Online dynamic assessment of system stability in power systems using the unscented Kalman filter
Dynamic state estimation on of power systems stability based on Phasor Measurement Units (PMUs) data is a requirement in order to assess power system security. This paper addresses the state estimation problem from a strong mathematical point of view to be applied for estimation on real time. Specif...
- Autores:
-
Moreno-Chuquen, Ricardo
Florez Cediel, Oscar David
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/12920
- Acceso en línea:
- https://hdl.handle.net/10614/12920
- Palabra clave:
- Teoría de la estimación
Kalman filtering
Estimation theory
Filtración Kalman
State estimation
Kalman filter
On-line monitoring
Data-driven methods
Sliding window
Phasor measurements units
Dynamic state estimation
Wide area protection systems
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/
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Online dynamic assessment of system stability in power systems using the unscented Kalman filter |
| title |
Online dynamic assessment of system stability in power systems using the unscented Kalman filter |
| spellingShingle |
Online dynamic assessment of system stability in power systems using the unscented Kalman filter Teoría de la estimación Kalman filtering Estimation theory Filtración Kalman State estimation Kalman filter On-line monitoring Data-driven methods Sliding window Phasor measurements units Dynamic state estimation Wide area protection systems |
| title_short |
Online dynamic assessment of system stability in power systems using the unscented Kalman filter |
| title_full |
Online dynamic assessment of system stability in power systems using the unscented Kalman filter |
| title_fullStr |
Online dynamic assessment of system stability in power systems using the unscented Kalman filter |
| title_full_unstemmed |
Online dynamic assessment of system stability in power systems using the unscented Kalman filter |
| title_sort |
Online dynamic assessment of system stability in power systems using the unscented Kalman filter |
| dc.creator.fl_str_mv |
Moreno-Chuquen, Ricardo Florez Cediel, Oscar David |
| dc.contributor.author.none.fl_str_mv |
Moreno-Chuquen, Ricardo Florez Cediel, Oscar David |
| dc.subject.armarc.spa.fl_str_mv |
Teoría de la estimación Kalman filtering |
| topic |
Teoría de la estimación Kalman filtering Estimation theory Filtración Kalman State estimation Kalman filter On-line monitoring Data-driven methods Sliding window Phasor measurements units Dynamic state estimation Wide area protection systems |
| dc.subject.armarc.eng.fl_str_mv |
Estimation theory Filtración Kalman |
| dc.subject.proposal.eng.fl_str_mv |
State estimation Kalman filter On-line monitoring Data-driven methods Sliding window Phasor measurements units Dynamic state estimation Wide area protection systems |
| description |
Dynamic state estimation on of power systems stability based on Phasor Measurement Units (PMUs) data is a requirement in order to assess power system security. This paper addresses the state estimation problem from a strong mathematical point of view to be applied for estimation on real time. Specifically, this paper formulates a framework based on the Unscented Kalman Filter (UFK) in order to estimate in real-time if the angle stability is compromised or approaching instability. This paper proposes a predicting window as a time interval to forecast the rotor angle using real-time information. The framework uses the generator rotor angles and the electrical angular velocity as state variables, given that the power output of the generators is measured by PMUs and that the rotor movement equations are separated from the network ones. The estimation of the angle stability can provide meaningful information needed to evaluate and enhance the power system security. In order to note the performance for the UKF to assess and forecast online system stability for power systems, the UKF is tested when there are not measurements available. In this case, the UKF performs a forecasting on the angle in a onesecond window without available data. Emulated PMUs sampling data have been used for carrying the simulations and have been validated using the 9-IEEE buses test system. The results confirm the method and the performance of the estimation. The proposed approach provides an effective tool for real-time environment for security. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2021-03-31T23:40:44Z |
| dc.date.available.none.fl_str_mv |
2021-03-31T23:40:44Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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publishedVersion |
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18276660 |
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https://hdl.handle.net/10614/12920 |
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eng |
| language |
eng |
| dc.relation.citationendpage.spa.fl_str_mv |
472 |
| dc.relation.citationissue.spa.fl_str_mv |
Número 6 |
| dc.relation.citationstartpage.spa.fl_str_mv |
465 |
| dc.relation.citationvolume.spa.fl_str_mv |
Volumen 14 |
| dc.relation.cites.eng.fl_str_mv |
Moreno-Chuquen, Ricardo y Flórez-Cediel, Oscar. Online dynamic assessment of system stability in power systems using the unscented kalman filter. En: Revista Internacional de Ingeniería Eléctrica (IREE), volumen 14, número 6 (Noviembre-Diciembre, 2019), páginas 465-472. ISSN 1827-6660 |
| dc.relation.ispartofjournal.eng.fl_str_mv |
International Review of Electrical Engineering (I.R.E.E.) |
| dc.relation.references.none.fl_str_mv |
[1] T. Kropp, System threats and vulnerabilities [power system pro- tection], IEEE Power and Energy Magazine, vol. 4, pp. 46–50, March 2006 [2] Phanendra Babu, N., Suresh Babu, P., Siva Sarma, D., A Reliable Wide-Area Measurement System Using Hybrid Genetic Particle Swarm Optimization (HGPSO), (2015) International Review of Electrical Engineering (IREE), 10 (6), pp. 747-763. doi: https://doi.org/10.15866/iree.v10i6.7127 [3] Cai, G., Yang, D., Jiang, Y., Synchrophasor Associated Adaptive Control Strategy for Under Frequency Protection and Load Shedding in Smart Grid, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 227-235 [4] M. Anjia, Y. Jiaxi, and G. Zhizhong, PMU placement and data processing in WAMS that complements SCADA, in IEEE Power Engineering Society General Meeting, 2005, pp. 780–783 Vol. 1, June 2005 [5] R. Avila-Rosales, M. J. Rice, J. Giri, L. Beard, and F. Galvan, Recent experience with a hybrid SCADA/PMU on-line state estimator, in 2009 IEEE Power Energy Society General Meeting, pp. 1–8, July 2009. doi: https://doi.org/10.1109/PES.2009.5275954 [6] Moreno, R., Obando, J., Gonzalez, G., An integrated OPF dispatching model with wind power and demand response for day-ahead markets, (2019) International Journal of Electrical and Computer Engineering (IJECE), 4 (4), pp. 2794-2802. doi: http://doi.org/10.11591/ijece.v9i4.pp2794-2802 [7] Udomsuk, S., Areerak, K., Areerak, T., Areerak, K., Speed Estimation of Three-Phase Induction Motor Using Kalman Filter,(2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 267-275. doi: https://doi.org/10.15866/iree.v13i4.13451 [8] Molina-Cabrera, A., Rios, M., A Kalman Latency Compensation Strategy for Model Predictive Control to Damp Inter-Area Oscillations in Delayed Power Systems, (2016) International Review of Electrical Engineering (IREE), 11 (3), pp. 296-304. doi: https://doi.org/10.15866/iree.v11i3.8661 [9] Mohanty, K., Fuzzy Control of Wind Cage Induction Generator System, (2017) International Journal on Energy Conversion (IRECON), 5 (4), pp. 122-129. doi: https://doi.org/10.15866/irecon.v5i4.13755 [10] Grillo, C., Montano, F., Automatic EKF Tuning for UAS Path Following in Turbulent Air, (2018) International Review of Aerospace Engineering (IREASE), 11 (6), pp. 241-246. doi: https://doi.org/10.15866/irease.v11i6.15122 [11] Gaga, A., Benssassi, H., Errahimi, F., Es-Sbai, N., Battery State of Charge Estimation Using an Adaptive Unscented Kalman Filter for Photovoltaics Applications, (2017) International Review of Automatic Control (IREACO), 10 (4), pp. 349-358. doi: https://doi.org/10.15866/ireaco.v10i4.11393 [12] M. Rampelli and D. Jena, Advantage of unscented kalman filter over extended kalman filter in dynamic state estimation of power system network, in Michael Faraday IET International Summit 2015, pp. 278– 283, Sept 2015. [13] E. A. Feilat, Performance estimation techniques for power system dynamic stability using least squares, Kalman filtering and genetic algorithms, in Proceedings of the IEEE SoutheastCon 2000. ’Preparing for The New Millennium’ (Cat. No.00CH37105), pp. 489–492, April 2000. [14] H. Tebianian and B. Jeyasurya, Dynamic state estimation in power systems using kalman filters, in 2013 IEEE Electrical Power Energy Conference, pp. 1–5, Aug 2013. doi: https://doi.org/10.1109/EPEC.2013.6802979 [15] P. Pan, M. K. Sharma, T. Ghose, and D. K. Mohanta, Synchrophasor based concurrent swing and state estimation of synchronous generator, in 2018 Technologies for Smart-City Energy Security and Power (ICSESP), pp. 1–5, March 2018. doi: https://doi.org/10.1109/ICSESP.2018.8376701 [16] M. Netto, J. Zhao, and L. Mili, A robust extended kalman filter for power system dynamic state estimation using pmu measurements, in 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1–5, July 2016. doi: https://doi.org/10.1109/PESGM.2016.7741374 [17] E. Ghahremani and I. Kamwa, PMU analytics for decentralized dynamic state estimation of power systems using the extended kalman filter with unknown inputs, in 2015 IEEE Power Energy Society General Meeting, pp. 1–5, July 2015. doi: https://doi.org/10.1109/PESGM.2015.7286334 [18] H. Khazraj, F. F. da Silva, and C. L. Bak, Modeling of HVDC in dynamic state estimation using unscented kalman filter method, in 2016 IEEE International Energy Conference (ENERGYCON), pp. 1–6, April 2016. doi: https://doi.org/10.1109/ENERGYCON.2016.7513891 [19] Yunus, M., Djalal, M., Marhatang, M., Optimal Design Power System Stabilizer Using Firefly Algorithm in Interconnected 150 kV Sulselrabar System, Indonesia, (2017) International Review of Electrical Engineering (IREE), 12 (3), pp. 250-259. doi: https://doi.org/10.15866/iree.v12i3.11136 [20] R. Moreno, M.A. Rios, and A. Torres, Security schemes of power systems against blackouts, in 2010 IREP Symposium Bulk Power System Dynamics and Control - VIII (IREP), 2010. doi: https://doi.org/10.1109/IREP.2010.5563271 [21] Moreno-Chuquen, R., Obando-Ceron, J., Network Topological Notions for Power Systems Security Assessment, (2018) International Review of Electrical Engineering (IREE), 13 (3), pp. 237-245. [22] Herrera, J., Rios, M., A Robust Control for Damping Electromechanical Oscillations Modelling Additive Uncertainties in Power Systems, (2016) International Review of Electrical Engineering (IREE), 11 (5), pp. 467-476. doi: https://doi.org/10.15866/iree.v11i5.9314 [23] Zouhri, A., Boumhidi, I., Decentralized Robust H∞ Control of Large Scale Systems with Polytopic-Type Uncertainty, (2016) International Review of Automatic Control (IREACO), 9 (2), pp. 103- 109. doi: https://doi.org/10.15866/ireaco.v9i2.8728 [24] Shouman, M., El Bayoumi, G., Adaptive Robust Control of Satellite Attitude System, (2015) International Review of Aerospace Engineering (IREASE), 8 (1), pp. 35-42. doi: https://doi.org/10.15866/irease.v8i1.5322 [25] Kulkarni, S., Wagh, S., Singh, N., Challenges in Model Predictive Control Application for Transient Stability Improvement Using TCSC, (2015) International Review of Automatic Control (IREACO), 8 (2), pp. 163-169. doi: https://doi.org/10.15866/ireaco.v8i2.5562 [26] P. Korba, M. Larsson, and C. Rehtanz, Detection of oscillations in power systems using kalman filtering techniques, in Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003. vol. 1, pp. 183–188 vol.1, June 2003. doi: https://doi.org/10.1109/CCA.2003.1223290 [27] J. C. Peng and N. C. Nair, Comparative assessment of kalman filter and prony methods for power system oscillation monitoring, in 2009 IEEE Power Energy Society General Meeting, pp. 1–8, July 2009. doi: https://doi.org/10.1109/PES.2009.5275656 [28] R. Chang and T. K. Saha, Improved extended complex kalman filter for identifying inter-area oscillations, in 2012 IEEE International Conference on Power System Technology (POWERCON), pp. 1–6, Oct 2012. doi: https://doi.org/10.1109/PowerCon.2012.6401278 [29] J. Ma, T. Wang, Z. Wang, and J. S. Thorp, Adaptive damping control of inter-area oscillations based on federated kalman filter using wide area signals, IEEE Transactions on Power Systems, vol. 28, pp. 1627–1635, May 2013. doi: https://doi.org/10.1109/TPWRS.2012.2223721 [30] H. Khazraj, F. F. da Silva, and C. L. Bak, A performance comparison between extended kalman filter and unscented kalman filter in power system dynamic state estimation, in 2016 51st International Universities Power Engineering Conference (UPEC), pp. 1–6, Sept 2016. [31] D. Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, John Wiley & Sons, 2006 |
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Moreno-Chuquen, Ricardof36efacf1d947d7410ab7d332d414753Florez Cediel, Oscar David592f4439bdd28cd7649c75cefd9658a82021-03-31T23:40:44Z2021-03-31T23:40:44Z201918276660https://hdl.handle.net/10614/12920Dynamic state estimation on of power systems stability based on Phasor Measurement Units (PMUs) data is a requirement in order to assess power system security. This paper addresses the state estimation problem from a strong mathematical point of view to be applied for estimation on real time. Specifically, this paper formulates a framework based on the Unscented Kalman Filter (UFK) in order to estimate in real-time if the angle stability is compromised or approaching instability. This paper proposes a predicting window as a time interval to forecast the rotor angle using real-time information. The framework uses the generator rotor angles and the electrical angular velocity as state variables, given that the power output of the generators is measured by PMUs and that the rotor movement equations are separated from the network ones. The estimation of the angle stability can provide meaningful information needed to evaluate and enhance the power system security. In order to note the performance for the UKF to assess and forecast online system stability for power systems, the UKF is tested when there are not measurements available. In this case, the UKF performs a forecasting on the angle in a onesecond window without available data. Emulated PMUs sampling data have been used for carrying the simulations and have been validated using the 9-IEEE buses test system. The results confirm the method and the performance of the estimation. The proposed approach provides an effective tool for real-time environment for security.8 páginasapplication/pdfenghttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Online dynamic assessment of system stability in power systems using the unscented Kalman filterArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Teoría de la estimaciónKalman filteringEstimation theoryFiltración KalmanState estimationKalman filterOn-line monitoringData-driven methodsSliding windowPhasor measurements unitsDynamic state estimationWide area protection systems472Número 6465Volumen 14Moreno-Chuquen, Ricardo y Flórez-Cediel, Oscar. Online dynamic assessment of system stability in power systems using the unscented kalman filter. En: Revista Internacional de Ingeniería Eléctrica (IREE), volumen 14, número 6 (Noviembre-Diciembre, 2019), páginas 465-472. ISSN 1827-6660International Review of Electrical Engineering (I.R.E.E.)[1] T. Kropp, System threats and vulnerabilities[power system pro- tection], IEEE Power and Energy Magazine, vol. 4, pp. 46–50, March 2006[2] Phanendra Babu, N., Suresh Babu, P., Siva Sarma, D., A Reliable Wide-Area Measurement System Using Hybrid Genetic Particle Swarm Optimization (HGPSO), (2015) International Review of Electrical Engineering (IREE), 10 (6), pp. 747-763. doi: https://doi.org/10.15866/iree.v10i6.7127[3] Cai, G., Yang, D., Jiang, Y., Synchrophasor Associated Adaptive Control Strategy for Under Frequency Protection and Load Shedding in Smart Grid, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 227-235[4] M. Anjia, Y. Jiaxi, and G. Zhizhong, PMU placement and data processing in WAMS that complements SCADA, in IEEE Power Engineering Society General Meeting, 2005, pp. 780–783 Vol. 1, June 2005[5] R. Avila-Rosales, M. J. Rice, J. Giri, L. Beard, and F. Galvan, Recent experience with a hybrid SCADA/PMU on-line state estimator, in 2009 IEEE Power Energy Society General Meeting, pp. 1–8, July 2009. doi: https://doi.org/10.1109/PES.2009.5275954[6] Moreno, R., Obando, J., Gonzalez, G., An integrated OPF dispatching model with wind power and demand response for day-ahead markets, (2019) International Journal of Electrical and Computer Engineering (IJECE), 4 (4), pp. 2794-2802. doi: http://doi.org/10.11591/ijece.v9i4.pp2794-2802[7] Udomsuk, S., Areerak, K., Areerak, T., Areerak, K., Speed Estimation of Three-Phase Induction Motor Using Kalman Filter,(2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 267-275. doi: https://doi.org/10.15866/iree.v13i4.13451[8] Molina-Cabrera, A., Rios, M., A Kalman Latency Compensation Strategy for Model Predictive Control to Damp Inter-Area Oscillations in Delayed Power Systems, (2016) International Review of Electrical Engineering (IREE), 11 (3), pp. 296-304. doi: https://doi.org/10.15866/iree.v11i3.8661[9] Mohanty, K., Fuzzy Control of Wind Cage Induction Generator System, (2017) International Journal on Energy Conversion (IRECON), 5 (4), pp. 122-129. doi: https://doi.org/10.15866/irecon.v5i4.13755[10] Grillo, C., Montano, F., Automatic EKF Tuning for UAS Path Following in Turbulent Air, (2018) International Review of Aerospace Engineering (IREASE), 11 (6), pp. 241-246. doi: https://doi.org/10.15866/irease.v11i6.15122[11] Gaga, A., Benssassi, H., Errahimi, F., Es-Sbai, N., Battery State of Charge Estimation Using an Adaptive Unscented Kalman Filter for Photovoltaics Applications, (2017) International Review of Automatic Control (IREACO), 10 (4), pp. 349-358. doi: https://doi.org/10.15866/ireaco.v10i4.11393[12] M. Rampelli and D. Jena, Advantage of unscented kalman filter over extended kalman filter in dynamic state estimation of power system network, in Michael Faraday IET International Summit 2015, pp. 278– 283, Sept 2015.[13] E. A. Feilat, Performance estimation techniques for power system dynamic stability using least squares, Kalman filtering and genetic algorithms, in Proceedings of the IEEE SoutheastCon 2000. ’Preparing for The New Millennium’ (Cat. No.00CH37105), pp. 489–492, April 2000.[14] H. Tebianian and B. Jeyasurya, Dynamic state estimation in power systems using kalman filters, in 2013 IEEE Electrical Power Energy Conference, pp. 1–5, Aug 2013. doi: https://doi.org/10.1109/EPEC.2013.6802979[15] P. Pan, M. K. Sharma, T. Ghose, and D. K. Mohanta, Synchrophasor based concurrent swing and state estimation of synchronous generator, in 2018 Technologies for Smart-City Energy Security and Power (ICSESP), pp. 1–5, March 2018. doi: https://doi.org/10.1109/ICSESP.2018.8376701[16] M. Netto, J. Zhao, and L. Mili, A robust extended kalman filter for power system dynamic state estimation using pmu measurements, in 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1–5, July 2016. doi: https://doi.org/10.1109/PESGM.2016.7741374[17] E. Ghahremani and I. Kamwa, PMU analytics for decentralized dynamic state estimation of power systems using the extended kalman filter with unknown inputs, in 2015 IEEE Power Energy Society General Meeting, pp. 1–5, July 2015. doi: https://doi.org/10.1109/PESGM.2015.7286334[18] H. Khazraj, F. F. da Silva, and C. L. Bak, Modeling of HVDC in dynamic state estimation using unscented kalman filter method, in 2016 IEEE International Energy Conference (ENERGYCON), pp. 1–6, April 2016. doi: https://doi.org/10.1109/ENERGYCON.2016.7513891[19] Yunus, M., Djalal, M., Marhatang, M., Optimal Design Power System Stabilizer Using Firefly Algorithm in Interconnected 150 kV Sulselrabar System, Indonesia, (2017) International Review of Electrical Engineering (IREE), 12 (3), pp. 250-259. doi: https://doi.org/10.15866/iree.v12i3.11136[20] R. Moreno, M.A. Rios, and A. Torres, Security schemes of power systems against blackouts, in 2010 IREP Symposium Bulk Power System Dynamics and Control - VIII (IREP), 2010. doi: https://doi.org/10.1109/IREP.2010.5563271[21] Moreno-Chuquen, R., Obando-Ceron, J., Network Topological Notions for Power Systems Security Assessment, (2018) International Review of Electrical Engineering (IREE), 13 (3), pp. 237-245.[22] Herrera, J., Rios, M., A Robust Control for Damping Electromechanical Oscillations Modelling Additive Uncertainties in Power Systems, (2016) International Review of Electrical Engineering (IREE), 11 (5), pp. 467-476. doi: https://doi.org/10.15866/iree.v11i5.9314[23] Zouhri, A., Boumhidi, I., Decentralized Robust H∞ Control of Large Scale Systems with Polytopic-Type Uncertainty, (2016) International Review of Automatic Control (IREACO), 9 (2), pp. 103- 109. doi: https://doi.org/10.15866/ireaco.v9i2.8728[24] Shouman, M., El Bayoumi, G., Adaptive Robust Control of Satellite Attitude System, (2015) International Review of Aerospace Engineering (IREASE), 8 (1), pp. 35-42. doi: https://doi.org/10.15866/irease.v8i1.5322[25] Kulkarni, S., Wagh, S., Singh, N., Challenges in Model Predictive Control Application for Transient Stability Improvement Using TCSC, (2015) International Review of Automatic Control (IREACO), 8 (2), pp. 163-169. doi: https://doi.org/10.15866/ireaco.v8i2.5562[26] P. Korba, M. Larsson, and C. Rehtanz, Detection of oscillations in power systems using kalman filtering techniques, in Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003. vol. 1, pp. 183–188 vol.1, June 2003. doi: https://doi.org/10.1109/CCA.2003.1223290[27] J. C. Peng and N. C. Nair, Comparative assessment of kalman filter and prony methods for power system oscillation monitoring, in 2009 IEEE Power Energy Society General Meeting, pp. 1–8, July 2009. doi: https://doi.org/10.1109/PES.2009.5275656[28] R. Chang and T. K. Saha, Improved extended complex kalman filter for identifying inter-area oscillations, in 2012 IEEE International Conference on Power System Technology (POWERCON), pp. 1–6, Oct 2012. doi: https://doi.org/10.1109/PowerCon.2012.6401278[29] J. Ma, T. Wang, Z. Wang, and J. S. 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Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, John Wiley & Sons, 2006Comunidad universitaria en generalPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://dspace7-uao.metacatalogo.com/bitstreams/6783bb02-1a6d-4ab3-a024-2923816f7402/download20b5ba22b1117f71589c7318baa2c560MD52ORIGINALA0269 Online dynamic assessment of system stability in power systems using the unscented kalman filter.pdfA0269 Online dynamic assessment of system stability in power systems using the unscented kalman filter.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf384844https://dspace7-uao.metacatalogo.com/bitstreams/9d6785e7-d7ea-4945-b3dc-4a7ff290d18a/download357f1189faa1b2847764c93625bcb560MD53TEXTA0269 Online dynamic assessment of system stability in power systems using the unscented kalman filter.pdf.txtA0269 Online dynamic assessment of system stability in power systems using the unscented kalman filter.pdf.txtExtracted texttext/plain37206https://dspace7-uao.metacatalogo.com/bitstreams/bde6000f-1635-4c5b-8d21-c09fb4936e1a/download18814025bad7540ea12db8f15db7a3a2MD54THUMBNAILA0269 Online dynamic assessment of system stability in power systems using the unscented kalman filter.pdf.jpgA0269 Online dynamic assessment of system stability in power systems using the unscented kalman filter.pdf.jpgGenerated Thumbnailimage/jpeg14207https://dspace7-uao.metacatalogo.com/bitstreams/8c30de8e-ca97-4984-81af-c22c003cf5fb/download5e491af7dc860a228aa1e90591ba8365MD5510614/12920oai:dspace7-uao.metacatalogo.com:10614/129202024-01-19 17:06:06.908https://creativecommons.org/licenses/by-nc-nd/4.0/open.accesshttps://dspace7-uao.metacatalogo.comRepositorio UAOrepositorio@uao.edu.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 |