Выборочный контроль показателей качества электроэнергии в распределительных сетях с большой долей генерации на основе возобновляемых источников энергии
Ключевые слова:
возобновляемые источники энергии, показатели качества электроэнергии, пространственный вектор, коэффициент взаимной корреляции, имитационное моделирование
Аннотация
Важную роль в достижении углеродной нейтральности играет развитие генерации на основе возобновляемых источников энергии (ВИЭ). Современные солнечные и ветровые электростанции присоединяются к распределительным электрическим сетям через инверторы, в которых реализованы алгоритмы управления выдачей мощности и защиты. При низкой загрузке инверторов в сетях фиксируются превышения нормируемых уровней показателей качества электрической энергии (ПКЭ): отклонений, колебаний, провалов и несинусоидальности напряжения. Это имеет место при низком уровне солнечной радиации на солнечных электростанциях и небольших ветровых напорах на ветровых электростанциях. Как правило, в составе промышленных потребителей, подключенных к таким распределительным сетям, имеются электроприемники, для которых отклонения ПКЭ от нормируемых значений критичны и приводят к их отключению защитами, останову технологических процессов, ущербам от брака и недоотпуска продукции. Для выявления источников возмущений и предъявления исков с целью компенсации ущербов промышленные потребители внедряют различные системы контроля ПКЭ. В статье рассмотрен метод выборочного контроля для автоматического выявления превышения нормативных значений ПКЭ. Для учета комплексного воздействия ПКЭ на электроприемники предложено использовать модуль коэффициента взаимной корреляции токов и напряжений. Рассмотрена структурная схема устройства контроля ПКЭ, в основу которой положены результаты имитационного моделирования различных режимов работы сети, а также процедура последовательного анализа Вальда. Внедрение данного устройства позволит предотвращать значительные и длительные превышения ПКЭ, обеспечивая надежную работу промышленных потребителей в распределительных сетях с большой долей генерации на основе ВИЭ.
Литература
1. Dong F., et al. Towards carbon neutrality: the impact of renewable energy development on carbon emission efficiency. – International Journal of Environmental Research and Public Health, 2021, vol. 18, No. 24, 13284.
2. Zhang H. Technology Innovation, Economic Growth and Carbon Emissions in the Context of Carbon Neutrality: Evidence from BRICS. – Sustainability, 2021, vol. 13, No. 20, 11138, DOI:10.3390/su132011138.
3. Iin J., et al. BRICS Carbon Neutrality Target: Measuring the Impact of Electricity Production from Renewable Energy Sources and Globalization. – Journal of Environmental Management, 2021, vol. 298, 113460, DOI:10.1016/j.jenvman.2021.113460.
4. Ilyushin P.V., et al. Consideration of Distinguishing Design Features of Gas-Turbine and Gas-Reciprocating Units in Design of Emergency Control Systems. – Machines, 2021, 9(3), 47, DOI:10.3390/machines9030047.
5. Praiselin W.J., Edward J.B. A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation. – International Journal of Intelligent Systems and Applications, 2018, vol. 10, No. 3, pp. 67–81, DOI:10.5815/IJISA.2018.03.08.
6. ГОСТ 32144-2013. Электрическая энергия. Совместимость технических средств электромагнитная. Нормы качества электрической энергии в системах электроснабжения общего назначения. М.: Стандартинформ. 2014, 16 с.
7. Burmeyster M.V., et al. Study and analysis of the influence of wind power station on the power quality. –2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering, 2020, p. 9059105, DOI:10.1109/REEPE49198.2020.9059105.
8. Huang J.S., Negnevitsky M., Nguyen D.T. A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances. – IEEE Transactions on Power Delivery, 2002, vol. 17, No. 2, pp. 609–616, DOI:10.1109/PESW.2002.985141.
9. Ghosh A.K., Lubkeman D.L. The Classification of Power System Disturbance Waveforms Using a Neural Network Approach. – IEEE Transactions on Power Delivery, 1990, vol. 10, pp. 671–683, DOI:10.1109/TDC.1994.328398.
10. Таваров С.Ш. Алгоритм обучения искусственной нейронной сети для факторного прогнозирования электропотребления бытового сектора. – Электричество, 2022, № 3, с. 30–38.
11. Balouji E., Salor O. Classification of Power Quality Events Using Deep Learning on Event Images. – IPRIA, 2017, pp. 216–221, DOI:10.1109/PRIA.2017.7983049.
12. Ma J., et al. Classification of Power Quality Disturbances Via Deep Learning, – Journal of IETE, Technical Review, 2017, vol. 34, No. 4, pp. 408–415, DOI:10.1080/02564602.2016.1196620.
13. Zhao F., Yang R. Power Quality Disturbance Recognition Using S-Transform. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 2, pp. 944–950, DOI:10.1109/TPWRD.2006.881575.
14. Axelberg P.G.V., Gu I.Y.H., Bollen M.H.J. Support Vector Machine for Classification of Voltage Disturbances. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 3, pp. 1297–1303, DOI:10.1109/TPWRD.2007.900065.
15. Bagheri A., Bollen M.H.J., Gu I.Y.H. Improved Characterization of Multistage Voltage Dips Based on the Space Phasor Model. – Electric Power Systems Research, 2018, vol. 154, pp. 319–328, DOI:10.1016/J.EPSR.2017.09.004.
16. Ribeiro P.F., et al. Power Systems Signal Processing for Smart Grids. John Wiley & Sons Ltd, 2014, 442 p.
17. Grus J. Data Science from Scratch: First Principles with Python. O’Reilly Media, Inc., 2015, 330 p.
18. Ignatova V., Granjon P., Bacha S. Space Vector Method for Voltage Dips and Swells Analysis. – IEEE Transactions on Power Delivery, 2009, vol. 24, No. 4, pp. 2054–2061, DOI:10.1109/TPWRD.2009.2028787.
19. Wang Y., et al. Single-Event Characteristics for Voltage Dips in Three-Phase Systems. – IEEE Transactions on Power Delivery, 2017, vol. 32, No. 2, pp. 832–840, DOI:10.1109/TPWRD.2016.2574924.
20. Liu H., et al. A Power Quality Disturbance Classification Method Based on Park Transform and Clarke Transform Analysis. – 2008 3rd International Conference on Innovative Computing Information and Control, 2008, DOI: 10.1109/ICICIC.2008.77.
21. Bagheri A., et al. A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification. – IEEE Transactions on Power Delivery, 2018, vol. 33, iss. 6, рр. 2794–2802.
22. Радиоэлектронные системы: основы построения и теория. Справочник / под ред. Я.Д. Ширмана. М.: Радиотехника, 2007, 512 с.
23. Фальшина В.А., Куликов А.Л. Алгоритмы упрощенной цифровой фильтрации электрических сигналов промышленной частоты. – Промышленная энергетика, 2012, № 5, с. 39–46.
24. Вальд А. Последовательный анализ. М.: Физматгиз, 1960, 328 с.
25. ISO 2859-1:1999. Sampling Procedures for Inspection by Attributes. Part 1: Sampling Schemes Indexed by Acceptance Quality Limit (AQL) for Lot-by-Lot Inspection, 1999, 87 p.
#
1. Dong F., et al. Towards carbon neutrality: the impact of renewable energy development on carbon emission efficiency. – International Journal of Environmental Research and Public Health, 2021, vol. 18, No. 24, 13284.
2. Zhang H. Technology Innovation, Economic Growth and Carbon Emissions in the Context of Carbon Neutrality: Evidence from BRICS. – Sustainability, 2021, vol. 13, No. 20, 11138, DOI:10.3390/su132011138.
3. Iin J., et al. BRICS Carbon Neutrality Target: Measuring the Impact of Electricity Production from Renewable Energy Sources and Globalization. – Journal of Environmental Management, 2021, vol. 298, 113460, DOI:10.1016/j.jenvman.2021.113460.
4. Ilyushin P.V., et al. Consideration of Distinguishing Design Features of Gas-Turbine and Gas-Reciprocating Units in Design of Emergency Control Systems. – Machines, 2021, 9(3), 47, DOI:10.3390/machines9030047.
5. Praiselin W.J., Edward J.B. A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation. – International Journal of Intelligent Systems and Applications, 2018, vol. 10, No. 3, pp. 67–81, DOI:10.5815/IJISA.2018.03.08.
6. GOST 32144-2013. Elektricheskaya energiya. Sovmestimost' tekhnicheskikh sredstv elektromagnitnaya. Normy kachestva elektricheskoy energii v sistemakh elektrosnabzheniya obshchego naznacheniya (Electric Energy. Electromagnetic Compatibility of Technical Equipment. Power Quality Limits in the Public Power Supply Systems). M.: Standartinform, 2014, 16 p.
7. Burmeyster M.V., et al. Study and analysis of the influence of wind power station on the power quality. –2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering, 2020, p. 9059105, DOI:10.1109/REEPE49198.2020.9059105.
8. Huang J.S., Negnevitsky M., Nguyen D.T. A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances. – IEEE Transactions on Power Delivery, 2002, vol. 17, No. 2, pp. 609–616, DOI:10.1109/PESW.2002.985141.
9. Ghosh A.K., Lubkeman D.L. The Classification of Power System Disturbance Waveforms Using a Neural Network Approach. – IEEE Transactions on Power Delivery, 1990, vol. 10, pp. 671–683, DOI:10.1109/TDC.1994.328398.
10. Tavarov S.Sh. Elektrichestvo – in Russ. (Electricity), 2022, No. 3, pp. 30–38.
11. Balouji E., Salor O. Classification of Power Quality Events Using Deep Learning on Event Images. – IPRIA, 2017, pp. 216–221, DOI:10.1109/PRIA.2017.7983049.
12. Ma J., et al. Classification of Power Quality Disturbances Via Deep Learning, – Journal of IETE, Technical Review, 2017, vol. 34, No. 4, pp. 408–415, DOI:10.1080/02564602.2016.1196620.
13. Zhao F., Yang R. Power Quality Disturbance Recognition Using S-Transform. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 2, pp. 944–950, DOI:10.1109/TPWRD.2006.881575.
14. Axelberg P.G.V., Gu I.Y.H., Bollen M.H.J. Support Vector Machine for Classification of Voltage Disturbances. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 3, pp. 1297–1303, DOI:10.1109/TPWRD.2007.900065.
15. Bagheri A., Bollen M.H.J., Gu I.Y.H. Improved Characterization of Multistage Voltage Dips Based on the Space Phasor Model. – Electric Power Systems Research, 2018, vol. 154, pp. 319–328, DOI:10.1016/J.EPSR.2017.09.004.
16. Ribeiro P.F., et al. Power Systems Signal Processing for Smart Grids. John Wiley & Sons Ltd, 2014, 442 p.
17. Grus J. Data Science from Scratch: First Principles with Python. O’Reilly Media, Inc., 2015, 330 p.
18. Ignatova V., Granjon P., Bacha S. Space Vector Method for Voltage Dips and Swells Analysis. – IEEE Transactions on Power Delivery, 2009, vol. 24, No. 4, pp. 2054–2061, DOI:10.1109/TPWRD.2009.2028787.
19. Wang Y., et al. Single-Event Characteristics for Voltage Dips in Three-Phase Systems. – IEEE Transactions on Power Delivery, 2017, vol. 32, No. 2, pp. 832–840, DOI:10.1109/TPWRD.2016.2574924.
20. Liu H., et al. A Power Quality Disturbance Classification Method Based on Park Transform and Clarke Transform Analysis. – 2008 3rd International Conference on Innovative Computing Information and Control, 2008, DOI: 10.1109/ICICIC.2008.77.
21. Bagheri A., et al. A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification. – IEEE Transactions on Power Delivery, 2018, vol. 33, iss. 6, рр. 2794–2802.
22. Radioelektronnye sistemy: osnovy postroeniya i teoriya. Spravochnik (Radio-Electronic Systems: Fundamentals of Construction and Theory. Guide) / Under ed. Ya.D. Shirman. М.: Radiotekhnika, 2007, 512 р.
23. Fal'shina V.A., Kulikov A.L. Promyshlennaya energetika – in Russ. (Industrial Power Engineering), 2012, No. 5, pp. 39–46.
24. Val'd A. Posledovatel'nyy analiz (Sequential Analysis). М.: Fizmatgiz, 1960, 328 p.
25. ISO 2859-1:1999. Sampling Procedures for Inspection by Attributes. Part 1: Sampling Schemes Indexed by Acceptance Quality Limit (AQL) for Lot-by-Lot Inspection, 1999, 87 p.
2. Zhang H. Technology Innovation, Economic Growth and Carbon Emissions in the Context of Carbon Neutrality: Evidence from BRICS. – Sustainability, 2021, vol. 13, No. 20, 11138, DOI:10.3390/su132011138.
3. Iin J., et al. BRICS Carbon Neutrality Target: Measuring the Impact of Electricity Production from Renewable Energy Sources and Globalization. – Journal of Environmental Management, 2021, vol. 298, 113460, DOI:10.1016/j.jenvman.2021.113460.
4. Ilyushin P.V., et al. Consideration of Distinguishing Design Features of Gas-Turbine and Gas-Reciprocating Units in Design of Emergency Control Systems. – Machines, 2021, 9(3), 47, DOI:10.3390/machines9030047.
5. Praiselin W.J., Edward J.B. A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation. – International Journal of Intelligent Systems and Applications, 2018, vol. 10, No. 3, pp. 67–81, DOI:10.5815/IJISA.2018.03.08.
6. ГОСТ 32144-2013. Электрическая энергия. Совместимость технических средств электромагнитная. Нормы качества электрической энергии в системах электроснабжения общего назначения. М.: Стандартинформ. 2014, 16 с.
7. Burmeyster M.V., et al. Study and analysis of the influence of wind power station on the power quality. –2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering, 2020, p. 9059105, DOI:10.1109/REEPE49198.2020.9059105.
8. Huang J.S., Negnevitsky M., Nguyen D.T. A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances. – IEEE Transactions on Power Delivery, 2002, vol. 17, No. 2, pp. 609–616, DOI:10.1109/PESW.2002.985141.
9. Ghosh A.K., Lubkeman D.L. The Classification of Power System Disturbance Waveforms Using a Neural Network Approach. – IEEE Transactions on Power Delivery, 1990, vol. 10, pp. 671–683, DOI:10.1109/TDC.1994.328398.
10. Таваров С.Ш. Алгоритм обучения искусственной нейронной сети для факторного прогнозирования электропотребления бытового сектора. – Электричество, 2022, № 3, с. 30–38.
11. Balouji E., Salor O. Classification of Power Quality Events Using Deep Learning on Event Images. – IPRIA, 2017, pp. 216–221, DOI:10.1109/PRIA.2017.7983049.
12. Ma J., et al. Classification of Power Quality Disturbances Via Deep Learning, – Journal of IETE, Technical Review, 2017, vol. 34, No. 4, pp. 408–415, DOI:10.1080/02564602.2016.1196620.
13. Zhao F., Yang R. Power Quality Disturbance Recognition Using S-Transform. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 2, pp. 944–950, DOI:10.1109/TPWRD.2006.881575.
14. Axelberg P.G.V., Gu I.Y.H., Bollen M.H.J. Support Vector Machine for Classification of Voltage Disturbances. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 3, pp. 1297–1303, DOI:10.1109/TPWRD.2007.900065.
15. Bagheri A., Bollen M.H.J., Gu I.Y.H. Improved Characterization of Multistage Voltage Dips Based on the Space Phasor Model. – Electric Power Systems Research, 2018, vol. 154, pp. 319–328, DOI:10.1016/J.EPSR.2017.09.004.
16. Ribeiro P.F., et al. Power Systems Signal Processing for Smart Grids. John Wiley & Sons Ltd, 2014, 442 p.
17. Grus J. Data Science from Scratch: First Principles with Python. O’Reilly Media, Inc., 2015, 330 p.
18. Ignatova V., Granjon P., Bacha S. Space Vector Method for Voltage Dips and Swells Analysis. – IEEE Transactions on Power Delivery, 2009, vol. 24, No. 4, pp. 2054–2061, DOI:10.1109/TPWRD.2009.2028787.
19. Wang Y., et al. Single-Event Characteristics for Voltage Dips in Three-Phase Systems. – IEEE Transactions on Power Delivery, 2017, vol. 32, No. 2, pp. 832–840, DOI:10.1109/TPWRD.2016.2574924.
20. Liu H., et al. A Power Quality Disturbance Classification Method Based on Park Transform and Clarke Transform Analysis. – 2008 3rd International Conference on Innovative Computing Information and Control, 2008, DOI: 10.1109/ICICIC.2008.77.
21. Bagheri A., et al. A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification. – IEEE Transactions on Power Delivery, 2018, vol. 33, iss. 6, рр. 2794–2802.
22. Радиоэлектронные системы: основы построения и теория. Справочник / под ред. Я.Д. Ширмана. М.: Радиотехника, 2007, 512 с.
23. Фальшина В.А., Куликов А.Л. Алгоритмы упрощенной цифровой фильтрации электрических сигналов промышленной частоты. – Промышленная энергетика, 2012, № 5, с. 39–46.
24. Вальд А. Последовательный анализ. М.: Физматгиз, 1960, 328 с.
25. ISO 2859-1:1999. Sampling Procedures for Inspection by Attributes. Part 1: Sampling Schemes Indexed by Acceptance Quality Limit (AQL) for Lot-by-Lot Inspection, 1999, 87 p.
#
1. Dong F., et al. Towards carbon neutrality: the impact of renewable energy development on carbon emission efficiency. – International Journal of Environmental Research and Public Health, 2021, vol. 18, No. 24, 13284.
2. Zhang H. Technology Innovation, Economic Growth and Carbon Emissions in the Context of Carbon Neutrality: Evidence from BRICS. – Sustainability, 2021, vol. 13, No. 20, 11138, DOI:10.3390/su132011138.
3. Iin J., et al. BRICS Carbon Neutrality Target: Measuring the Impact of Electricity Production from Renewable Energy Sources and Globalization. – Journal of Environmental Management, 2021, vol. 298, 113460, DOI:10.1016/j.jenvman.2021.113460.
4. Ilyushin P.V., et al. Consideration of Distinguishing Design Features of Gas-Turbine and Gas-Reciprocating Units in Design of Emergency Control Systems. – Machines, 2021, 9(3), 47, DOI:10.3390/machines9030047.
5. Praiselin W.J., Edward J.B. A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation. – International Journal of Intelligent Systems and Applications, 2018, vol. 10, No. 3, pp. 67–81, DOI:10.5815/IJISA.2018.03.08.
6. GOST 32144-2013. Elektricheskaya energiya. Sovmestimost' tekhnicheskikh sredstv elektromagnitnaya. Normy kachestva elektricheskoy energii v sistemakh elektrosnabzheniya obshchego naznacheniya (Electric Energy. Electromagnetic Compatibility of Technical Equipment. Power Quality Limits in the Public Power Supply Systems). M.: Standartinform, 2014, 16 p.
7. Burmeyster M.V., et al. Study and analysis of the influence of wind power station on the power quality. –2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering, 2020, p. 9059105, DOI:10.1109/REEPE49198.2020.9059105.
8. Huang J.S., Negnevitsky M., Nguyen D.T. A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances. – IEEE Transactions on Power Delivery, 2002, vol. 17, No. 2, pp. 609–616, DOI:10.1109/PESW.2002.985141.
9. Ghosh A.K., Lubkeman D.L. The Classification of Power System Disturbance Waveforms Using a Neural Network Approach. – IEEE Transactions on Power Delivery, 1990, vol. 10, pp. 671–683, DOI:10.1109/TDC.1994.328398.
10. Tavarov S.Sh. Elektrichestvo – in Russ. (Electricity), 2022, No. 3, pp. 30–38.
11. Balouji E., Salor O. Classification of Power Quality Events Using Deep Learning on Event Images. – IPRIA, 2017, pp. 216–221, DOI:10.1109/PRIA.2017.7983049.
12. Ma J., et al. Classification of Power Quality Disturbances Via Deep Learning, – Journal of IETE, Technical Review, 2017, vol. 34, No. 4, pp. 408–415, DOI:10.1080/02564602.2016.1196620.
13. Zhao F., Yang R. Power Quality Disturbance Recognition Using S-Transform. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 2, pp. 944–950, DOI:10.1109/TPWRD.2006.881575.
14. Axelberg P.G.V., Gu I.Y.H., Bollen M.H.J. Support Vector Machine for Classification of Voltage Disturbances. – IEEE Transactions on Power Delivery, 2007, vol. 22, No. 3, pp. 1297–1303, DOI:10.1109/TPWRD.2007.900065.
15. Bagheri A., Bollen M.H.J., Gu I.Y.H. Improved Characterization of Multistage Voltage Dips Based on the Space Phasor Model. – Electric Power Systems Research, 2018, vol. 154, pp. 319–328, DOI:10.1016/J.EPSR.2017.09.004.
16. Ribeiro P.F., et al. Power Systems Signal Processing for Smart Grids. John Wiley & Sons Ltd, 2014, 442 p.
17. Grus J. Data Science from Scratch: First Principles with Python. O’Reilly Media, Inc., 2015, 330 p.
18. Ignatova V., Granjon P., Bacha S. Space Vector Method for Voltage Dips and Swells Analysis. – IEEE Transactions on Power Delivery, 2009, vol. 24, No. 4, pp. 2054–2061, DOI:10.1109/TPWRD.2009.2028787.
19. Wang Y., et al. Single-Event Characteristics for Voltage Dips in Three-Phase Systems. – IEEE Transactions on Power Delivery, 2017, vol. 32, No. 2, pp. 832–840, DOI:10.1109/TPWRD.2016.2574924.
20. Liu H., et al. A Power Quality Disturbance Classification Method Based on Park Transform and Clarke Transform Analysis. – 2008 3rd International Conference on Innovative Computing Information and Control, 2008, DOI: 10.1109/ICICIC.2008.77.
21. Bagheri A., et al. A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification. – IEEE Transactions on Power Delivery, 2018, vol. 33, iss. 6, рр. 2794–2802.
22. Radioelektronnye sistemy: osnovy postroeniya i teoriya. Spravochnik (Radio-Electronic Systems: Fundamentals of Construction and Theory. Guide) / Under ed. Ya.D. Shirman. М.: Radiotekhnika, 2007, 512 р.
23. Fal'shina V.A., Kulikov A.L. Promyshlennaya energetika – in Russ. (Industrial Power Engineering), 2012, No. 5, pp. 39–46.
24. Val'd A. Posledovatel'nyy analiz (Sequential Analysis). М.: Fizmatgiz, 1960, 328 p.
25. ISO 2859-1:1999. Sampling Procedures for Inspection by Attributes. Part 1: Sampling Schemes Indexed by Acceptance Quality Limit (AQL) for Lot-by-Lot Inspection, 1999, 87 p.
