Selective Monitoring of Power Quality Indicators in Distribution Networks with a Large Share of RES Generation
Keywords:
renewable energy sources, power quality indicators, spatial vector, cross-correlation coefficient, simulation
Abstract
The development of generation based on renewable energy sources (RES) plays an important role in achieving carbon neutrality. Modern solar and wind power plants are connected to distribution electric networks through inverters, which implement power output control and protection algorithms. With a low load of these inverters, violations of the standardized levels of power quality indicators (PQI) are recorded, such as voltage deviations, fluctuations, dips and non-sinusoidality. This is the case with low levels of solar radiation at solar power plants and small wind heads at wind farms. As a rule, the mix of industrial consumers connected to such distribution networks includes electric loads for which deviations of the PQIs from their standardized values are critical and lead to their disconnection by protections, shutdown of technological processes, damage from spoilage, and under-delivery of products. For identifying the disturbance sources and filing lawsuits to compensate for damages, industrial consumers introduce various PQI monitoring systems. A selective monitoring method for automatically detecting violations of the standardized PQI values is considered. To take into account the combined effect of PQIs on electric loads, it is proposed to use the modulus of the current and voltage mutual correlation coefficient. The PQI monitoring device structural diagram is considered, which is based on the results from simulating various network operation modes, as well as the sequential Wald analysis procedure. The introduction of this device will make it possible to prevent significant and long-term violations of the PQIs, thereby ensuring reliable operation of industrial consumers in distribution networks with a large share of RES based generation.
References
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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.
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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.
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2022-04-04
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