Deliv. 16(2), 208–214 (2001)

    Article  Google Scholar 

  46. Bhalja, B., Maheshwari, R.P., Nema, S., Verma, H.K.: Neuro-Fuzzy-Based scheme for stator winding protection of synchronous generator. Electr. Power Compon. Syst. 37(5), 560–576 (2009)

    Article  Google Scholar 

  47. IEEE Guide for AC Generator Protect ion, IEEE Standard C37.102- 2006, Nov. (2006)

  48. Abedini, M., Sanaye-Pasand, M., Davarpanah, M.: Flux linkage estimation based loss of excitation relay for synchronous generator. IET Gener. Transm. Distrib 11, 280–288 (2017)

    Article  Google Scholar 

  49. Abedini, M., Sanaye-Pasand, M., Davarpanah, M., Iravani, R.: A loss-of-field detection relay based on rotor signals estimation. IEEE Trans. Power Deliv 33(2), 779–788 (2018)

    Article  Google Scholar 

  50. Abedini, M., Sanaye-Pasand, M., Davarpanah, M.: An analytical approach to detect generator loss of excitation based on internal voltage calculation. IEEE Trans. Power Deliv 32(5), 2329–2338 (2017)

    Article  Google Scholar 

  51. Amraee, T.: Loss-of-field detection in synchronous generators using decision tree technique. IET Gener. Transm. Distrib. 7(9), 943–954 (2013)

    Article  Google Scholar 

  52. De Morais, A.P., Cardoso, G., Mariotto, L.: An innovative loss-of-excitation protection based on the fuzzy inference mechanism. IEEE Trans. Power Deliv. 25(4), 2197–2204 (2010)

    Article  Google Scholar 

  53. Pajuelo, E., Gokaraju, R., Sachdev, M.S.: Identification of generator loss-of-excitation from power-swing conditions using a fast pattern classification method. IET Gener. Transm. Distrib. 7(1), 24–36 (2013)

    Article  Google Scholar 

  54. Rasoulpour, M., Amraee, T., Khaki Sedigh, A.: A relay logic for total and partial loss of excitation protection in synchronous generators. IEEE Trans. Power Deliv. 35(3), 1 (2019)

    Google Scholar 

  55. Abedini, M., Davarpanah, M., Sanaye-Pasand, M., Hashemi, S.M., Iravani, R.: Generator out-of-step prediction based on faster-than-real-time analysis: concepts and applications. IEEE Trans. Power Syst. 33(4), 4563–4573 (2017)

    Article  Google Scholar 

  56. Abedini, M., Sanaye-Pasand, M., Davarpanah, M., Lesani, H., Shahidehpour, M.: A predictive auto-reclosure approach to enhance Transient stability of grid-connected DGs. IET Gener. Transm. Distrib. 19(9), 943–954 (2019)

    Google Scholar 

  57. Rebizant, W.: Fuzzy logic application to out-of-step protection of generators. In: Proc. IEEE Power Eng. Soc. Summer Meeting, 2001, vol. 2, pp. 927–932 (2001)

  58. Phadke, A.G., Edris, A., Benton, J., Gaudi, M., Michel, G.: An adaptive out-of-step relay for power system protection. IEEE Trans. Power Deliv. 12(1), 61–71 (1997)

    Article  Google Scholar 

  59. Amraee, T., Ranjbar, S.: Transient instability prediction using decision tree technique. IEEE Trans. Power Syst. 25(3), 3028–3037 (2013)

    Article  Google Scholar 

  60. El-Arabaty, A.M., Talaat, H.A., Mansour, M.M., et al.: Out-of-step detection based on pattern recognition. Int. J. Electr. Power Energy Syst. 16(4), 269–275 (1994)

    Article  Google Scholar 

  61. Wang, L., Girgis, A.A.: A new method for power system transient instability detection. IEEE Trans. on Power Deliv. 12(3), 1082–1089 (1997)

    Article  Google Scholar 

  62. Abedini, M., Sanaye-Pasand, M., Azizi, S.: An adaptive load shedding scheme to preserve the power system stability following large disturbances. IET Gener. Transm. Distrib. 8(12), 2124–2133 (2019)

    Article  Google Scholar 

  63. Bruzzese, C.: Diagnosis of eccentric rotor in synchronous machines by analysis of split-phase currents—part II: experimental analysis. IEEE Trans. Ind. Electron. 61(8), 4206–4216 (2014)

    Article  Google Scholar 

  64. IEEE-SA Standards Board: IEEE Std 493–2007-Design of Reliable Industrial and Commercial Power Systems. IEEE Ind. Electron Society, New York (2007)

    Google Scholar 

  65. Zhiwei, G., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Trans. oInd. Electron. 62(6), 3757–3767 (2015)

    Article  Google Scholar 

  66. Ding, S.: Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer-Verlag, Berlin (2008)

    Google Scholar 

  67. Picher, P., et al.: Advances in the interpretation of transformer Frequency Response Analysis (FRA). In: Cigre Technical brochure, Brochure 812, (2020)

  68. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R.X.: Deep learning and its applications to machine health monitoring. Mech. Syst. Signal Process. 115, 213–237 (2019)

    Article  Google Scholar 

  69. Elashmawi, W.H., Kotp, N.A., El. Tawel, G.: Neural network monitoring model for industrial gas turbine. Asian J. Appl. Sci., 5(3), (2010)

  70. Tenbohlen, S., Coenen, S., Djamali, M., Müller, A., Samimi, M.H., Siegel, M.: Diagnostic measurements for power transformers. Energies 9(5), 347 (2016)

    Article  Google Scholar 

  71. Samimi, M.H., Tenbohlen, S., Akmal, A.A.S., Mohseni, H.: Effect of different connection schemes, terminating resistors and measurement impedances on the sensitivity of the FRA method. IEEE Trans. Power Deliv. 32(4), 1713–1720 (2016)

    Article  Google Scholar 

  72. Samimi, M.H., Ilkhechi, H.D.: Survey of different sensors employed for the power transformer monitoring. IET Sci. Meas. Technol. 14(1), 1–8 (2019)

    Article  Google Scholar 

  73. Sun, L., Ma, Z., Shang, Y., Liu, Y., Yuan, H., Wu, G.: Research on multi-attribute decision-making in condition evaluation for power transformer using fuzzy AHP and modified weighted averaging combination. IET Gener. Transm. Distrib. 10(15), 3855–3864 (2016)

    Article  Google Scholar 

  74. Samimi, M.H., Tenbohlen, S., Akmal, A.A.S., Mohseni, H.: Evaluation of numerical indices for the assessment of transformer frequency response. IET Gener. Transm. Distrib. 11(1), 218–227 (2017)

    Article  Google Scholar 

  75. Samimi, M.H., Tenbohlen, S.: FRA interpretation using numerical indices: State-of-the-art. Int. J. Electr. Power Energy Syst. 89, 115–125 (2017)

    Article  Google Scholar 

  76. Miranda, V., Castro, A.R.G.: Improving the IEC table for transformer failure diagnosis with knowledge extraction from neural networks. IEEE Trans. Power Delivery 20(4), 2509–2516 (2005)

    Article  Google Scholar 

  77. Dai, J., Song, H., Sheng, G., Jiang, X.: Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network. IEEE Trans. Dielectr. Electr. Insul. 24(5), 2828–2835 (2017)

    Article  Google Scholar 

  78. Aizpurua, J.I., et al.: Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing. IEEE Trans. Dielectr. Electr. Insul. 25(2), 494–506 (2018)

    Article  Google Scholar 

  79. Mahmoudi, N., Samimi, M.H., Mohseni, H.: Experiences with transformer diagnosis by DGA: case studies. IET Gener. Transm. Distrib. 13(23), 5431–5439 (2019)

    Article  Google Scholar 

  80. Zhou, L., Hu, T.: Multifactorial condition assessment for power transformers. IET Gener. Transm. Distrib. 14(9), 1607–1615 (2020)

    Article  Google Scholar 

  81. Samimi, M.H., Tenbohlen, S.: Using the temperature dependency of the FRA to evaluate the pressure of the transformer press ring. IEEE Trans. Power Deliv. 33(4), 2050–2052 (2017)

    Article  Google Scholar 

  82. Samimi, M.H., Tenbohlen, S., Akmal, A.A.S., Mohseni, H.: Dismissing uncertainties in the FRA interpretation. IEEE Trans. Power Deliv. 33(4), 2041–2043 (2016)

    Article  Google Scholar 

  83. Ghanizadeh, A.J., Gharehpetian, G.B.: ANN and cross-correlation based features for discrimination between electrical and mechanical defects and their localization in transformer winding. IEEE Trans. Dielectr. Electr. Insul. 21(5), 2374–2382 (2014)

    Article  Google Scholar 

  84. Tang, W.H., Wu, Q.H.: Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence. Springer Science & Business Media, London (2011)

    Book  Google Scholar 

  85. Djamali, M., Tenbohlen, S.: Hundred years of experience in the dynamic thermal modelling of power transformers. IET Gener. Transm. Distrib. 11(11), 2731–2739 (2017)

    Article  Google Scholar 

  86. Forouhari, S., Abu-Siada, A.: Application of adaptive neuro fuzzy inference system to support power transformer life estimation and asset management decision. IEEE Trans. Dielectr. Electr. Insul. 25(3), 845–852 (2018)

    Article  Google Scholar 

  87. Ranga, C., Chandel, A.K., Chandel, R.: Condition assessment of power transformers based on multi-attributes using fuzzy logic. IET Sci. Meas. Technol. 11(8), 983–990 (2017)

    Article  Google Scholar 

  88. Li, S., Ma, H., Saha, T., Wu, G.: Bayesian information fusion for probabilistic health index of power transformer. IET Gener. Transm. Distrib. 12(2), 279–287 (2017)

    Article  Google Scholar 

  89. Ranga, C., Chandel, A.K., Chandel, R.: Expert system for condition monitoring of power transformer using fuzzy logic. J. Renew. Sustain. Energy 9(4), 044901 (2017)

    Article  Google Scholar 

  90. Bakar, N.A., Abu-Siada, A.: Fuzzy logic approach for transformer remnant life prediction and asset management decision. IEEE Trans. Dielectr. Electr. Insul. 23(5), 3199–3208 (2016)

    Article  Google Scholar 

  91. Foros, J., Istad, M.: Health index, risk and remaining lifetime estimation of power transformers. In: IEEE Transactions on Power Delivery (2020)

  92. Ghunem, R.A., Assaleh, K., El-Hag, A.H.: Artificial neural networks with stepwise regression for predicting transformer oil furan content. IEEE Trans. Dielectr. Electr. Insul. 19(2), 414–420 (2012)

    Article  Google Scholar 

  93. Furanic Compounds for Diagnosis, CIGRE Technical Brochure 494 (2012)

  94. Höhlein, I., Kachler, J.: Aging of cellulose at transformer service temperatures. Part 2. Influence of water content and temperature on degree of polymerization and formation of furanic compounds in Free-Breathing Systems. IEEE Electr. Insul. Mag. 21(5), 20–23 (2005)

    Article  Google Scholar 

  95. Bhalja, B., Maheshwari, R.P.: An adaptive distance relaying scheme using radial basis function neural network. Electr. Power Compon. Syst. 35(3), 245–259 (2007)

    Article  Google Scholar 

  96. Bhalja, B., Maheshwari, R.P.: Trends in adaptive distance protection of multiterminal and double-circuit lines. Electr. Power Compon. Syst. 34(6), 603–617 (2006)

    Article  Google Scholar 

  97. Jafarian, P., Sanaye-Pasand, M.: A traveling-wave-based protection technique using Wavelet/PCA analysis. IEEE Trans. Power Del. 25(2), 588–599 (2010)

    Article  Google Scholar 

  98. Raza, A., Benrabah, A., Alquthami, T., Akmal, M.: A review of fault diagnosing methods in power transmission systems. Appl. Sci. 10(4), 1312 (2020)

    Article  Google Scholar 

  99. Parikh, U.B., Das, B., Maheshwari, R.P.: Combined wavelet-SVM technique for fault zone detection in a series compensated transmission line. IEEE Trans. Power Delivery 23(4), 1789–1794 (2008)

    Article  Google Scholar 

  100. Zin, A.A.M., et al.: New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke’s transformation. Neurocomputing 168, 983–993 (2015)

    Article  Google Scholar 

  101. Moravej, Z., Pazoki, M., Khederzadeh, M.: New pattern recognition method for fault analysis in transmission line with UPFC. IEEE Trans. Power Deliv. 30(3), 1231–1242 (2014)

    Article  Google Scholar 

  102. Godse, R., Bhat, S.: Mathematical morphology-based feature-extraction technique for detection and classification of faults on power transmission line. IEEE Trans. Power Deliv. 8, 347–356 (2020)

    Google Scholar 

  103. Abdullah, A.: Ultra fast transmission line fault detection using a DWT-based ANN. IEEE Trans. Ind. Appl. 54(2), 1182–1193 (2017)

    Article  Google Scholar 

  104. Pradhan, A.K., Routray, A., Pati, S., Pradhan, D.K.: Wavelet fuzzy combined approach for fault classification of a series-compensated transmission line. IEEE Trans. Power Deliv. 19(4), 1612–1618 (2004)

    Article  Google Scholar 

  105. Reddy, M.J., Mohanta, D.K.: A wavelet-fuzzy combined approach for classification and location of transmission line faults. Int. J. Electr. Power Energy Syst. 29(9), 669–678 (2007)

    Article  Google Scholar 

  106. Samantaray, S.R.: Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line. IET Gener. Transm. Distrib. 3(5), 425–436 (2009)

    Article  Google Scholar 

  107. Jafarian, P., Sanaye-Pasand, M.: High-frequency transients-based protection of multiterminal transmission lines using the SVM technique. IEEE Trans. Power Deliv. 28(1), 188–196 (2012)

    Article  Google Scholar 

  108. Liacco, T.E.D.: The adaptive reliability control system. IEEE Trans. Power Appar. Syst. 5, 517–531 (1967)

    Article  Google Scholar 

  109. Li, K.K., Lai, L.L., David, A.K.: Stand alone intelligent digital distance relay. IEEE Trans. Power Syst. 15(1), 137–142 (2000)

    Article  Google Scholar 

  110. Dubey, R., Samantaray, S.R., Panigrahi, B.K.: An extreme learning machine based fast and accurate adaptive distance relaying scheme. Int. J. Electr. Power Energy Syst. 73, 1002–1014 (2015)

    Article  Google Scholar 

  111. Dubey, R., et al.: Extreme learning machine based adaptive distance relaying scheme for static synchronous series compensator based transmission lines. Electr. Power Compon. Syst. 44(2), 219–232 (2016)

    Article  Google Scholar 

  112. Mohajeri, A., Seyedi, H., Sabahi, M.: Optimal setting of distance relays quadrilateral characteristic considering the uncertain effective parameters. Int. J. Electr. Power Energy Syst. 73, 1051–1059 (2015)

    Article  Google Scholar 

  113. Jongepier, A.G., Van Der Sluis, L.: Adaptive distance protection of double-circuit lines using artificial neural networks. IEEE Trans. Power Delivery 12(1), 97–105 (1997)

    Article  Google Scholar 

  114. Dash, P.K., Pradhan, A.K., Panda, G.: A novel fuzzy neural network based distance relaying scheme. IEEE Trans. Power Deliv. 15(3), 902–907 (2000)

    Article  Google Scholar 

  115. Kawady, T.A., Sowilam, G.M., Shalwala, R.: Improved distance relaying for double-circuit lines using adaptive neuro-fuzzy inference system. Arab. J. Sci. Eng. 45(3), 1969–1984 (2020)

    Article  Google Scholar 

  116. Bernabeu, E.E., Thorp, J.S., Centeno, V.: Methodology for a security/dependability adaptive protection scheme based on data mining. IEEE Trans. Power Deliv. 27(1), 104–111 (2012)

    Article  Google Scholar 

  117. Sanaye-Pasand, M., Jafarian, P.: An adaptive decision logic to enhance distance protection of transmission lines. IEEE Trans. Power Deliv. 26(4), 2134–2144 (2011)

    Article  Google Scholar 

  118. Joshi, S.K., Sahoo, B., Samantaray, S.R.: A new approach to supervise vulnerable third zone relay operation for power transmission system. In: 8th International Conference on Power Systems (ICPS), Jaipur, India, (2019)

  119. Das, S., Dubey, R., Panigrahi, B.K., Samantaray, S.R.: Secured zone-3 protection during power swing and voltage instability: an online approach. IET Gener. Transm. Distrib. 11(2), 437–446 (2017)

    Article  Google Scholar 

  120. Kundu, P., Pradhan, A.K.: Wide area measurement based protection support during power swing. Int. J. Electr. Power Energy Syst. 63, 546–554 (2014)

    Article  Google Scholar 

  121. Niyas, M., Sunitha, K.: Identification and classification of fault during power swing using decision tree approach. In: IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Kollam, India, Aug. 2017.

  122. Chaudhari, N., Hinge, T., Dambhare, S.: Blocking of distance relays zone3 under load encroachment conditions using decision tree technique. In: 7th Power India International Conference (PIICON), Bikaner, India, (2016)

  123. Yu, D.C., et al.: Correction of current transformer distorted secondary currents due to saturation using artificial neural networks. IEEE Trans. Power Delivery 16(2), 189–194 (2001)

    Article  Google Scholar 

  124. Khorashadi-Zadeh, H., Sanaye-Pasand, M.: Correction of saturated current transformers secondary current using ANNs. IEEE Trans. Power Deliv. 21(1), 73–79 (2006)

    Article  Google Scholar 

  125. Erenturk, K.: ANFIS-based compensation algorithm for current-transformer saturation effects. IEEE Trans. Power Delivery 24(1), 195–201 (2009)

    Article  Google Scholar 

  126. Khorashadi-Zadeh, H.: Correction of capacitive voltage transformer distorted secondary voltages using artificial neural networks. In: Neural Network Applications in Electrical Engineering (NEUREL), pp. 131–134, Belgrade, Serbia, (2004)

  127. Ahmadi, S., Sanaye-Pasand, M., Jafarian, P., Mehrjerdi, H.: Adaptive single-phase auto-reclosing approach for shunt compensated transmission lines. In: IEEE Transactions on Power Delivery (2020). https://doi.org/10.1109/TPWRD.2020.3007392

  128. IEEE Guide for Engineering, Implementation, and Management of System Integrity Protection Schemes. In: IEEE Std C37.250–2020. pp. 1–71 (2020)

  129. Madani, V., et al.: IEEE PSRC Report on global industry experiences with system integrity protection schemes (SIPS). IEEE Trans. Power Deliv. 25(4), 2143–2155 (2010)

    Article  Google Scholar 

  130. Cai, D., Wall, P., Osborne, M., Terzija, V.: Roadmap for the deployment of WAMPAC in the future GB power system. IET Gener. Transm. Distrib. 10(7), 1553–1562 (2016)

    Article  Google Scholar 

  131. Islam, S.R., Sutanto, D., Muttaqi, K.M.: Coordinated decentralized emergency voltage and reactive power control to prevent long-term voltage instability in a power system. IEEE Trans. Power Syst. 30(5), 2591–2603 (2015)

    Article  Google Scholar 

  132. Islam, S.R., Sutanto, D., Muttaqi, K.M.: A distributed multi-agent based emergency control approach following catastrophic disturbances in interconnected power systems. IEEE Trans. Power Syst. 31(4), 2764–2775 (2016)

    Article  Google Scholar 

  133. Adewole, A.C., Tzoneva, R., Apostolov, A.: Adaptive under-voltage load shedding scheme for large interconnected smart grids based on wide area synchrophasor measurements. IET Gener. Transm. Distrib. 10(8), 1957–1968 (2016)

    Article  Google Scholar 

  134. Ashrafi, A., Shahrtash, S.M.: Dynamic wide area voltage control strategy based on organized multi-agent system. IEEE Trans. Power Syst. 29(6), 2590–2601 (2014)

    Article  Google Scholar 

  135. Ghahremani, E., Heniche-Oussedik, A., Perron, M., Racine, M., Landry, S., Akremi, H.: A detailed presentation of an innovative local and wide-area special protection scheme to avoid voltage collapse: from proof of concept to grid implementation. IEEE Trans. Smart Grid 10(5), 5196–5211 (2019)

    Article  Google Scholar 

  136. Wang, P., Govindarasu, M.: Multi-agent based attack-resilient system integrity protection for smart grid. IEEE Trans. Smart Grid 11(4), 3447–3456 (2020)

    Article  Google Scholar 

  137. Padrón, S., Hernández, M., Falcón, A.: Reducing under-frequency load shedding in isolated power systems using neural networks. Gran Canaria: a case study. IEEE Trans. Power Syst. 31(1), 63–71 (2016)

    Article  Google Scholar 

  138. Sigrist, L., Rouco, L., Echavarren, F.M.: A review of the state of the art of UFLS schemes for isolated power systems. Int. J. Electr. Power Energy Syst. 99, 525–539 (2018)

    Article  Google Scholar 

  139. Hooshmand, R., Moazzami, M.: Optimal design of adaptive under frequency load shedding using artificial neural networks in isolated power system. Int. J. Electr. Power Energy Syst. 42(1), 220–228 (2012)

    Article  Google Scholar 

  140. Santos, A.Q., Monaro, R.M., Coury, D.V., Oleskovicz, M.: A new real-time multi-agent system for under frequency load shedding in a smart grid context. Electr. Power Syst. Res. 174, 105851 (2019)

    Article  Google Scholar 

  141. Gu, W., et al.: Adaptive decentralized under-frequency load shedding for islanded smart distribution networks. IEEE Trans. Sustain. Energy 5(3), 886–895 (2014)

    Article  Google Scholar 

  142. Laghari, J.A., Mokhlis, H., Bakar, A.H.A., Mohamad, H.: Application of computational intelligence techniques for load shedding in power systems: A review. Energy Convers. Manag. 75, 130–140 (2013)

    Article  Google Scholar 

  143. Jung, J., Liu, C.-C., Tanimoto, S.L., Vittal, V.: Adaptation in load shedding under vulnerable operating conditions. IEEE Trans. Power Syst. 17(4), 1199–1205 (2002)

    Article  Google Scholar 

  144. Diao, R., Vittal, V., Sun, K., Kolluri, S., Mandal, S., Galvan, F.: Decision tree assisted controlled islanding for preventing cascading events. In: 2009 IEEE/PES Power Systems Conference and Exposition, pp. 1–8 (2009)

  145. Sun, R., Wu, Z., Centeno, V.A.: Power system islanding detection & identification using topology approach and decision tree. In: 2011 IEEE Power and Energy Society General Meeting, pp. 1–6 (2011)

  146. Kamali, S., Amraee, T., Bathaee, S.M.T.: Prediction of unplanned islanding using an energy based strategy. IET Gener. Transm. Distrib. 10(1), 183–191 (2016)

    Article  Google Scholar 

  147. Raak, F., Susuki, Y., Hikihara, T.: Data-driven partitioning of power networks via Koopman mode analysis. IEEE Trans. Power Syst. 31(4), 2799–2808 (2016)

    Article  Google Scholar 

  148. Liu, S., et al.: Robust system separation strategy considering online wide-area coherency identification and uncertainties of renewable energy sources. IEEE Trans. Power Syst. 35, 1 (2020)

    Article  Google Scholar 

  149. Sun, R., Centeno, V.A.: Wide Area System Islanding Contingency Detecting and Warning Scheme. IEEE Trans. Power Syst. 29(6), 2581–2589 (2014)

    Article  Google Scholar 

  150. Zhang, S., Zhang, Y.: A novel out-of-step splitting protection based on the wide area information. IEEE Trans. Smart Grid 8(1), 41–51 (2017)

    Google Scholar 

  151. Farantatos, E., Huang, R., Cokkinides, G.J., Meliopoulos, A.P.: A predictive generator out-of-step protection and transient stability monitoring scheme enabled by a distributed dynamic state estimator. IEEE Trans. Power Deliv. 31(4), 1826–1835 (2016)

    Article  Google Scholar 

  152. Hashiesh, F., Mostafa, H.E., Khatib, A., Helal, I., Mansour, M.M.: An intelligent wide area synchrophasor based system for predicting and mitigating transient instabilities. IEEE Trans. Smart Grid 3(2), 645–652 (2012)

    Article  Google Scholar 

  153. Zhu, L., Hill, D.J., Lu, C.: Hierarchical deep learning machine for power system online transient stability prediction. IEEE Trans. Power Syst. 35(3), 2399–2411 (2020)

    Article  Google Scholar 

  154. Wang, B., Fang, B., Wang, Y., Liu, H., Liu, Y.: Power system transient stability assessment based on big data and the core vector machine. IEEE Trans. Smart Grid 7(5), 2561–2570 (2016)

    Article  Google Scholar 

  155. Han, T., Chen, Y., Ma, J., Zhao, Y., Chi, Y.: Surrogate modeling-based multi-objective dynamic VAR planning considering short-term voltage stability and transient stability. IEEE Trans. Power Syst. 33(1), 622–633 (2018)

    Article  Google Scholar 

  156. Mosavi, A.B., Amiri, A., Hosseini, H.: A learning framework for size and type independent transient stability prediction of power system using twin convolutional support vector machine. IEEE Access 6, 69937–69947 (2018)

    Article  Google Scholar 

  157. Gomez, F.R., Rajapakse, A.D., Annakkage, U.D., Fernando, I.T.: Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements. IEEE Trans. Power Syst. 26(3), 1474–1483 (2011)

    Article  Google Scholar 

  158. Jiang, K., Singh, C.: New models and concepts for power system reliability evaluation including protection system failures. IEEE Trans. Power Syst. 26(4), 1845–1855 (2011). https://doi.org/10.1109/TPWRS.2011.2156820

    Article  Google Scholar 

  159. Eliassi, M., Seifi, H., Haghifam, M.-R.: Incorporation of protection system failures into bulk power system reliability assessment by Bayesian networks. IET Gener. Transm. Distrib. 9(11), 1226–1234 (2015)

    Article  Google Scholar 

  160. Ntalampiras, S.: Fault diagnosis for smart grids in pragmatic conditions. IEEE Trans. Smart Grid 9(3), 1964–1971 (2018). https://doi.org/10.1109/TSG.2016.2604120

    Article  Google Scholar 

  161. Ruiwen, H., Jianhua, D., Lai, L.L.: Reliability evaluation of communication-constrained protection systems using stochastic-flow network models. IEEE Trans. Smart Grid 9(3), 2371–2381 (2018). https://doi.org/10.1109/TSG.2017.2727227

    Article  Google Scholar 

  162. Kamps, K. et al.: Modelling and Risk Assessment of Special Protection Schemes in Transmission Systems. In: 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Liege, Belgium, pp. 1–6 (2020). https://doi.org/10.1109/PMAPS47429.2020.9183639.

  163. Jia, Y., Ying, L., Wang, D., Zhang, J.: Defect prediction of relay protection systems based on LSSVM-BNDT. IEEE Trans. Industr. Inf. 17(1), 710–719 (2021). https://doi.org/10.1109/TII.2020.2990962

    Article  Google Scholar 

  164. Rezaei, N., Uddin, M.N., Amin, I.K., Othman, M.L., Marsadek, M.B., Hasan, M.M.: A novel hybrid machine learning classifier-based digital differential protection scheme for intertie zone of large-scale centralized DFIG-based wind farms. IEEE Trans. Ind. Appl. 56(4), 3453–3465 (2020). https://doi.org/10.1109/TIA.2020.2990584

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL, USA

    Farrokh Aminifar & Mohammad Shahidehpour

  2. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Farrokh Aminifar, Moein Abedini & Mohammad Hamed Samimi

  3. Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Turaj Amraee

  4. Iran Grid Management Co., Tehran, Iran

    Peyman Jafarian

Authors
  1. Farrokh Aminifar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moein Abedini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Turaj Amraee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peyman Jafarian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Hamed Samimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohammad Shahidehpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farrokh Aminifar.

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

Aminifar, F., Abedini, M., Amraee, T. et al. A review of power system protection and asset management with machine learning techniques. Energy Syst 13, 855–892 (2022). https://doi.org/10.1007/s12667-021-00448-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12667-021-00448-6

Keywords

Search

Navigation