L-Index-Based Technique for Voltage Collapse Prediction and Voltage Stability Enhancement in Electrical Power Systems
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Published:
Jun 5, 2024
Keywords:
Voltage Collapse, Voltage Collapse Prediction, Voltage Stability, L-index, Static Synchronous Compensator (STATCOM), Voltage Stability Enhancement
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Abstract
Recent years have witnessed a notable increase in the occurrence of blackouts, especially in developing nations, attributed to the continuously growing demand on modern power networks. Given that the demand shows no signs of abating and is projected to increase further in the coming years, additional research on power system stability is imperative. This study, therefore, investigates voltage stability assessment in power systems using the L-index methodology, focusing on the Nigerian 28-bus system and the IEEE system. The L-index offers a practical means of identifying weak buses and evaluating voltage stability margins. Calculating L-index values for load buses under diverse conditions identifies critical points, with higher values indicating vulnerability. The research investigates injecting reactive power at load buses to prevent collapse, comparing outcomes with and without compensation. Analyzing the L-index's performance across varied loading scenarios confirms its precision in predicting breakdown points and identifying critical buses. Load flow analysis of the Nigerian 28-Bus system reveals that only bus 16 exceeds voltage limits, while line analysis shows total power losses. Increasing loadability exposes bus 16 as the weakest, supported by its low voltage magnitude. The research confirms bus 16 as the system's weakest point, guiding corrective measures to enhance stability and prevent collapse. Utilizing Matlab for implementation, this study contributes valuable insights into system vulnerability and provides a framework for improving voltage stability in power systems.
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[1]
A. S. Alayande, A. O. Hassan, F. Olobaniyi, S. O. Osokoya, A. I. Adebeshin, and A. B. Ogundare, “L-Index-Based Technique for Voltage Collapse Prediction and Voltage Stability Enhancement in Electrical Power Systems”, AJERD, vol. 7, no. 1, pp. 260-277, Jun. 2024.
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References
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[3] Zohre Alipour, M. A. S. M., (2013), Structural Properties and vulnerability of Iranian 400kv Power Transmission Grid: a Complex Systems Approach, Industrial Engineering & Management, 2(3), 1–7. https://doi.org/10.4172/2169-0316.1000112
[4] Abedi, A., Gaudard, L., & Romerio, F., (2018), Review of major approaches to analyze vulnerability in power system, Reliability Engineering and System Safety, 153–172. https://doi.org/10.1016/j.ress.2018.11.019
[5] Hailu, E. A., Nyakoe, G. N., & Muriithi, C. M., (2023), Techniques of power system static security assessment and improvement: A literature survey, Heliyon, 9(3), e14524. https://doi.org/10.1016/j.heliyon.2023.e14524
[6] Asadi Majd, A., Farjah, E., Rastegar, M., & Bacha, S., (2021), Generation and transmission expansion planning for bulk renewable energy export considering transmission service cost allocation, Electric Power Systems Research, 196(October 2020), 107197. https://doi.org/10.1016/j.epsr.2021.107197
[7] Power quality and stability improvement of more ‑ electronics power systems, (2018).
[8] Velayati, M. H., Amjady, N., & Khajevandi, I., (2015), Prediction of dynamic voltage stability status based on Hopf and limit induced bifurcations using extreme learning machine, International Journal of Electrical Power and Energy Systems, 69, 150–159. https://doi.org/10.1016/j.ijepes.2015.01.005
[9] Pinzón, J. D., & Colomé, D. G., (2019), Real-time multi-state classification of short-term voltage stability based on multivariate time series machine learning, International Journal of Electrical Power and Energy Systems, 108, 402–414. https://doi.org/10.1016/j.ijepes.2019.01.022
[10] Malbasa, V., Zheng, C., Chen, P. C., Popovic, T., & Kezunovic, M., (2017), Voltage Stability Prediction Using Active Machine Learning, IEEE Transactions on Smart Grid, 8(6), 3117–3124. https://doi.org/10.1109/TSG.2017.2693394
[11] Wokoma, B. A., Osegi, E. N., & Idachaba, A. O., (2019), Predicting Voltage Stability Indices of Nigerian 330kV 30 Bus Power Network Using an Auditory Machine Intelligence Technique, IEEE AFRICON Conference, 2019-Septe. https://doi.org/10.1109/AFRICON46755.2019.9133915
[12] Krishnan, V., & McCalley, J. D., (2012), Contingency assessment under uncertainty for voltage collapse and its application in risk based contingency ranking, International Journal of Electrical Power and Energy Systems, 43(1), 1025–1033. https://doi.org/10.1016/j.ijepes.2012.05.065
[13] Wang, G., Zhang, Z., Bian, Z., & Xu, Z., (2021), A short-term voltage stability online prediction method based on graph convolutional networks and long short-term memory networks, International Journal of Electrical Power and Energy Systems, 127. https://doi.org/10.1016/j.ijepes.2020.106647
[14] Ghaghishpour, A., & Koochaki, A., (2020), An intelligent method for online voltage stability margin assessment using optimized ANFIS and associated rules technique, ISA Transactions, 102, 91–104. https://doi.org/10.1016/j.isatra.2020.02.028
[15] Rahi, O. P., Yadav, A. K., Malik, H., Azeem, A., & Bhupesh, K., (2012), Power system voltage stability assessment through artificial neural network, Procedia Engineering, 30, 53–60. https://doi.org/10.1016/j.proeng.2012.01.833
[16] Salama, M. M., Saied, E. M., Abou-Elsaad, M. M., & Ghariany, E. F., (2001), Estimating the voltage collapse proximity indicator using artificial neural network, Energy Conversion and Management, 42(1), 69–79. https://doi.org/10.1016/S0196-8904(00)00023-6
[17] Ibrahim, A. M., & El-Amary, N. H., (2018), Particle Swarm Optimization trained recurrent neural network for voltage instability prediction, Journal of Electrical Systems and Information Technology, 5(2), 216–228. https://doi.org/10.1016/j.jesit.2017.05.001
[18] Goh, H. H., Chua, Q. S., Lee, S. W., Kok, B. C., Goh, K. C., & Teo, K. T. K., (2015), Evaluation for Voltage Stability Indices in Power System Using Artificial Neural Network, Procedia Engineering, 118, 1127–1136. https://doi.org/10.1016/j.proeng.2015.08.454
[19] Cai, H., & Hill, D. J., (2022), A real-time continuous monitoring system for long-term voltage stability with sliding 3D convolutional neural network, International Journal of Electrical Power and Energy Systems, 134. https://doi.org/10.1016/j.ijepes.2021.107378
[20] Jeyasurya, B., (1994), Artificial neural networks for power system steady-state voltage instability evaluation, Electric Power Systems Research, 29(2), 85–90. https://doi.org/10.1016/0378-7796(94)90065-5
[21] Nizam, M., Mohamed, A., & Hussain, A., (2010), Dynamic voltage collapse prediction in power systems using support vector regression, Expert Systems with Applications, 37(5), 3730–3736. https://doi.org/10.1016/j.eswa.2009.11.052
[22] Tiwari, R., Niazi, K. R., & Gupta, V., (2012), Line collapse proximity index for prediction of voltage collapse in power systems, International Journal of Electrical Power and Energy Systems, 41(1), 105–111. https://doi.org/10.1016/j.ijepes.2012.03.022
[23] Pama, A., & Radman, G., (2009), A new approach for estimating voltage collapse point based on quadratic approximation of PV-curves, Electric Power Systems Research, 79(4), 653–659. https://doi.org/10.1016/j.epsr.2008.09.018
[24] Woldu, T. A., Ziegler, C., & Wolter, M., (2020), A new method for prediction of static and dynamic voltage collapse using node parameters in large power networks, IEEE PES Innovative Smart Grid Technologies Conference Europe, 2020-Octob, 344–348. https://doi.org/10.1109/ISGT-Europe47291.2020.9248887
[25] Pourbagher, R., Derakhshandeh, S. Y., & Hamedani Golshan, M. E., (2022), An adaptive multi-step Levenberg-Marquardt continuation power flow method for voltage stability assessment in the Ill-conditioned power systems, International Journal of Electrical Power and Energy Systems, 134. https://doi.org/10.1016/j.ijepes.2021.107425
[26] Satpathy, P. K., Das, D., & Dutta Gupta, P. B., (2004), Critical switching of capacitors to prevent voltage collapse, Electric Power Systems Research, 71(1), 11–20. https://doi.org/10.1016/j.epsr.2003.12.016
[27] Vanfretti, L., & Arava, V. S. N., (2020), Decision tree-based classification of multiple operating conditions for power system voltage stability assessment, International Journal of Electrical Power and Energy Systems, 123, 1–9. https://doi.org/10.1016/j.ijepes.2020.106251
[28] Razmi, H., Shayanfar, H. A., & Teshnehlab, M., (2012), Steady state voltage stability with AVR voltage constraints, International Journal of Electrical Power and Energy Systems, 43(1), 650–659. https://doi.org/10.1016/j.ijepes.2012.06.051
[29] Perninge, M., & Söder, L., (2011), Risk estimation of the distance to voltage instability using a second order approximation of the saddle-node bifurcation surface, Electric Power Systems Research, 81(2), 625–635. https://doi.org/10.1016/j.epsr.2010.10.021
[30] Dobson, I., & Chiang, H. D., (1989), Towards a theory of voltage collapse in electric power systems, Systems and Control Letters, 13(3), 253–262. https://doi.org/10.1016/0167-6911(89)90072-8
[31] Church, C., Morsi, W. G., Diduch, C. P., El-Hawary, M. E., & Chang, L., (2010), Voltage collapse detection using ant colony optimization for smart grid applications, EPEC 2010 - IEEE Electrical Power and Energy Conference: “Sustainable Energy for an Intelligent Grid.” https://doi.org/10.1109/EPEC.2010.5697185
[32] Verbič, G., & Gubina, F., (2004), A novel scheme of local protection against voltage collapse based on the apparent-power losses, International Journal of Electrical Power and Energy System, 26(5), 341–347. https://doi.org/10.1016/j.ijepes.2003.11.001
[33] Nizam, M., Mohamed, A., & Hussain, A., (2010), Dynamic voltage collapse prediction in power systems using support vector regression, Expert Systems with Applications, 37(5), 3730–3736. https://doi.org/10.1016/j.eswa.2009.11.052
[34] Sanz, F. A., Ramirez, J. M., & Posada, J., (2016), Statistical method for on-line voltage collapse proximity estimation, International Journal of Electrical Power and Energy Systems, 82, 392–399. https://doi.org/10.1016/j.ijepes.2016.03.035
[35] Al-Hinai, A., & Choudhry, T. M. A. C., (2001), Voltage Collapse Prediction for Interconnected Power Systems, In Proc. of 33rd North American Power Symposium (NAPS), College Station, TX, October 2001. College Station, TX. Retrieved from https://www.researchgate.net/publication/266450683
[36] Balamourougan, V., Sidhu, T. S., & Sachdev, M. S., (2004), A technique for real time detection of voltage collapse in power systems, IEE Conference Publication, 2, 639–642. https://doi.org/10.1049/cp:20040205
[37] Pourjafari, E., & Mojallali, H., (2011), Predictive control for voltage collapse avoidance using a modified discrete multi-valued PSO algorithm, ISA Transactions, 50(2), 195–200. https://doi.org/10.1016/j.isatra.2010.12.006
[38] Wang, Y., Xu, Q., & Zheng, J., (2020), The new steady state voltage stability analysis methods with computation loads separation technique in DC power systems, International Journal of Electrical Power and Energy Systems, 115, 1–8. https://doi.org/10.1016/j.ijepes.2019.105482
[39] Li, X., Zhang, L., Jiang, T., Li, F., Chen, H., & Jia, H., (2021), Relaxed decoupled direct calculation of voltage collapse points and its application in static voltage stability region boundary formation, International Journal of Electrical Power and Energy Systems, 125, 1–13. https://doi.org/10.1016/j.ijepes.2020.106452
[40] Šmon, I., Pantoš, M., & Gubina, F., (2008), An improved voltage-collapse protection algorithm based on local phasors, Electric Power Systems Research, 78(3), 434–440. https://doi.org/10.1016/j.epsr.2007.03.012
[41] Liu, S., Shi, R., Zhang, T., Tang, F., Zhang, L., Liu, L., … Zhang, M., (2021), An integrated scheme for static voltage stability assessment based on correlation detection and random bits forest, International Journal of Electrical Power and Energy Systems, 130, 1–10. https://doi.org/10.1016/j.ijepes.2021.106898
[42] Yang, F., Ling, Z., Wei, M., Mi, T., Yang, H., & Qiu, R. C., (2021), Real-time static voltage stability assessment in large-scale power systems based on spectrum estimation of phasor measurement unit data, International Journal of Electrical Power and Energy Systems, 124, 1–10. https://doi.org/10.1016/j.ijepes.2020.106196
[43] Ratra, S., Tiwari, R., & Niazi, K. R., (2018), Voltage stability assessment in power systems using line voltage stability index, Computers and Electrical Engineering, 70, 199–211. https://doi.org/10.1016/j.compeleceng.2017.12.046
[44] Chandra, A., & Pradhan, A. K., (2019), Online voltage stability and load margin assessment using wide area measurements, International Journal of Electrical Power and Energy Systems, 108, 392–401. https://doi.org/10.1016/j.ijepes.2019.01.021
[45] Yang, H., Qiu, R. C., Shi, X., & He, X., (2020), Unsupervised feature learning for online voltage stability evaluation and monitoring based on variational autoencoder, Electric Power Systems Research, 182(4), 1–13. https://doi.org/10.1016/j.epsr.2020.106253
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