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A two-stage strategy for generator rotor angle stability prediction using the adaptive neuro-fuzzy inference system

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Abstract

Generator rotor angle oscillations can be caused by sudden changes in its mechanical power input or its electrical power output. When dampening or synchronizing torque is inadequate, rotor angle instability occurs, resulting in an increase in rotor angle, loss of synchronism, or oscillatory swings of the rotor angle with increasing amplitude. To this end, in this work, we offer a novel two-stage approach for predicting rotor angle stability following a large disturbance. The process begins with the creation of a database of dynamic simulation scenarios. This collection contains a wide set of stable and unstable rotor angle trajectories derived from different fault simulations. Then, the fuzzy c-means (FCM) clustering algorithm is utilized to put the sampled data of rotor angles with the highest degree of similarity in the same cluster. Angle sets generated by FCM are used to train the adaptive neuro-fuzzy inference system (ANFIS). Finally, the trained ANFIS is used to predict the future rotor angle stability situation of generators. In addition, a stability index is suggested in this article, which will assist ANFIS in more accurately predicting the stability condition. The efficiency of the proposed method is tested on the IEEE 39-bus system. The obtained results from simulations confirm that the proposed strategy can correctly predict the rotor angle instability or stability situation of generators in a few cycles after fault clearance.

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References

  1. Das S, Panigrahi BK (2019) Prediction and control of transient stability using system integrity protection schemes. IET Gener Transm Distrib 13(8):1247–1254

    Article  Google Scholar 

  2. Tziouvaras DA, Hou D (2004) Out-of-step protection fundamentals and advancements. In: 57th Annual conference for protective relay engineers, 2004, College Station, TX, USA. IEEE, pp 282–307

  3. Amjady N, Majedi SF (2007) Transient stability prediction by a hybrid intelligent system. IEEE Trans Power Syst 22(3):1275–1283

    Article  Google Scholar 

  4. Liu C-W, Thorp JS (2000) New methods for computing power system dynamic response for real-time transient stability prediction. IEEE Trans Circuits Syst I Fundam Theory Appl 47(3):324–337

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Zhang C, Li Y, Yu Z, Tian F (2016) A weighted random forest approach to improve predictive performance for power system transient stability assessment. In: 2016 IEEE PES Asia-Pacific power and energy engineering conference (APPEEC). IEEE, pp 1259–1263

  7. Li Y, Yang Z (2017) Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data. IEEE Access 5:23092–23101

    Article  Google Scholar 

  8. James J, Lam AY, Hill DJ, Li VO (2017) Delay aware intelligent transient stability assessment system. IEEE Access 5:17230–17239

    Article  Google Scholar 

  9. Behdadnia T, Yaslan Y, Genc I (2021) A new method of decision tree based transient stability assessment using hybrid simulation for real-time PMU measurements. IET Gener Transm Distrib. https://doi.org/10.1049/gtd2.12051

    Article  Google Scholar 

  10. Li X, Yang Z, Guo P, Cheng J (2021) An intelligent transient stability assessment framework with continual learning ability. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2021.3064052

    Article  Google Scholar 

  11. Dietrich K, Latorre JM, Olmos L, Ramos A (2011) Demand response in an isolated system with high wind integration. IEEE Trans Power Syst 27(1):20–29

    Article  Google Scholar 

  12. Anzai Y (2012) Pattern recognition and machine learning. Elsevier, Amsterdam

    MATH  Google Scholar 

  13. Alpaydin E (2020) Introduction to machine learning. MIT press, Cambridge

    MATH  Google Scholar 

  14. Sobbouhi AR, Vahedi A (2021) Transient stability prediction of power system; a review on methods, classification and considerations. Electr Power Syst Res 190:106853

    Article  Google Scholar 

  15. Gupta A, Gurrala G, Sastry P (2018) An online power system stability monitoring system using convolutional neural networks. IEEE Trans Power Syst 34(2):864–872

    Article  Google Scholar 

  16. Zhuo Z, Du E, Zhang N, Kang C, Xia Q, Wang Z (2019) Incorporating massive scenarios in transmission expansion planning with high renewable energy penetration. IEEE Trans Power Syst 35(2):1061–1074

    Article  Google Scholar 

  17. Sobbouhi AR, Vahedi A (2021) Transient stability improvement based on out-of-step prediction. Electr Power Syst Res 194:107108

    Article  Google Scholar 

  18. Huang D, Yang X, Chen S, Meng T (2018) Wide-area measurement system-based model-free approach of post-fault rotor angle trajectory prediction for on-line transient instability detection. IET Gener Transm Distrib 12(10):2425–2435

    Article  Google Scholar 

  19. Gurusinghe DR, Rajapakse AD (2015) Post-disturbance transient stability status prediction using synchrophasor measurements. IEEE Trans Power Syst 31(5):3656–3664

    Article  Google Scholar 

  20. Zhou Y, Wu J, Yu Z, Ji L, Hao L (2016) A hierarchical method for transient stability prediction of power systems using the confidence of a SVM-based ensemble classifier. Energies 9(10):778

    Article  Google Scholar 

  21. Zhu L, Hill DJ, Lu C (2019) Hierarchical deep learning machine for power system online transient stability prediction. IEEE Trans Power Syst 35(3):2399–2411

    Article  Google Scholar 

  22. Geeganage J, Annakkage U, Weekes T, Archer BA (2014) Application of energy-based power system features for dynamic security assessment. IEEE Trans Power Syst 30(4):1957–1965

    Article  Google Scholar 

  23. Tang Y, Li F, Wang Q, Xu Y (2018) Hybrid method for power system transient stability prediction based on two-stage computing resources. IET Gener Transm Distrib 12(8):1697–1703

    Article  Google Scholar 

  24. Hosseini H, Naderi S, Afsharnia S (2019) New approach to transient stability prediction of power systems in wide area measurement systems based on multiple-criteria decision making theory. IET Gener Transm Distrib 13(21):4960–4967

    Article  Google Scholar 

  25. Sharifian A, Sharifian S (2015) A new power system transient stability assessment method based on Type-2 fuzzy neural network estimation. Int J Electr Power Energy Syst 64:71–87

    Article  Google Scholar 

  26. Yan R, Geng G, Jiang Q, Li Y (2019) Fast transient stability batch assessment using cascaded convolutional neural networks. IEEE Trans Power Syst 34(4):2802–2813

    Article  Google Scholar 

  27. Arefi M, Chowdhury B (2017) Post-fault transient stability status prediction using Grey Wolf and Particle Swarm Optimization. In: SoutheastCon 2017, Charlotte, NC, USA. IEEE, pp 1–8

  28. Frimpong EA, Okyere PY, Asumadu J (2017) Prediction of transient stability status using Walsh-Hadamard transform and support vector machine. In: 2017 IEEE PES PowerAfrica, Accra, Ghana. IEEE, pp 301–306

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

    Article  Google Scholar 

  30. Mazhari SM, Safari N, Chung C, Kamwa I (2018) A hybrid fault cluster and thévenin equivalent based framework for rotor angle stability prediction. IEEE Trans Power Syst 33(5):5594–5603

    Article  Google Scholar 

  31. Rajapakse AD, Gomez F, Nanayakkara K, Crossley PA, Terzija VV (2009) Rotor angle instability prediction using post-disturbance voltage trajectories. IEEE Trans Power Syst 25(2):947–956

    Article  Google Scholar 

  32. Abdul Wahab NI, Mohamed A (2012) Area-based COI-referred rotor angle index for transient stability assessment and control of power systems. Abstr Appl Anal. https://doi.org/10.1155/2012/410461

    Article  Google Scholar 

  33. Liao TW (2005) Clustering of time series data—a survey. Pattern Recogn 38(11):1857–1874

    Article  MATH  Google Scholar 

  34. Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158–168

    Article  Google Scholar 

  35. Kar S, Das S, Ghosh PK (2014) Applications of neuro fuzzy systems: A brief review and future outline. Appl Soft Comput 15:243–259

    Article  Google Scholar 

  36. Karahoca A, Karahoca D (2011) GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system. Expert Syst Appl 38(3):1814–1822

    Article  Google Scholar 

  37. Pai M (2012) Energy function analysis for power system stability. Springer Science & Business Media, Berlin

    Google Scholar 

  38. Milano F, Vanfretti L, Morataya JC (2008) An open source power system virtual laboratory: the PSAT case and experience. IEEE Trans Educ 51(1):17–23

    Article  Google Scholar 

  39. Chandra A, Pradhan AK (2020) Model-free angle stability assessment using wide area measurements. Int J Electr Power Energy Syst 120:105972

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Kurdistan, Sanandaj, Kurdistan, Iran

    Shiva Amini, Sasan Ghasemi & Jamal Moshtagh

  2. Taha Niroo Arya Company, Tehran, Iran

    Iman Azadimoshfegh

  3. Department of Management and Innovation Systems, University of Salerno, Fisciano, Italy

    Pierluigi Siano

Authors
  1. Shiva Amini
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  2. Sasan Ghasemi
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  3. Iman Azadimoshfegh
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  4. Jamal Moshtagh
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  5. Pierluigi Siano
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All authors contribute equally to this manuscript.

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Correspondence to Sasan Ghasemi.

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Amini, S., Ghasemi, S., Azadimoshfegh, I. et al. A two-stage strategy for generator rotor angle stability prediction using the adaptive neuro-fuzzy inference system. Electr Eng 105, 2871–2887 (2023). https://doi.org/10.1007/s00202-023-01827-1

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  • DOI: https://doi.org/10.1007/s00202-023-01827-1

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