Способы использования синхронизированных векторных измерений в задаче оценивания состояния электрической сети
Ключевые слова:
оценивание состояния, УСВИ, линейное оценивание состояния, векторные измерения, поиск плохих данных, метод взвешенных наименьших квадратов, весовые коэффициенты измерений
Аннотация
В статье представлен обзор публикаций на тему использования информации от устройств синхронизированных векторных измерений в алгоритмах оценивания состояния электрической сети. На основе метода взвешенных наименьших квадратов рассмотрены способы включения синхронизированных векторных измерений в задачу оценивания состояния, проанализированы преимущества и недостатки каждого представленного подхода. Большое внимание в статье уделяется перспективам совместного оценивания телеметрической информации и данных от устройств синхронизированных векторных измерений, а также выполнения оценивания состояния только на основе синхронизированных векторных измерений. Проанализированы вычислительные трудности и другие препятствия, возникающие при использовании синхронизированных векторных измерений в задаче оценивания состояния, представлены различные способы решения существующих проблем в данной области. В силу кардинального отличия в точности данных, предоставляемых устройствами синхронизированных векторных измерений и классической измерительной аппаратурой, разбираются особенности выбора весовых коэффициентов измерений в задаче оценивания состояния. Выполнен обзор существующих подходов к определению весовых коэффициентов для синхронизированных векторных измерений, в том числе путем их расчета на базе имеющихся статистических данных.
Литература
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.
