Арктическая ветродизельная электростанция с интеллектуальной системой автоматического управления
Ключевые слова:
ветровая энергия, ветродизельная электростанция, интеллектуальное управление, Арктика, активно-адаптивные связи, машинное обучение
Аннотация
Основной проблемой энергоснабжения изолированных потребителей являются большие логистические расходы, связанные с доставкой топлива и оборудования для дизельных электростанций, низкой плотностью транспортной инфраструктуры и, как следствие, высокой стоимостью топлива. Также велики эксплуатационные расходы на дизельных электростанциях и удельный расход топлива, отсутствуют мониторинг и автоматизация управления. С учетом высокого ветропотенциала арктических территорий модернизация и строительство систем энергетических комплексов и систем может эффективно проводиться на основе ветродизельных электростанций модульного типа с интеллектуальной системой управления. В статье предложены концепция и аппаратные решения интеллектуальной системы автоматического управления, обеспечивающей возможность максимизации производства электроэнергии от возобновляемых источников за счет динамического перераспределения мощности между элементами гибридного энергетического комплекса и, как следствие, минимизации расхода топлива. Анализ управляемой ветродизельной электростанции показал, что использование интеллектуальной системы автоматического управления, прогнозирование выработки электроэнергии ветроэнергетической установкой и работа аккумуляторной батареи в циклическом режиме позволяют эффективно покрывать график нагрузки автономного потребителя и увеличивать до 60 % и более уровень замещения дизельного топлива.
Литература
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11. Aasim, Singh S.N., Mohapatra A. Repeated Wavelet Transform Based ARIMA Model for Very Short-Term Wind Speed Forecasting. – Renewable Energy, 2019,136, pp. 758–768, DOI:10.1016/j.renene.2019.01.031.
12. Carpinone A., et al. Markov Chain Modeling for Very-Short-Term Wind Power Forecasting. Electric Power Systems Research 2015, 122, pp. 152–158, DOI:10.1016/j.epsr.2014.12.025.
13. D’Amico G., et al. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. – Energies, 2020, 13, 4246, DOI:10.3390/en13164246.
14. Marugán A.P., et al. A Survey of Artificial Neural Network In Wind Energy Systems. – Applied Energy, 2018, 228, pp. 1822–836, DOI:10.1016/j.apenergy.2018.07.084.
15. Santhosh M., Venkaiah C., Vinod Kumar D.M. Ensemble Empirical Mode Decomposition Based Adaptive Wavelet Neural Network Method for Wind Speed Prediction. – Energy Conversion and Management, 2018, 168, pp. 482– 493, DOI:10.1016/j.enconman.2018.04.099.
16. Huang C., et al. Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method. – IEEE Green Technologies Conference (IEEE-Green), 2011, DOI: 10.1109/GREEN.2011.5754856.
17. Gobiet A., et al. Operational Forecasting of Wet Snow Avalanche Activity: a Case Study for the Eastern European Alps. – Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, pp. 132– 139.
18. Battisti L. Wind Turbines in Cold Climates: Icing Impacts and Mitigation Systems, Green Energy and Technology, Springer, 2015.
19. Parent O., Ilinca A. Anti-Icing and De-Icing Techniques for Wind Turbines: Critical review. – Cold Regions Science and Technology, 2011, 65(1), pp. 88–96, DOI:10.1016/j.coldregions.2010.01.005.
20. Homola M.C., et al. Effect of Atmospheric Temperature and Droplet Size Variation on Ice Accretion of Wind Turbine Blades. – Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(10), DOI:10.1016/j.jweia.2010.06.007.
#
1. Elistratov V.V. Vozobnovlyaemaya energetika (Renewable Energy). SPb.: Izd-vo politekhn. un-ta, 2016, 424 p.
2. Projected Costs of Generating Electricity 2020, IEA, Paris [Electron. resource], URL: https://www.iea.org/reports/projected-costs-of-generating-electricity-2020 (Date of appeal 8.12.2021).
3. Kazem H.A., et al. Optimum design and evaluation of hybrid solar/wind/diesel power system for Masirah Island. – Environment, Development and Sustainability, 2017, 19 (5), pp. 1761–1778, DOI:10.1007/s10668-016-9828-1.
4. Elistratov V.V., et al. The Application of Adapted Materials and Technologies to Create Energy Systems Based on Renewable Energy Sources under Harsh Climatic Condition. – Applied Solar Energy, 2018, 54(6), pp. 472–476, DOI:10.3103/S0003701X18060087.
5. Elistratov V., et al. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. – Energies, 2021, 14, 4188, DOI:10.3390/en14144188.
6. Elistratov V.V., Denisov R.S. Energetic and Ecological Justification of RE-hybrid Systems for Vulnerable Ecosystems. – IOP Conf. Series: Earth and Environmental Science, 2021, 689, DOI:10.1088/1755-1315/689/1/012017.
7. Ansari M.M.T., Sangoden V. DMLHFLC (Dual Mode Linguistic Hedge Fuzzy Logic Controller) for an Isolated Wind-Diesel Hybrid Power System with BES (Battery Energy Storage) Unit. – Energy, 2010, 35(9), pp. 3827– 3837, DOI:10.1016/j.energy.2010.05.037.
8. Vachirasricirikul S., Ngamroo I., Kaitwanidvilai S. Coordinated SVC and AVR for Robust Voltage Control in a Hybrid Wind-Diesel System. – Energy Conversion and Management, 2010, 51, pp. 2383–2393, DOI:10.1016/j.enconman.2010.05.001.
9. Elistratov V.V., Bogun I.V., Kasina V.I. Optimization of Wind-Diesel Power Plants Parameters and Placement for Power Supply of Russia’s Northern Regions Consumers. – 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), 2019, 8771647, DOI: 10.1109/ELMA.2019.8771647.
10. Shukur O.B., Lee M.H. Daily Wind Speed Forecasting Through Hybrid KF-ANN model based on ARIMA. – Renewable Energy, 2015, 76, pp. 637–647, DOI:10.1016/j.renene.2014.11.084.
11. Aasim, Singh S.N., Mohapatra A. Repeated Wavelet Transform Based ARIMA Model for Very Short-Term Wind Speed Forecasting. – Renewable Energy, 2019,136, pp. 758–768, DOI:10.1016/j.renene.2019.01.031.
12. Carpinone A., et al. Markov Chain Modeling for Very-Short-Term Wind Power Forecasting. Electric Power Systems Research 2015, 122, pp. 152–158, DOI:10.1016/j.epsr.2014.12.025.
13. D’Amico G., et al. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. – Energies, 2020, 13, 4246, DOI:10.3390/en13164246.
14. Marugán A.P., et al. A Survey of Artificial Neural Network In Wind Energy Systems. – Applied Energy, 2018, 228, pp. 1822–836, DOI:10.1016/j.apenergy.2018.07.084.
15. Santhosh M., Venkaiah C., Vinod Kumar D.M. Ensemble Empirical Mode Decomposition Based Adaptive Wavelet Neural Network Method for Wind Speed Prediction. – Energy Conversion and Management, 2018, 168, pp. 482– 493, DOI:10.1016/j.enconman.2018.04.099.
16. Huang C., et al. Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method. – IEEE Green Technologies Conference (IEEE-Green), 2011, DOI: 10.1109/GREEN.2011.5754856.
17. Gobiet A., et al. Operational Forecasting of Wet Snow Avalanche Activity: a Case Study for the Eastern European Alps. – Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, pp. 132– 139.
18. Battisti L. Wind Turbines in Cold Climates: Icing Impacts and Mitigation Systems, Green Energy and Technology, Springer, 2015.
19. Parent O., Ilinca A. Anti-Icing and De-Icing Techniques for Wind Turbines: Critical review. – Cold Regions Science and Technology, 2011, 65(1), pp. 88–96, DOI:10.1016/j.coldregions.2010.01.005.
20. Homola M.C., et al. Effect of Atmospheric Temperature and Droplet Size Variation on Ice Accretion of Wind Turbine Blades. – Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(10), DOI:10.1016/j.jweia.2010.06.007.
2. Projected Costs of Generating Electricity 2020, IEA, Paris [Электрон. ресурс], URL: https://www.iea.org/reports/projected-costs-of-generating-electricity-2020 (дата обращения 8.12.2021).
3. Kazem H.A., et al. Optimum design and evaluation of hybrid solar/wind/diesel power system for Masirah Island. – Environment, Development and Sustainability, 2017, 19 (5), pp. 1761–1778, DOI:10.1007/s10668-016-9828-1.
4. Elistratov V.V., et al. The Application of Adapted Materials and Technologies to Create Energy Systems Based on Renewable Energy Sources under Harsh Climatic Condition. – Applied Solar Energy, 2018, 54(6), pp. 472–476, DOI:10.3103/S0003701X18060087.
5. Elistratov V., et al. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. – Energies, 2021, 14, 4188, DOI:10.3390/en14144188.
6. Elistratov V.V., Denisov R.S. Energetic and Ecological Justification of RE-hybrid Systems for Vulnerable Ecosystems. – IOP Conf. Series: Earth and Environmental Science, 2021, 689, DOI:10.1088/1755-1315/689/1/012017.
7. Ansari M.M.T., Sangoden V. DMLHFLC (Dual Mode Linguistic Hedge Fuzzy Logic Controller) for an Isolated Wind-Diesel Hybrid Power System with BES (Battery Energy Storage) Unit. – Energy, 2010, 35(9), pp. 3827– 3837, DOI:10.1016/j.energy.2010.05.037.
8. Vachirasricirikul S., Ngamroo I., Kaitwanidvilai S. Coordinated SVC and AVR for Robust Voltage Control in a Hybrid Wind-Diesel System. – Energy Conversion and Management, 2010, 51, pp. 2383–2393, DOI:10.1016/j.enconman.2010.05.001.
9. Elistratov V.V., Bogun I.V., Kasina V.I. Optimization of Wind-Diesel Power Plants Parameters and Placement for Power Supply of Russia’s Northern Regions Consumers. – 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), 2019, 8771647, DOI: 10.1109/ELMA.2019.8771647.
10. Shukur O.B., Lee M.H. Daily Wind Speed Forecasting Through Hybrid KF-ANN model based on ARIMA. – Renewable Energy, 2015, 76, pp. 637–647, DOI:10.1016/j.renene.2014.11.084.
11. Aasim, Singh S.N., Mohapatra A. Repeated Wavelet Transform Based ARIMA Model for Very Short-Term Wind Speed Forecasting. – Renewable Energy, 2019,136, pp. 758–768, DOI:10.1016/j.renene.2019.01.031.
12. Carpinone A., et al. Markov Chain Modeling for Very-Short-Term Wind Power Forecasting. Electric Power Systems Research 2015, 122, pp. 152–158, DOI:10.1016/j.epsr.2014.12.025.
13. D’Amico G., et al. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. – Energies, 2020, 13, 4246, DOI:10.3390/en13164246.
14. Marugán A.P., et al. A Survey of Artificial Neural Network In Wind Energy Systems. – Applied Energy, 2018, 228, pp. 1822–836, DOI:10.1016/j.apenergy.2018.07.084.
15. Santhosh M., Venkaiah C., Vinod Kumar D.M. Ensemble Empirical Mode Decomposition Based Adaptive Wavelet Neural Network Method for Wind Speed Prediction. – Energy Conversion and Management, 2018, 168, pp. 482– 493, DOI:10.1016/j.enconman.2018.04.099.
16. Huang C., et al. Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method. – IEEE Green Technologies Conference (IEEE-Green), 2011, DOI: 10.1109/GREEN.2011.5754856.
17. Gobiet A., et al. Operational Forecasting of Wet Snow Avalanche Activity: a Case Study for the Eastern European Alps. – Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, pp. 132– 139.
18. Battisti L. Wind Turbines in Cold Climates: Icing Impacts and Mitigation Systems, Green Energy and Technology, Springer, 2015.
19. Parent O., Ilinca A. Anti-Icing and De-Icing Techniques for Wind Turbines: Critical review. – Cold Regions Science and Technology, 2011, 65(1), pp. 88–96, DOI:10.1016/j.coldregions.2010.01.005.
20. Homola M.C., et al. Effect of Atmospheric Temperature and Droplet Size Variation on Ice Accretion of Wind Turbine Blades. – Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(10), DOI:10.1016/j.jweia.2010.06.007.
#
1. Elistratov V.V. Vozobnovlyaemaya energetika (Renewable Energy). SPb.: Izd-vo politekhn. un-ta, 2016, 424 p.
2. Projected Costs of Generating Electricity 2020, IEA, Paris [Electron. resource], URL: https://www.iea.org/reports/projected-costs-of-generating-electricity-2020 (Date of appeal 8.12.2021).
3. Kazem H.A., et al. Optimum design and evaluation of hybrid solar/wind/diesel power system for Masirah Island. – Environment, Development and Sustainability, 2017, 19 (5), pp. 1761–1778, DOI:10.1007/s10668-016-9828-1.
4. Elistratov V.V., et al. The Application of Adapted Materials and Technologies to Create Energy Systems Based on Renewable Energy Sources under Harsh Climatic Condition. – Applied Solar Energy, 2018, 54(6), pp. 472–476, DOI:10.3103/S0003701X18060087.
5. Elistratov V., et al. Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System. – Energies, 2021, 14, 4188, DOI:10.3390/en14144188.
6. Elistratov V.V., Denisov R.S. Energetic and Ecological Justification of RE-hybrid Systems for Vulnerable Ecosystems. – IOP Conf. Series: Earth and Environmental Science, 2021, 689, DOI:10.1088/1755-1315/689/1/012017.
7. Ansari M.M.T., Sangoden V. DMLHFLC (Dual Mode Linguistic Hedge Fuzzy Logic Controller) for an Isolated Wind-Diesel Hybrid Power System with BES (Battery Energy Storage) Unit. – Energy, 2010, 35(9), pp. 3827– 3837, DOI:10.1016/j.energy.2010.05.037.
8. Vachirasricirikul S., Ngamroo I., Kaitwanidvilai S. Coordinated SVC and AVR for Robust Voltage Control in a Hybrid Wind-Diesel System. – Energy Conversion and Management, 2010, 51, pp. 2383–2393, DOI:10.1016/j.enconman.2010.05.001.
9. Elistratov V.V., Bogun I.V., Kasina V.I. Optimization of Wind-Diesel Power Plants Parameters and Placement for Power Supply of Russia’s Northern Regions Consumers. – 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), 2019, 8771647, DOI: 10.1109/ELMA.2019.8771647.
10. Shukur O.B., Lee M.H. Daily Wind Speed Forecasting Through Hybrid KF-ANN model based on ARIMA. – Renewable Energy, 2015, 76, pp. 637–647, DOI:10.1016/j.renene.2014.11.084.
11. Aasim, Singh S.N., Mohapatra A. Repeated Wavelet Transform Based ARIMA Model for Very Short-Term Wind Speed Forecasting. – Renewable Energy, 2019,136, pp. 758–768, DOI:10.1016/j.renene.2019.01.031.
12. Carpinone A., et al. Markov Chain Modeling for Very-Short-Term Wind Power Forecasting. Electric Power Systems Research 2015, 122, pp. 152–158, DOI:10.1016/j.epsr.2014.12.025.
13. D’Amico G., et al. Managing Wind Power Generation via Indexed Semi-Markov Model and Copula. – Energies, 2020, 13, 4246, DOI:10.3390/en13164246.
14. Marugán A.P., et al. A Survey of Artificial Neural Network In Wind Energy Systems. – Applied Energy, 2018, 228, pp. 1822–836, DOI:10.1016/j.apenergy.2018.07.084.
15. Santhosh M., Venkaiah C., Vinod Kumar D.M. Ensemble Empirical Mode Decomposition Based Adaptive Wavelet Neural Network Method for Wind Speed Prediction. – Energy Conversion and Management, 2018, 168, pp. 482– 493, DOI:10.1016/j.enconman.2018.04.099.
16. Huang C., et al. Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method. – IEEE Green Technologies Conference (IEEE-Green), 2011, DOI: 10.1109/GREEN.2011.5754856.
17. Gobiet A., et al. Operational Forecasting of Wet Snow Avalanche Activity: a Case Study for the Eastern European Alps. – Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, pp. 132– 139.
18. Battisti L. Wind Turbines in Cold Climates: Icing Impacts and Mitigation Systems, Green Energy and Technology, Springer, 2015.
19. Parent O., Ilinca A. Anti-Icing and De-Icing Techniques for Wind Turbines: Critical review. – Cold Regions Science and Technology, 2011, 65(1), pp. 88–96, DOI:10.1016/j.coldregions.2010.01.005.
20. Homola M.C., et al. Effect of Atmospheric Temperature and Droplet Size Variation on Ice Accretion of Wind Turbine Blades. – Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(10), DOI:10.1016/j.jweia.2010.06.007.
