Skip to main content

Advertisement

Springer Nature Link
Log in

A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system

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

Abstract

Modern power system networks are inherently complex and susceptible to a range of undesirable events, such as line and generator outages, transmission line faults, and power oscillations. These power oscillations arise following disturbances, causing generators to oscillate in relation to one another. Effectively damping these oscillations is crucial for ensuring the reliable operation of the overall system. In light of this, our research employs a convolutional neural network (CNN) approach to fine-tune the parameters of a conventional power system stabilizer (PSS). The goal is to enhance the damping of power oscillations triggered by abrupt disturbances in a multi-machine system. In the process of training the neural network, input vectors are transformed into image vectors and subsequently trained using a CNN architecture. This trained network is then utilized to derive optimized PSS parameters from test data. The proposed CNN-based PSS is rigorously evaluated across a variety of operational scenarios, demonstrating its effectiveness in enhancing power oscillation damping in both single-machine infinite bus and multi-machine systems. Time-domain simulations are conducted to analyze rotor speed and rotor angle deviations within the system equipped with the proposed CNN-based PSS. Comparative assessments are made against systems employing different conventional PSS configurations and systems without PSS. The advantages of CNN-based PSS are its ability to capture complex patterns and relationships in the data, enabling it to effectively learn and adapt to various operating conditions, resulting in improved damping performance. Remarkably, our proposed PSS exhibits superior performance in terms of damping power oscillations, effectively outperforming conventional PSS approaches.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

All data generated or analyzed during this study are included in this published article.

References

  1. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control, vol 7. McGraw-hill New York

  2. Kundur PS, Balu NJ, Lauby MG (2017) Power system dynamics and stability. Power Syst Stab Control 3:700–701

    Google Scholar 

  3. Sarkar DU, Prakash T (2023) A recent review on approaches to design power system stabilizers: status, challenges and future scope. IEEE Access 11(34044–34):061. https://doi.org/10.1109/ACCESS.2023.3244687

    Article  Google Scholar 

  4. Zhang X, Lu C, Liu S et al (2016) A review on wide-area damping control to restrain inter-area low frequency oscillation for large-scale power systems with increasing renewable generation. Renew Sustain Energy Rev 57:45–58

    Article  Google Scholar 

  5. Padiyar K et al (1996) Power system dynamics: stability and control. John Wiley, New York

    Google Scholar 

  6. Kundur P (2007) Power system stability. Power Syst Stab Control 10:7

  7. Devarapalli R, Sinha NK, García Márquez FP (2022) A review on the computational methods of power system stabilizer for damping power network oscillations. Archiv Comput Methods Eng 29:1–27

  8. Han W, Stanković AM (2022) Model-predictive control design for power system oscillation damping via excitation-a data-driven approach. IEEE Trans Power Syst 38(2):1176–1188

    Article  ADS  Google Scholar 

  9. Tzounas G, Sipahi R, Milano F (2021) Damping power system electromechanical oscillations using time delays. IEEE Trans Circ Syst I Regul Pap 68(6):2725–2735

    Article  MathSciNet  Google Scholar 

  10. Du W, Dong W, Wang Y et al (2020) A method to design power system stabilizers in a multi-machine power system based on single-machine infinite-bus system model. IEEE Trans Power Syst 36(4):3475–3486

    Article  ADS  Google Scholar 

  11. Banna HU, Luna A, Rodriguez P, et al (2014) Performance analysis of conventional pss and fuzzy controller for damping power system oscillations. In: 2014 International Conference on Renewable Energy Research and Application (ICRERA), IEEE, pp 229–234

  12. Shah B (2013) Comparative study of conventional and fuzzy based power system stabilizer. In: 2013 International Conference on Communication Systems and Network Technologies, IEEE, pp 547–551

  13. Yao W, Jiang L, Wu Q et al (2010) Delay-dependent stability analysis of the power system with a wide-area damping controller embedded. IEEE Trans Power Syst 26(1):233–240

    Article  ADS  Google Scholar 

  14. Beiraghi M, Ranjbar A (2016) Adaptive delay compensator for the robust wide-area damping controller design. IEEE Trans Power Syst 31(6):4966–4976

    Article  ADS  Google Scholar 

  15. Prakash T, Singh VP, Mohanty SR (2019) A synchrophasor measurement based wide-area power system stabilizer design for inter-area oscillation damping considering variable time-delays. Int J Electr Power Energy Syst 105:131–141

    Article  Google Scholar 

  16. Taghinezhad J, Sheidaei S (2022) Prediction of operating parameters and output power of ducted wind turbine using artificial neural networks. Energy Rep 8:3085–3095

    Article  Google Scholar 

  17. Yang Y, Li Z, Song A et al (2023) Parameter coordination optimization of power system stabilizer based on similarity index of power system state-bp neural network. Energy Rep 9:427–437

    Article  Google Scholar 

  18. Sabo A, Wahab NIA, Othman ML et al (2021) Artificial intelligence-based power system stabilizers for frequency stability enhancement in multi-machine power systems. IEEE Access 9:166095–166116

    Article  Google Scholar 

  19. Kumar PM, Saravanakumar R, Karthick A et al (2022) Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system. Environ Sci Pollut Res 29(7):10173–10182

    Article  Google Scholar 

  20. Velasco LCP, Arnejo KAS, Macarat JSS (2022) Performance analysis of artificial neural network models for hour-ahead electric load forecasting. Proc Comput Sci 197:16–24

    Article  Google Scholar 

  21. Ravesh NR, Ramezani N, Ahmadi I et al (2022) A hybrid artificial neural network and wavelet packet transform approach for fault location in hybrid transmission lines. Electr Power Syst Res 204(107):721

    Google Scholar 

  22. Segal R, Kothari M, Madnani S (2000) Radial basis function (rbf) network adaptive power system stabilizer. IEEE Trans Power Syst 15(2):722–727

    Article  ADS  Google Scholar 

  23. Ramakrishna G, Malik O (2004) Radial basis function identifier and pole-shifting controller for power system stabilizer application. IEEE Trans Energy Convers 19(4):663–670. https://doi.org/10.1109/TEC.2004.837268

    Article  ADS  Google Scholar 

  24. Ramakrishna G, Malik O, Segal R et al (2000) Discussion of “radial basis function (rbf) network adaptive power system stabilizer’’ [and reply]. IEEE Trans Power Syst 15(4):1448–1449. https://doi.org/10.1109/59.919213

    Article  ADS  Google Scholar 

  25. Luo X, Wu C, Rosenberg D et al (2009) Supplier selection in agile supply chains: an information-processing model and an illustration. J Purch Supply Manag 15(4):249–262

    Article  Google Scholar 

  26. Gupta P, Pal A, Vittal V (2022) Coordinated wide-area damping control using deep neural networks and reinforcement learning. IEEE Trans Power Syst 37(1):365–376. https://doi.org/10.1109/TPWRS.2021.3091940

    Article  ADS  Google Scholar 

  27. Hannan MA, Islam NN, Mohamed A et al (2018) Artificial intelligent based damping controller optimization for the multi-machine power system: a review. IEEE Access 6:39574–39594

    Article  Google Scholar 

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

    Article  ADS  Google Scholar 

  29. Ni C, Ma X, Bai Y (2018) Convolutional neural network based power generation prediction of wave energy converter. In: 2018 24th International Conference on Automation and Computing (ICAC), pp 1–6, https://doi.org/10.23919/IConAC.2018.8749043

  30. Dong N, Chang JF, Wu AG et al (2020) A novel convolutional neural network framework based solar irradiance prediction method. Int J Electr Power Energy Syst 114(105):411

    Google Scholar 

  31. Nsangou JC, Kenfack J, Nzotcha U et al (2022) Explaining household electricity consumption using quantile regression, decision tree and artificial neural network. Energy 250(123):856

    Google Scholar 

  32. Sarkar DU, Prakash T (2022) A neural network approach to design power system stabilizer for damping power oscillations. In: 2022 22nd National Power Systems Conference (NPSC), pp 837–842, https://doi.org/10.1109/NPSC57038.2022.10070020

  33. Sauer PW and Pai MA (1997) Power system dynamics and stability. https://bit.ly/3JgwKjJ, published: 1997

  34. Sarkar DU, Prakash T (2022) A novel design of power system stabilizer via gwo-tuned radial-basis function neural network for damping power oscillations. In: 2022 IEEE 10th Power India International Conference (PIICON), pp 1–6, https://doi.org/10.1109/PIICON56320.2022.10045259

  35. Li J, Xu J, Tang X, et al (2020) Adaptive structure evolution convolutional neural network for image recognition. In: 2020 Chinese Control And Decision Conference (CCDC), IEEE, pp 5291–5296

  36. Rafique Z, Khalid HM, Muyeen S et al (2022) Bibliographic review on power system oscillations damping: an era of conventional grids and renewable energy integration. Int J Electr Power Energy Syst 136(107):556

    Google Scholar 

  37. Shi Z, Yao W, Zeng L et al (2020) Convolutional neural network-based power system transient stability assessment and instability mode prediction. Appl Energy 263(114):586

    Google Scholar 

  38. Jana D, Patil J, Herkal S et al (2022) Cnn and convolutional autoencoder (cae) based real-time sensor fault detection, localization, and correction. Mech Syst Signal Process 169(108):723

    Google Scholar 

  39. Guo Q, Liu L, Xu W et al (2020) An improved faster r-cnn for high-speed railway dropper detection. IEEE Access 8:105622–105633

    Article  Google Scholar 

  40. Alqudah M, Pavlovski M, Dokic T et al (2022) Fault detection utilizing convolution neural network on timeseries synchrophasor data from phasor measurement units. IEEE Trans Power Syst 37(5):3434–3442. https://doi.org/10.1109/TPWRS.2021.3135336

    Article  ADS  Google Scholar 

  41. Janssens O, Slavkovikj V, Vervisch B et al (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345

    Article  ADS  Google Scholar 

  42. Mitiche I, Nesbitt A, Conner S et al (2020) 1d-cnn based real-time fault detection system for power asset diagnostics. IET Gener Trans Distrib 14(24):5766–5773

    Article  Google Scholar 

  43. Tikariha A, Bag BN, Londhe ND, et al (2021) Fault classification in an ieee 30 bus system using convolutional neural network. In: 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), IEEE, pp 57–61

  44. Xu G, Liu M, Jiang Z et al (2019) Online fault diagnosis method based on transfer convolutional neural networks. IEEE Trans Instrum Meas 69(2):509–520

    Article  ADS  Google Scholar 

  45. Lee HJ, Kim KT, Park JH et al (2021) Convolutional neural network-based false battery data detection and classification for battery energy storage systems. IEEE Trans Energy Convers 36(4):3108–3117

    Article  ADS  Google Scholar 

  46. Wang J, Hu X (2021) Convolutional neural networks with gated recurrent connections. IEEE Transactions on Pattern Analysis and Machine Intelligence

  47. Jingfei C, Yang L, Ping X et al (2021) Compressing convolutional neural networks via intermediate features. J Intell Fuzzy Syst 41(2):2687–2699

    Article  Google Scholar 

  48. Chen W, Jiang M, Zhang WG et al (2021) A novel graph convolutional feature based convolutional neural network for stock trend prediction. Inf Sci 556:67–94

    Article  MathSciNet  Google Scholar 

  49. Sauer PW (1997) A self-tuning power system stabilizer based on artificial neural network. Int J Electr Power Energy Syst 26(6):423–430

    Google Scholar 

  50. Park YM, Choi MS, Lee KY (1996) A neural network-based power system stabilizer using power flow characteristics. IEEE Trans Energy Convers 11(2):435–441

    Article  ADS  Google Scholar 

  51. Demello FP, Concordia C (1969) Concepts of synchronous machine stability as affected by excitation control. IEEE Trans Power Appar Syst 88(4):316–329

    Article  ADS  Google Scholar 

  52. Gholamy A, Kreinovich V, Kosheleva O (2018) Why 70/30 or 80/20 relation between training and testing sets: a pedagogical explanation. Technical Report: UTEP-CS-18-09

  53. Sharma A, Kothari ML, Ravi S, Soni MG, Bhaskar MK (2002) Radial Basis Function (RBF) Network based Adaptive Dual Input Power System Stabilizer. National Power System Conference

  54. Pai M, Gupta DS, Padiyar K (2004) Small signal analysis of power systems. Alpha Science Int’l Ltd

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Vellore Institute of Technology University, Thiruvalam Road, Vellore, Tamil Nadu, 632014, India

    Devesh Umesh Sarkar & Tapan Prakash

Authors
  1. Devesh Umesh Sarkar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Tapan Prakash
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Tapan Prakash.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Additional information

Publisher's Note

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

Appendix

Appendix

The nominal operating initial condition is given below,

Inertia constant (H)=6.5; M=2*H; \(T_{do'}\)=8; Exciter gain (\(K_A\))=200; time constant (\(T_A\))=0.001 s; \(X_d\)=1.8; \(X_{d}'\)=0.3; \(X_q\)=1.7; Rated rotor speed (\(w_0\))=377 rad/s; Damping ratio (\(\zeta\)) =0.5; transducer time constant (\(T_R\))=0.02 [1, 51].

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sarkar, D.U., Prakash, T. A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system. Neural Comput & Applic 36, 5059–5075 (2024). https://doi.org/10.1007/s00521-023-09323-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09323-0

Keywords