Ways of Using Phasor Measurements in Power System State Estimation
Keywords:
state estimation, PMU, linear state estimation, phasor measurements, bad data detection, weighted least squares method, measurement data weighting factors
Abstract
A review of publications on using data from phasor measurement units (PMU) in electric grid state estimation (SE) algorithms is presented. Ways of including phasor measurement data in the SE problem formulation are considered based on the weighted least squares method. The advantages and drawbacks of each of the presented approaches are analyzed. Much attention is paid to the prospects of simultaneously estimating telemetric information and PMU data, and performing SE based solely on the PMU data. The computational difficulties and other obstacles that arise in using phasor measurements in performing SE are analyzed, and various ways of solving existing problems in this field are presented. It is pointed out that the accuracy of data obtained from PMU devices differs essentially from that of data obtained from classic measurement instruments. This circumstance imposes certain specific features on selecting the weighting factors for phasor measurement data in solving the SE problem. The existing approaches to determining the weighting factors for phasor measurement data, in particular, by calculating them based on available statistical data, are reviewed.
References
1. Гамм А.З. Статистические методы оценивания состояния электроэнергетических систем. М.: Наука, 1976, 220 с.
2. Zhu J., Abur A. Bad Data Identification When Using Phasor Measurements. – IEEE Lausanne Power Tech, 2007, pp. 1676–1681, DOI: 10.1109/PCT.2007.4538567.
3. Chen J., Abur A. Improved Bad Data Processing Via Strategic Placement of PMUs. – IEEE Power Engineering Society General Meeting, San Francisco, USA, 2005, pp. 2759–2763. DOI: 10.1109/PES.2005.1489694.
4. Göl M., Abur A. LAV Based Robust State Estimation for Systems Measured by PMUs. – IEEE Transactions on Smart Grid, 2014, vol. 5, No. 4, pp. 1808–1814, DOI: 10.1109/TSG.2014.2302213.
5. Göl M., Abur A. A Hybrid State Estimator for Systems with Limited Number of PMUs. – IEEE Transactions on Power Systems, 2015, vol. 30, No. 3, pp. 1511–1517, DOI: 10.1109/TPWRS.2014.2344012.
6. Kolosok I.N., Korkina E.S., Mahnitko A.E. Detection of Systematic Errors in PMU Measurements by the Power System State Estimation Methods. – 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2015, DOI: 10.1109/RTUCON.2015.7343131.
7. Zhang J. et al. A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation. – IEEE Transactions on Sustainable Energy, 2014, vol. 5, No. 2, pp. 629–636, DOI: 10.1109/TSTE.2013.2280246.
8. Ghahremani E., Kamwa I. Dynamic State Estimation in Power System by Applying the Extended Kalman Filter with Unknown Inputs to Phasor Measurements. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 4, pp. 2556–2566, DOI: 10.1109/TPWRS.2011.2145396.
9. Zivanovic R., Cairns C. Implementation of PMU Technology in State Estimation: an Overview. – IEEE. AFRICON ’96, 1996, vol. 2, pp. 1006–1011, DOI: 10.1109/AFRCON.1996.563034.
10. Бартоломей П.И., Семененко С.И. Оптимизация состава традиционных и высокоточных синхронизированных векторных измерений для ускоренной оценки состояния ЭЭС. – Электроэнергия. Передача и Распределение, 2019, вып. 1 (52), c. 128–133.
11. Zhou M. et al. An Alternative for Including Phasor Measurements in State Estimators. – IEEE Transactions on Power Systems, 2006, vol. 21, No. 4, pp. 1930–1937, DOI: 10.1109/TPWRS. 2006.881112.
12. Phadke A.G. et al. Recent Developments in State Estimation with Phasor Measurements. – IEEE/PES Power Systems Conference and Exposition, Seattle, USA, 2009, DOI: 10.1109/PSCE.2009.4839954.
13. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part I: Architecture. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 46–53, DOI: 10.1109/TPWRS.2010.2050078.
14. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part II: Algorithm. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 54–62, DOI: 10.1109/TPWRS.2010.2050077.
15. Joshi P.M., Verma H. Synchrophasor Measurement Applications and Optimal PMU Placement: A review. – Electric Power Systems Research, 2021, No. 199, DOI: 10.1016/J.EPSR.2021.107428.
16. Zhu J., Abur A. Effect of Phasor Measurements on the Choice of Reference Bus for State Estimation – IEEE Power Engineering Society General Meeting, 2007, DOI: 10.1109/PES.2007.386175.
17. Abur A. Impact of Phasor Measurements on State Estimation. – International Conference on Electrical and Electronics Engineering (ELECO), 2009, DOI: 10.1109/ELECO.2009.5355207.
18. Ree J.D.L. et al. Synchronized Phasor Measurement Applications in Power Systems. – IEEE Transactions on Smart Grid, 2010, DOI: 10.1109/TSG.2010.2044815.
19. Abur A., Gomez-Exposito A. Power System State Estimation: Theory and Implementation, 2004, vol. 24, DOI: 10.1201/9780203913673.
20. De la Villa Jaén A. et al. Tuning of Measurement Weights in State Estimation: Theoretical Analysis and Case Study. – IEEE Transactions on Power Systems, 2018, vol. 33, No. 4, pp. 4583–4592, DOI: 10.1109/TPWRS.2017.2786403.
21. Zhong S., Abur A. State Estimator Tuning for PMU Measurements. –North American Power Symposium, 2011, DOI: 10.1109/NAPS.2011.6025196.
22. Zhong S., Abur A. Auto Tuning of Measurement Weights in WLS State Estimation. – IEEE Transactions on Power Systems, 2004, vol. 19, No. 4, pp. 2006–2013, DOI: 10.1109/TPWRS.2004.836182.
#
1. Gamm A.Z. Statisticheskie metody otsenivaniya sostoyaniya elektroenergeticheskih sistem (Statistical Methods for Assessing the State of Electric Power Systems). М.: Nauka, 1976, 220 p.
2. Zhu J., Abur A. Bad Data Identification When Using Phasor Measurements. – IEEE Lausanne Power Tech, 2007, pp. 1676–1681, DOI: 10.1109/PCT.2007.4538567.
3. Chen J., Abur A. Improved Bad Data Processing Via Strategic Placement of PMUs. – IEEE Power Engineering Society General Meeting, San Francisco, USA, 2005, pp. 2759–2763. DOI: 10.1109/PES.2005.1489694.
4. Göl M., Abur A. LAV Based Robust State Estimation for Systems Measured by PMUs. – IEEE Transactions on Smart Grid, 2014, vol. 5, No. 4, pp. 1808–1814, DOI: 10.1109/TSG.2014.2302213.
5. Göl M., Abur A. A Hybrid State Estimator for Systems with Limited Number of PMUs. – IEEE Transactions on Power Systems, 2015, vol. 30, No. 3, pp. 1511–1517, DOI: 10.1109/TPWRS.2014.2344012.
6. Kolosok I.N., Korkina E.S., Mahnitko A.E. Detection of Systematic Errors in PMU Measurements by the Power System State Estimation Methods. – 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2015, DOI: 10.1109/RTUCON.2015.7343131.
7. Zhang J. et al. A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation. – IEEE Transactions on Sustainable Energy, 2014, vol. 5, No. 2, pp. 629–636, DOI: 10.1109/TSTE.2013.2280246.
8. Ghahremani E., Kamwa I. Dynamic State Estimation in Power System by Applying the Extended Kalman Filter with Unknown Inputs to Phasor Measurements. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 4, pp. 2556–2566, DOI: 10.1109/TPWRS.2011.2145396.
9. Zivanovic R., Cairns C. Implementation of PMU Technology in State Estimation: an Overview. – IEEE. AFRICON ’96, 1996, vol. 2, pp. 1006–1011, DOI: 10.1109/AFRCON.1996.563034.
10. Bartolomey P.I., Semenenko S.I. Elektroenergiya. Peredacha i Raspredelenie – in Russ. (Electric Power. Transmission and Distri-bution), 2019, iss. 1 (52), pp. 128–133.
11 Zhou M. et al. An Alternative for Including Phasor Measure-ments in State Estimators. – IEEE Transactions on Power Systems, 2006, vol. 21, No. 4, pp. 1930–1937, DOI: 10.1109/TPWRS.2006.881112.
12. Phadke A.G. et al. Recent Developments in State Estimation with Phasor Measurements. – IEEE/PES Power Systems Conference and Exposition, Seattle, USA, 2009, DOI: 10.1109/PSCE.2009.4839954.
13. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part I: Architecture. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 46–53, DOI: 10.1109/TPWRS.2010.2050078.
14. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part II: Algorithm. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 54–62, DOI: 10.1109/TPWRS.2010.2050077.
15. Joshi P.M., Verma H. Synchrophasor Measurement Applications and Optimal PMU Placement: A review. – Electric Power Systems Research, 2021, No. 199, DOI: 10.1016/J.EPSR.2021.107428.
16. Zhu J., Abur A. Effect of Phasor Measurements on the Choice of Reference Bus for State Estimation – IEEE Power Engineering Society General Meeting, 2007, DOI: 10.1109/PES.2007.386175.
17. Abur A. Impact of Phasor Measurements on State Estimation. – International Conference on Electrical and Electronics Engineering (ELECO), 2009, DOI: 10.1109/ELECO.2009.5355207.
18. Ree J.D.L. et al. Synchronized Phasor Measurement Applications in Power Systems. – IEEE Transactions on Smart Grid, 2010, DOI: 10.1109/TSG.2010.2044815.
19. Abur A., Gomez-Exposito A. Power System State Estimation: Theory and Implementation, 2004, vol. 24, DOI: 10.1201/9780203913673.
20. De la Villa Jaén A. et al. Tuning of Measurement Weights in State Estimation: Theoretical Analysis and Case Study. – IEEE Transactions on Power Systems, 2018, vol. 33, No. 4, pp. 4583–4592, DOI: 10.1109/TPWRS.2017.2786403.
21. Zhong S., Abur A. State Estimator Tuning for PMU Measurements. – North American Power Symposium, 2011, DOI: 10.1109/NAPS.2011.6025196.
22. Zhong S., Abur A. Auto Tuning of Measurement Weights in WLS State Estimation. – IEEE Transactions on Power Systems, 2004, vol. 19, No. 4, pp. 2006–2013, DOI: 10.1109/TPWRS.2004.836182.
2. Zhu J., Abur A. Bad Data Identification When Using Phasor Measurements. – IEEE Lausanne Power Tech, 2007, pp. 1676–1681, DOI: 10.1109/PCT.2007.4538567.
3. Chen J., Abur A. Improved Bad Data Processing Via Strategic Placement of PMUs. – IEEE Power Engineering Society General Meeting, San Francisco, USA, 2005, pp. 2759–2763. DOI: 10.1109/PES.2005.1489694.
4. Göl M., Abur A. LAV Based Robust State Estimation for Systems Measured by PMUs. – IEEE Transactions on Smart Grid, 2014, vol. 5, No. 4, pp. 1808–1814, DOI: 10.1109/TSG.2014.2302213.
5. Göl M., Abur A. A Hybrid State Estimator for Systems with Limited Number of PMUs. – IEEE Transactions on Power Systems, 2015, vol. 30, No. 3, pp. 1511–1517, DOI: 10.1109/TPWRS.2014.2344012.
6. Kolosok I.N., Korkina E.S., Mahnitko A.E. Detection of Systematic Errors in PMU Measurements by the Power System State Estimation Methods. – 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2015, DOI: 10.1109/RTUCON.2015.7343131.
7. Zhang J. et al. A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation. – IEEE Transactions on Sustainable Energy, 2014, vol. 5, No. 2, pp. 629–636, DOI: 10.1109/TSTE.2013.2280246.
8. Ghahremani E., Kamwa I. Dynamic State Estimation in Power System by Applying the Extended Kalman Filter with Unknown Inputs to Phasor Measurements. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 4, pp. 2556–2566, DOI: 10.1109/TPWRS.2011.2145396.
9. Zivanovic R., Cairns C. Implementation of PMU Technology in State Estimation: an Overview. – IEEE. AFRICON ’96, 1996, vol. 2, pp. 1006–1011, DOI: 10.1109/AFRCON.1996.563034.
10. Бартоломей П.И., Семененко С.И. Оптимизация состава традиционных и высокоточных синхронизированных векторных измерений для ускоренной оценки состояния ЭЭС. – Электроэнергия. Передача и Распределение, 2019, вып. 1 (52), c. 128–133.
11. Zhou M. et al. An Alternative for Including Phasor Measurements in State Estimators. – IEEE Transactions on Power Systems, 2006, vol. 21, No. 4, pp. 1930–1937, DOI: 10.1109/TPWRS. 2006.881112.
12. Phadke A.G. et al. Recent Developments in State Estimation with Phasor Measurements. – IEEE/PES Power Systems Conference and Exposition, Seattle, USA, 2009, DOI: 10.1109/PSCE.2009.4839954.
13. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part I: Architecture. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 46–53, DOI: 10.1109/TPWRS.2010.2050078.
14. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part II: Algorithm. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 54–62, DOI: 10.1109/TPWRS.2010.2050077.
15. Joshi P.M., Verma H. Synchrophasor Measurement Applications and Optimal PMU Placement: A review. – Electric Power Systems Research, 2021, No. 199, DOI: 10.1016/J.EPSR.2021.107428.
16. Zhu J., Abur A. Effect of Phasor Measurements on the Choice of Reference Bus for State Estimation – IEEE Power Engineering Society General Meeting, 2007, DOI: 10.1109/PES.2007.386175.
17. Abur A. Impact of Phasor Measurements on State Estimation. – International Conference on Electrical and Electronics Engineering (ELECO), 2009, DOI: 10.1109/ELECO.2009.5355207.
18. Ree J.D.L. et al. Synchronized Phasor Measurement Applications in Power Systems. – IEEE Transactions on Smart Grid, 2010, DOI: 10.1109/TSG.2010.2044815.
19. Abur A., Gomez-Exposito A. Power System State Estimation: Theory and Implementation, 2004, vol. 24, DOI: 10.1201/9780203913673.
20. De la Villa Jaén A. et al. Tuning of Measurement Weights in State Estimation: Theoretical Analysis and Case Study. – IEEE Transactions on Power Systems, 2018, vol. 33, No. 4, pp. 4583–4592, DOI: 10.1109/TPWRS.2017.2786403.
21. Zhong S., Abur A. State Estimator Tuning for PMU Measurements. –North American Power Symposium, 2011, DOI: 10.1109/NAPS.2011.6025196.
22. Zhong S., Abur A. Auto Tuning of Measurement Weights in WLS State Estimation. – IEEE Transactions on Power Systems, 2004, vol. 19, No. 4, pp. 2006–2013, DOI: 10.1109/TPWRS.2004.836182.
#
1. Gamm A.Z. Statisticheskie metody otsenivaniya sostoyaniya elektroenergeticheskih sistem (Statistical Methods for Assessing the State of Electric Power Systems). М.: Nauka, 1976, 220 p.
2. Zhu J., Abur A. Bad Data Identification When Using Phasor Measurements. – IEEE Lausanne Power Tech, 2007, pp. 1676–1681, DOI: 10.1109/PCT.2007.4538567.
3. Chen J., Abur A. Improved Bad Data Processing Via Strategic Placement of PMUs. – IEEE Power Engineering Society General Meeting, San Francisco, USA, 2005, pp. 2759–2763. DOI: 10.1109/PES.2005.1489694.
4. Göl M., Abur A. LAV Based Robust State Estimation for Systems Measured by PMUs. – IEEE Transactions on Smart Grid, 2014, vol. 5, No. 4, pp. 1808–1814, DOI: 10.1109/TSG.2014.2302213.
5. Göl M., Abur A. A Hybrid State Estimator for Systems with Limited Number of PMUs. – IEEE Transactions on Power Systems, 2015, vol. 30, No. 3, pp. 1511–1517, DOI: 10.1109/TPWRS.2014.2344012.
6. Kolosok I.N., Korkina E.S., Mahnitko A.E. Detection of Systematic Errors in PMU Measurements by the Power System State Estimation Methods. – 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2015, DOI: 10.1109/RTUCON.2015.7343131.
7. Zhang J. et al. A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation. – IEEE Transactions on Sustainable Energy, 2014, vol. 5, No. 2, pp. 629–636, DOI: 10.1109/TSTE.2013.2280246.
8. Ghahremani E., Kamwa I. Dynamic State Estimation in Power System by Applying the Extended Kalman Filter with Unknown Inputs to Phasor Measurements. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 4, pp. 2556–2566, DOI: 10.1109/TPWRS.2011.2145396.
9. Zivanovic R., Cairns C. Implementation of PMU Technology in State Estimation: an Overview. – IEEE. AFRICON ’96, 1996, vol. 2, pp. 1006–1011, DOI: 10.1109/AFRCON.1996.563034.
10. Bartolomey P.I., Semenenko S.I. Elektroenergiya. Peredacha i Raspredelenie – in Russ. (Electric Power. Transmission and Distri-bution), 2019, iss. 1 (52), pp. 128–133.
11 Zhou M. et al. An Alternative for Including Phasor Measure-ments in State Estimators. – IEEE Transactions on Power Systems, 2006, vol. 21, No. 4, pp. 1930–1937, DOI: 10.1109/TPWRS.2006.881112.
12. Phadke A.G. et al. Recent Developments in State Estimation with Phasor Measurements. – IEEE/PES Power Systems Conference and Exposition, Seattle, USA, 2009, DOI: 10.1109/PSCE.2009.4839954.
13. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part I: Architecture. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 46–53, DOI: 10.1109/TPWRS.2010.2050078.
14. Yang T., Sun H., Bose A. Transition to a Two-Level Linear State Estimator. Part II: Algorithm. – IEEE Transactions on Power Systems, 2011, vol. 26, No. 1, pp. 54–62, DOI: 10.1109/TPWRS.2010.2050077.
15. Joshi P.M., Verma H. Synchrophasor Measurement Applications and Optimal PMU Placement: A review. – Electric Power Systems Research, 2021, No. 199, DOI: 10.1016/J.EPSR.2021.107428.
16. Zhu J., Abur A. Effect of Phasor Measurements on the Choice of Reference Bus for State Estimation – IEEE Power Engineering Society General Meeting, 2007, DOI: 10.1109/PES.2007.386175.
17. Abur A. Impact of Phasor Measurements on State Estimation. – International Conference on Electrical and Electronics Engineering (ELECO), 2009, DOI: 10.1109/ELECO.2009.5355207.
18. Ree J.D.L. et al. Synchronized Phasor Measurement Applications in Power Systems. – IEEE Transactions on Smart Grid, 2010, DOI: 10.1109/TSG.2010.2044815.
19. Abur A., Gomez-Exposito A. Power System State Estimation: Theory and Implementation, 2004, vol. 24, DOI: 10.1201/9780203913673.
20. De la Villa Jaén A. et al. Tuning of Measurement Weights in State Estimation: Theoretical Analysis and Case Study. – IEEE Transactions on Power Systems, 2018, vol. 33, No. 4, pp. 4583–4592, DOI: 10.1109/TPWRS.2017.2786403.
21. Zhong S., Abur A. State Estimator Tuning for PMU Measurements. – North American Power Symposium, 2011, DOI: 10.1109/NAPS.2011.6025196.
22. Zhong S., Abur A. Auto Tuning of Measurement Weights in WLS State Estimation. – IEEE Transactions on Power Systems, 2004, vol. 19, No. 4, pp. 2006–2013, DOI: 10.1109/TPWRS.2004.836182.
Published
2023-04-13
Issue
Section
Article
