About the Validity of Measurements in an Electric Power System with Electric Energy Storages
Keywords:
electric energy storages, electric power system,, measurements, gross errors
Abstract
The validity and completeness of data in an electric power system are achieved by state estimation, which is aimed at filtering measurement errors and calculating unmeasured operating parameters. Highquality estimates can be obtained if there are no gross errors in measurements, which are revealedand eliminated at the stage of detecting erroneous data, provided the measurements are redundant. With the advent of renewable energy sources, many new facilities have been integrated into the power system, which are not fully equipped with measuring devices, due to which the parameters of such facilities are measured with insufficient redundancy. Under such conditions, and especially when it is difficult to predict the power output produced by stochastic and intermittent sources (wind and solar energy), the conventional validation methods do not always work in a proper way. A new method for revealing erroneous data is proposed, which is based on analyzing the strategy for controlling a storage battery in a power system containing wind farms. A fivenode test system containing a wind farm and a highcapacity storage battery with a simple control strategy (load switching) is considered. It is shown that by using the proposed method it is possible to detect gross errors in active power measurements under the conditions of low information redundancy.
References
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#
1. Abur A, Exposito A.G. Power system state estimation: theory nd implementation. New York: Marcel Dekker, 2004, 327 p.
2. Heydt G.T. The next generation of power distribution systems. – IEEE Transactions on Smart Grid. 2010, vol. 1, No. 3, pp. 225–235.
3. Yelistratov V.V. Vozobnovlyayemaya energetika – in Russ. (Proceeded in energy) – SPb.: Publ. St. Petersburg Polytechnic University of Peter Great, 2016, 424 p.
4. Kucherov Yu.N., Berezovskii P.K. Sovremennyy uroven’ razvitiya tekhnologii nakopleniya elektricheskoy energii i funktsional’nyye usloviya ikh primeneniya v energosisteme. Opyt SIGRE (The current level of development of technology for the accumulation of electric energy and the functional conditions of their use in the power system. CIGRE experience) [Electron. Resourse] www.soups.ru, 2014 (Data of appeal 23.01.2020).
5. Primadianto A., Lu C.N. A review on distribution system state estimation. – IEEE Transactions on Power System. 2016, vol. 32, No. 5, pp. 3875–3883.
6. Singh D., Pandey J., Chauhan D. Topology identification, bad data processing, and state estimation using fuzzy pattern matching. – IEEE Transactions on Power Systems. 2005, vol. 20, No. 3, pp. 1570–1579.
7. Gamm A.Z., Kucherov YU.N. Palamarchuk S.I. et al. Metody resheniya zadach real’nogo vremeni v elektroenergetike (Methods for solving realtime problems in the electric power industry). Novosibirsk. Nauka, 1991, 293 p.
8. Clanents K.A., Krutnpholz G.R., Davis P.W. Power System State Estimation with Measurement Deficiency: an Observability/Measurement Placement Algorithm. – IEEE Transactions on Power Apparatus and Systems. 1983, vol. PAS102, No. 7, pp. 2012–2020.
9. Gamm A.Z. Statisticheskiye metody otsenivaniya sostoyaniya elektroenergeticheskikh system (Statistical methods for assessing the state of electric power systems). Moscow, Nauka,1976, 220 p.
10. Glazunova A.M., Kolosok I.N., Korkina E.C. Study of Test Equations Method’s Application for Bad Data Detection in PMU Measurement. – PMAPS 2012, Istanbul, Turkey, June 10–14.
11. Murat Gol, Abur Ali. A modified chi–squares test for improved bad data detection – The International Conference PowerTech. 2015, Eindhoven, Netherlands.
12. Yun Yang, Wei Hu, Yong Min. Projected unscented Kalman filter for dynamic state estimation and bad data detection in power system. – 12th IET International conference on Developments in Power System Protection (DPSP), 2014.
13. Do Coutto Filho M.B., Stacchini de Souza J.C., Freund R.S. Forecastingaided state estimation – Part 2: Implementation. – IEEE Transactions on Power System, 2009, vol. 24, No. 4, pp. 1678–1685.
14. Describing Wind Variations: Weibull Distribution [Electron. Resourse] http://xn—drmstrre64ad.dk/wpcontent/wind/miller/windpower%20web/en/tour/wres/weibull.htm (Data of appeal 23.01.2020].
15. Semshchikov E., Hamilton J., Wu L., Negnevitsky M., Wang X., Lyden S. Frequency control within high renewable penetration hybrid systems adopting low load diesel methodologies. – Energy Procedia, 2019.
16. Vidyanandan K.V., Senroy N. Primary frequency regulation by deloaded wind turbines using variable droop. – IEEE Transactions on Power Systems. 2013, vol. 28, No. 2, pp. 837–846.
17. Glazunova A., Aksaeva E. An Increase in Information Security of Electric Power System with Wind Power Penetration under Low Redundancy of Measurements. – Proc. of the Intern. Conf. PowerTech. Milan 2019, 23–28 July.
2. Heydt G.T. The next generation of power distribution systems.– IEEE Transactions on Smart Grid. 2010, vol. 1, No. 3, pp.225–235.
3. Елистратов В.В. Возобновляемая энергетика. СанктПетербург: Издво СанктПетербургского политехнического университета Петра Великого, 2016, 424 c.
4. Кучеров Ю.Н., Березовский П.К. Современный уровень развития технологии накопления электрической энергии и функциональные условия их применения в энергосистеме. Опыт СИГРЭ [Электрон. ресурс] www.soups.ru, 2014 (Дата обращения 23.01.2020)
5. Primadianto A., Lu C.N. A review on distribution system state estimation. – IEEE Transactions on Power System. 2016, vol. 32, No. 5, pp. 3875–3883.
6. Singh D., Pandey J., Chauhan D. Topology identification, bad data processing, and state estimation using fuzzy pattern matching. – IEEE Transactions on Power Systems. 2005, vol. 20, No. 3, pp. 1570–1579.
7. Гамм А.З., Кучеров Ю.Н., Паламарчук С.И. и др. Методы решения задач реального времени в электроэнергетике. Новосибирск: Наука, 1991, 293 p.
8. Clanents K.A., Krutnpholz G.R., Davis P.W. Power System State Estimation with Measurement Deficiency: an Observability/Measurement Placement Algorithm. – IEEE Transactions on Power Apparatus and Systems. 1983, vol. PAS102, No. 7, pp. 2012–2020.
9. Гамм А.З. Статистические методы оценивания состояния электроэнергетических систем. М.: Наука, 1976, 220 c.
10. Glazunova A.M., Kolosok I.N., Korkina E.C. Study of Test Equations Method’s Application for Bad Data Detection in PMU Measurement. – PMAPS 2012, Istanbul, Turkey, June 10–14.
11. Murat Gol, Abur Ali. A modified chi–squares test for improved bad data detection – The International Conference PowerTech. 2015, Eindhoven, Netherlands.
12. Yun Yang, Wei Hu, Yong Min. Projected unscented Kalman filter for dynamic state estimation and bad data detection in power system. – 12th IET International conference on Developments in Power System Protection (DPSP), 2014.
13. Do Coutto Filho M.B., Stacchini de Souza J.C., Freund R.S. Forecastingaided state estimation – Part 2: Implementation. – IEEE Transactions on Power System, 2009, vol. 24, No. 4, pp. 1678–1685.
14. Describing Wind Variations: Weibull Distribution [Электрон. ресурс] http://xn—drmstrre64ad.dk/wpcontent/wind/miller/windpower%20web/en/tour/wres/weibull.htm (Дата обращения 23.01.2020].
15. Semshchikov E., Hamilton J., Wu L., Negnevitsky M., Wang X., Lyden S. Frequency control within high renewable penetration hybrid systems adopting low load diesel methodologies. – Energy Procedia, 2019.
16. Vidyanandan K.V., Senroy N. Primary frequency regulation by deloaded wind turbines using variable droop. – IEEE Transactions on Power Systems. 2013, vol. 28, No. 2, pp. 837–846.
17. Glazunova A., Aksaeva E. An Increase in Information Security of Electric Power System with Wind Power Penetration under Low Redundancy of Measurements. – Proc. of the Intern. Conf. PowerTech. Milan 2019, 23–28 July.
#
1. Abur A, Exposito A.G. Power system state estimation: theory nd implementation. New York: Marcel Dekker, 2004, 327 p.
2. Heydt G.T. The next generation of power distribution systems. – IEEE Transactions on Smart Grid. 2010, vol. 1, No. 3, pp. 225–235.
3. Yelistratov V.V. Vozobnovlyayemaya energetika – in Russ. (Proceeded in energy) – SPb.: Publ. St. Petersburg Polytechnic University of Peter Great, 2016, 424 p.
4. Kucherov Yu.N., Berezovskii P.K. Sovremennyy uroven’ razvitiya tekhnologii nakopleniya elektricheskoy energii i funktsional’nyye usloviya ikh primeneniya v energosisteme. Opyt SIGRE (The current level of development of technology for the accumulation of electric energy and the functional conditions of their use in the power system. CIGRE experience) [Electron. Resourse] www.soups.ru, 2014 (Data of appeal 23.01.2020).
5. Primadianto A., Lu C.N. A review on distribution system state estimation. – IEEE Transactions on Power System. 2016, vol. 32, No. 5, pp. 3875–3883.
6. Singh D., Pandey J., Chauhan D. Topology identification, bad data processing, and state estimation using fuzzy pattern matching. – IEEE Transactions on Power Systems. 2005, vol. 20, No. 3, pp. 1570–1579.
7. Gamm A.Z., Kucherov YU.N. Palamarchuk S.I. et al. Metody resheniya zadach real’nogo vremeni v elektroenergetike (Methods for solving realtime problems in the electric power industry). Novosibirsk. Nauka, 1991, 293 p.
8. Clanents K.A., Krutnpholz G.R., Davis P.W. Power System State Estimation with Measurement Deficiency: an Observability/Measurement Placement Algorithm. – IEEE Transactions on Power Apparatus and Systems. 1983, vol. PAS102, No. 7, pp. 2012–2020.
9. Gamm A.Z. Statisticheskiye metody otsenivaniya sostoyaniya elektroenergeticheskikh system (Statistical methods for assessing the state of electric power systems). Moscow, Nauka,1976, 220 p.
10. Glazunova A.M., Kolosok I.N., Korkina E.C. Study of Test Equations Method’s Application for Bad Data Detection in PMU Measurement. – PMAPS 2012, Istanbul, Turkey, June 10–14.
11. Murat Gol, Abur Ali. A modified chi–squares test for improved bad data detection – The International Conference PowerTech. 2015, Eindhoven, Netherlands.
12. Yun Yang, Wei Hu, Yong Min. Projected unscented Kalman filter for dynamic state estimation and bad data detection in power system. – 12th IET International conference on Developments in Power System Protection (DPSP), 2014.
13. Do Coutto Filho M.B., Stacchini de Souza J.C., Freund R.S. Forecastingaided state estimation – Part 2: Implementation. – IEEE Transactions on Power System, 2009, vol. 24, No. 4, pp. 1678–1685.
14. Describing Wind Variations: Weibull Distribution [Electron. Resourse] http://xn—drmstrre64ad.dk/wpcontent/wind/miller/windpower%20web/en/tour/wres/weibull.htm (Data of appeal 23.01.2020].
15. Semshchikov E., Hamilton J., Wu L., Negnevitsky M., Wang X., Lyden S. Frequency control within high renewable penetration hybrid systems adopting low load diesel methodologies. – Energy Procedia, 2019.
16. Vidyanandan K.V., Senroy N. Primary frequency regulation by deloaded wind turbines using variable droop. – IEEE Transactions on Power Systems. 2013, vol. 28, No. 2, pp. 837–846.
17. Glazunova A., Aksaeva E. An Increase in Information Security of Electric Power System with Wind Power Penetration under Low Redundancy of Measurements. – Proc. of the Intern. Conf. PowerTech. Milan 2019, 23–28 July.
