Assessment of the voltage stability of the power system using stability indices and artificial neural network | Science and Technology for Energy Transition (STET)

Open Access

Issue		Sci. Tech. Energ. Transition Volume 81, 2026


Article Number		4
Number of page(s)		16
DOI		https://doi.org/10.2516/stet/2026001
Published online		24 March 2026

Jayasankar V., Kamaraj N., Vanaja N. (2010) Estimation of voltage stability index for power system employing artificial neural network technique and TCSC placement, Neurocomputing 73, 16–18, 3005–3011. [Google Scholar]
Princy U., Jaseena S., Sreedharan S., Sreejith S. (2017) Voltage stability analysis of power system network integrated with renewable source and SVC, in: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, pp. 1–6. [Google Scholar]
Kamalasadan S., Srivastava A.K., Thukaram D. (2006) Novel algorithm for online voltage stability assessment based on feed forward neural network, in 2006 IEEE Power Engineering Society General Meeting, p. 7. [Google Scholar]
Puppala V., Chandrarao P. (2015) Analysis of continuous power flow method, model analysis, linear regression and ANN for voltage stability assessment for different loading conditions, Procedia Comput. Sci. 47, 168–178. [Google Scholar]
Modarresi J., Gholipour E., Khodabakhshian A. (2016) A comprehensive review of the voltage stability indices, Renew. Sustain. Energy Rev. 63, 1–12. [CrossRef] [Google Scholar]
Borazjani O., Roosta M., Isapour K., Rajabi A.R. (2015) Using artificial neural network algorithm for voltage stability improvement, Int. J. Electric. Comput Eng. 9, 3, 365–370. [Google Scholar]
Jadhao C.W., Vadirajacharya K. (2015) Performance improvement of power system through static VAR compensator using sensitivity indices analysis method, in 2015 International Conference on Energy systems and applications, IEEE, pp. 200–202. [Google Scholar]
Sharma A. (2016) Voltage stability assessment using GVSM and preventive control using SVC, Int. J. Comput. Appl. 142, 11. [Google Scholar]
Chakrabarti S. (2008) Voltage stability monitoring by artificial neural networkusing a regression-based feature selection method, Expert Syst. Appl. 35, 4, 1802–1808. [Google Scholar]
Ashraf S.M., Gupta A., Choudhary D.K., Chakrabarti S. (2017) Voltage stability monitoring of power systems using reduced network and artificial neural network. Int. J. Electr. Power Energy Syst. 87, 43–51. [Google Scholar]
Sharma P., Ahuja R.K., Vashisth S., Hudda V. (2014) Computation of sensitive node for IEEE-14 bus system subjected to load variation, in Int. J. Innovative Res. Electric. Electron. Instrum. Control Eng. 2, 6, 1603–1607. [Google Scholar]
Devaraj D., Roselyn J.P. (2011) Online voltage stability assessment using radial basis function network model with reduced input features, Int. J. Electr. Power Energy Syst. 33(9), 1550–1555. [Google Scholar]
Subramani C., Jimoh A., Kiran S.H., Dash S.S. (2016) Artificial neural network-based voltage stability analysis in power system, in 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), IEEE, pp. 1–4. [Google Scholar]
Goh H., Chua Q., Lee S., Kok B., Goh K., Teo K. (2015) Evaluation for voltage stability indices in power system using artificial neural network, Procedia Eng. 118, 1127–1136. [Google Scholar]
Singh P., Parida S.K., Chauhan B., Choudhary N. (2020) Online Voltage stability assessment using artificial neural network considering voltage stability indices, in 2020 21st National Power Systems Conference (NPSC), Gandhinagar, India, pp. 1–5. https://doi.org/10.1109/NPSC49263.2020.9331954. [Google Scholar]
Aydin F., Gümüş B. (2020) Study of different ANN algorithms for voltage stability analysis, in 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, pp. 1–5. https://doi.org/10.1109/ASYU50717.2020.9259817. [Google Scholar]
Kumar S., Tyagi B., Kumar V., Chohan S. (2022) PMU-based voltage stability measurement under contingency using ANN, IEEE Trans. Instrum. Measure. 71, 1–11. Art. No. 9000111. https://doi.org/10.1109/TIM.2021.3129210. [CrossRef] [Google Scholar]
Zhang C., Mijatovic N., Cai X., Dragičević T. (2022) Artificial neural network-based pole-tracking method for online stabilization control of grid-tied VSC, IEEE Trans. Indus. Electron. 69, 12, 13902–13909. https://doi.org/10.1109/TIE.2021.3134079. [Google Scholar]
Chettibi N., Massi Pavan A., Mellit A., Forsyth A.J., Todd R. (2021) Real-time prediction of grid voltage and frequency using artificial neural networks: An experimental validation, Sustain. Energy Grid. Netw. 27, 100502. [Google Scholar]
Veerasamy V., Abdul Wahab N.I., Ramachandran R., Othman M.L., Hizam H., Devendran V.S., Irudayaraj A.X.R., Vinayagam A. (2021) Recurrent network-based power flow solution for voltage stability assessment and improvement with distributed energy sources. Appl. Energy 302, 117524. [Google Scholar]
Amroune M. (2021) Machine learning techniques applied to on-line voltage stability assessment: a review, Arch. Computat. Methods Eng., 28, 273–287. https://doi.org/10.1007/s11831-019-09368-2. [Google Scholar]
Saha G., Chakraborty K., Das P. (2022) Enrichment of voltage stability in power system through novel generalized approximate reasoning based intelligent control with African buffalo optimization approach, Soft Comput. 27, 7473–7496. https://doi.org/10.1007/s00500-022-07688-3. [Google Scholar]
Yadav A.N., Pal K. (2022) Demand side maximum loading limit ranking based on composite index using ANN, Int. J. Syst. Assur. Eng. Manag. 13, 1419–1429. [Google Scholar]
Hiremath R., Moger T. (2022) Grid-connected DFIG driven wind system for low voltage ride through enhancement using neural predictive controller, J. Inst. Eng. India Ser. B 103, 1577–1588. https://doi.org/10.1007/s40031-022-00761-3. [Google Scholar]
Alimi O.A., Ouahada K., Abu-Mahfouz A.M. (2020) A review of machine learning approaches to power system security and stability, IEEE Access 8, 113512–113531. https://doi.org/10.1109/ACCESS.2020.3003568. [Google Scholar]
Kessel P., Glavitsch H. (1986) Estimating the voltage stability of a power system, IEEE Trans. Power Delivery 3, 346–354. [Google Scholar]
Gupta S.K., Mallik S.K., Tripathi J.M., Sahu P. 2021 Comparison of voltage stability index with optimal placement of SSSC considering maximum loadability, in 2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation (IRIA), Goa, India, pp. 1–6. https://doi.org/10.1109/IRIA53009.2021.9588759. [Google Scholar]
Moghavvemi M., Omar F.M. (1998) Technique for contingency monitoring and voltage collapse prediction. IEE Proc Gen. Trans. Distrib. 145, 6, 634–640. https://doi.org/10.1049/ipgtd:19982355. [Google Scholar]
Musirin I., Rahman T.K.A. (2002) Online voltage stability based contingency ranking using fast voltage stability index (FVSI), in Proceedings IEEE/PES Transmission Distribution and Exhibition Conference, Vol. 2, pp. 1118–1123. [Google Scholar]
Mohamed A., Jasmon G.B., Yusoff S. (1989) A static voltage collapse indicator using line stability factors, J. Indus. Technol. 7, 73–85. [Google Scholar]
Ratra S., Tiwari R., Niazi K.R. (2018) Voltage stability assessment in power systems using line voltage stability index, Comp. Electric. Eng. 70, 199–211. [Google Scholar]
Adetokun B.B., Muriithi C.M., Ojo J.O. (2020) Voltage stability assessment and enhancement of power grid with increasing wind energy penetration, Int. J. Electric. Power Energy Syst. 120, 105988. [Google Scholar]

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