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Rüzgâr Enerjisi Dönüşüm Sistemlerinde bir DC-DC Güç Dönüştürücüsünün Denetimli Öğrenme Destekli Kontrolü

Yıl 2021, Cilt: 33 - ASYU 2020 Özel Sayısı, 47 - 56, 30.12.2021

Alper Nabi Akpolat Erkan Dursun Ahmet Emin Kuzucuoğlu

https://doi.org/10.7240/jeps.897076

Öz

Son on yıllarda, elektrik enerjisi sistemlerinde yenilenebilir enerji kaynaklarının yüksek oranda nüfuzunu yaygınlaştırmak için dağıtılmış enerji kaynakları öne çıkmıştır. Küçük rüzgâr türbinlerinin kolay erişilebilirlik durumu nedeniyle, rüzgâr enerjisi dönüşüm sistemleri özellikle rüzgârlı alanlarda küçük müşteriler için elverişli uygulamalardır. Önümüzdeki on yıl muhtemelen dağıtık enerji kaynakları önemli bir artışa tanık olacaktır. Bu bağlamda, rüzgâr enerji dönüşüm sistemleri yaygın olarak tercih edilmektedir, bu nedenle rüzgâr enerjisinin elektrik enerjisine etkin bir şekilde dönüştürülmesi önemli bir konudur. Rüzgâr türbinleri çeşitli topolojilerle şebekeye bağlı veya otonom modda dâhil edilebilirler. Bu makalede, denetimli öğrenme yöntemine dayalı yapay zekâ destekli PI denetleyicisi yardımıyla bir rüzgâr enerji dönüşüm sistemindeki yükselten DC-DC güç dönüştürücüsünün kontrolünü incelemekteyiz. Önerilen yöntemle ilgili olarak, yapay zekânın bir alt kümesi olarak yapay sinir ağları kullanılmaktadır. Önerilen kontrol yönteminin uygulanabilirliğini test etmek ve doğrulamak için, MATLAB/Simulink ortamında bir DC baraya sabit mıknatıslı senkron generatör ile küçük bir rüzgar enerji dönüşüm sistem uygulanmıştır. Sonuçlar, çalışma kapsamında dinamik yanıtın ve daha az karmaşıklığın yüksek doğruluk ile elde edildiğini göstermektedir.

Anahtar Kelimeler

Rüzgâr Enerjisi Dönüşüm Sistemleri Yapay Zeka Yapay Sinir Ağı DC-DC Güç Dönüştürücüler

Destekleyen Kurum

Bu çalışma ASYU2020_Akıllı Sistemlerde Yenilikler ve Uygulamaları Özel sayısı için değerlendirilmek üzere gönderilmiştir.

Teşekkür

Bu çalışma ASYU2020_Akıllı Sistemlerde Yenilikler ve Uygulamaları Özel sayısı için değerlendirilmek üzere gönderilmiştir.

Kaynakça

  • [1] Kolar, J. W., Biela, J., Waffler S., Friedli, T. and Badstuebner, U. (2011). Performance trends and limitations of power electronic systems. 2010 6th International Conference on Integrated Power Electronics Systems, Nuremberg, pp. 1-20.
  • [2] Melicio, R., Mendes, V. M. F. and Catalao, J. P. S. (2010). Power converter topologies for wind energy conversion systems: Integrated modeling, control strategy and performance simulation. Renewable Energy, 35 (10), pp. 2165-2174.
  • [3] Hannan, M. A., et al. (2019). Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations. Applied Energy, 251, pp. 113404.
  • [4] Soliman, M. A., Hasanien, H. M., Azazi, H. Z., El-Kholy, E. E. and Mahmoud, S. A. (2019). An Adaptive Fuzzy Logic Control Strategy for Performance Enhancement of a Grid-Connected PMSG-Based Wind Turbine. IEEE Transactions on Industrial Informatics, vol. 15 (6), pp. 3163-3173.
  • [5] Zhang, Y., Wang, Z., Wang, H. and Blaabjerg, F. (2020). Artificial Intelligence-Aided Thermal Model Considering Cross-Coupling Effects. IEEE Transactions on Power Electronics, 35 (10), pp. 9998-10002.
  • [6] Bayhan, S., Demirbaş, S. and Abu-Rub, H. (2016). Fuzzy-PI-based sensorless frequency and voltage controller for doubly fed induction generator connected to a DC microgrid. IET Renewable Power Generation, 10, (8), pp. 1069-1077.
  • [7] Mesbahi, A., Aljarhizi, Y., Hassoune, A., Khafallah, M., and Alibrahmi, E. (2020). Boost Converter implementation for Wind Generation System based on a variable speed PMSG," 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco, pp. 1-6.
  • [8] Deng, X. et al. (2019). Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine. Energy, 186 (115790).
  • [9] Wei, C., Zhang, Z., Qiao, W., and Qu, L. (2016). An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems. IEEE Transactions on Power Electronics, 31 (11), pp. 7837-7848.
  • [10] Chatri, C. and Ouassaid, M. (2018). Sensorless Control of the PMSG in WECS Using Artificial Neural Network and Sliding Mode Observer. 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Rabat, Morocco, pp. 1-6.
  • [11] Jday, M. and Haggège, J. (2017). Modeling and neural networks based control of power converters associated with a wind turbine. 2017 International Conference on Green Energy Conversion Systems (GECS), Hammamet, Tunisia, pp. 1-7.
  • [12] Adineh, B., Habibi, M. R., Akpolat, A. N. and Blaabjerg, F. (2021). Sensorless Voltage Estimation for Total Harmonic Distortion Calculation using Artificial Neural Networks in Microgrids. IEEE Transactions on Circuits and Systems II: Express Briefs, doi: 10.1109/TCSII.2021.3059410.
  • [13] Akpolat, A. N., Dursun, E. and Kuzucuoğlu, A. E. (2020). AI-Aided Control of a Power Converter in Wind Energy Conversion System. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, pp. 1-6, doi: 10.1109/ASYU50717.2020.9259877.
  • [14] Samara, S. and Natsheh, E. (2019). Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks. IEEE Access, 7, pp. 50287-50299.
  • [15] Akpolat, A. N., Habibi, M. R., Dursun, E., Kuzucuoğlu, A. E., Yang, Y., Dragicevic, T. and Blaabjerg, F. (2020). Sensorless Control of DC Microgrid Based on Artificial Intelligence. IEEE Transactions on Energy Conversion, doi: 10.1109/TEC.2020.3044270.
  • [16] Egea-Àlvarez, A., Aragüés-Peñalba, M., Gomis-Bellmunt, O., Rull-Duran, J. and Sudrià-Andreu, A. (2016). Sensorless control of a power converter for a cluster of small wind turbines. IET Renewable Power Generation, 10 (5), pp. 721-728.
  • [17] Teiar, H., Chaoui H. and Sicard, P. (2015). Almost parameter-free sensorless control of PMSM. IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, pp. 004667-004671.
  • [18] Syskakis. T. and Ordonez, M. (2019). MPPT for Small Wind Turbines: Zero-Oscillation Sensorless Strategy. 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Xi'an, China, pp. 1060-1065.
  • [19] Onar, O. C. and Khaligh, A. (2015). Alternative Energy in Power Electronics. Chapter 2 - Energy Sources, 1st ed., UK: Butterworth-Heinemann, Elsevier, 2015, pp. 81-154.
  • [20] Heier, S. (2014). Wind Energy Conversion System. Grid Integration of Wind Energy: Onshore and Offshore Conversion Systems, 3rd ed., Germany: John Wiley & Sons, pp. 31-117.
  • [21] Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts’s ‘logical calculus of ideas immanent in nervous activity. Synthese, 141, pp. 75–215.
  • [22] Rathnayaka, R. M. K. T. and Seneviratna, D. M. K. N. (2019). A Novel Hybrid Back Propagation Neural Network Approach for Time Series Forecasting Under the Volatility. Communications in Computer and Information Science, Singapore.

Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems

Yıl 2021, Cilt: 33 - ASYU 2020 Özel Sayısı, 47 - 56, 30.12.2021

Alper Nabi Akpolat Erkan Dursun Ahmet Emin Kuzucuoğlu

https://doi.org/10.7240/jeps.897076

Öz

Over the last decades, to adopt high penetration of renewable energy sources (RESs) in electrical energy systems, distributed energy resources (DERs) have become prominent. Due to easy attainability status of small wind turbines (WTs), wind energy conversion systems (WECSs) are feasible applications for small customers, especially in windy areas. The next decade is likely to witness a considerable rise in DERs. In this context, WECSs are preferred broadly, thus harvesting wind energy into electrical energy effectively is a substantial issue. WTs can be got involved in the grid-connected or autonomous mode with a variety of topologies. In this paper, we examine to control of DC-DC boost converter of a WECS with the help of artificial intelligence (AI)-aided PI controller based on supervised learning method. Regarding the proposed method, artificial neural networks (ANNs) as a subset of AI are utilized. To test and ensure the applicability of the proposed control method, a small WECS with a permanent magnet synchronous generator (PMSG) connected a DC bus was implemented in MATLAB/Simulink environment. The proposed ANN scheme has reached a high accuracy rate with an overall mean squared error (MSE) equal to 7.4e-08. The results present that dynamic response and less complexity with a high accuracy rate have been obtained under study. The main target of this study is to reduce the number of sensors in the control layer. Thus, a cost-effective and more reliable structure is obtained with fewer sensor requirements.

Anahtar Kelimeler

Wind Energy Conversion Systems Artificial Intelligence Artificial Neural Networks DC-DC Power Converters

Kaynakça

  • [1] Kolar, J. W., Biela, J., Waffler S., Friedli, T. and Badstuebner, U. (2011). Performance trends and limitations of power electronic systems. 2010 6th International Conference on Integrated Power Electronics Systems, Nuremberg, pp. 1-20.
  • [2] Melicio, R., Mendes, V. M. F. and Catalao, J. P. S. (2010). Power converter topologies for wind energy conversion systems: Integrated modeling, control strategy and performance simulation. Renewable Energy, 35 (10), pp. 2165-2174.
  • [3] Hannan, M. A., et al. (2019). Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations. Applied Energy, 251, pp. 113404.
  • [4] Soliman, M. A., Hasanien, H. M., Azazi, H. Z., El-Kholy, E. E. and Mahmoud, S. A. (2019). An Adaptive Fuzzy Logic Control Strategy for Performance Enhancement of a Grid-Connected PMSG-Based Wind Turbine. IEEE Transactions on Industrial Informatics, vol. 15 (6), pp. 3163-3173.
  • [5] Zhang, Y., Wang, Z., Wang, H. and Blaabjerg, F. (2020). Artificial Intelligence-Aided Thermal Model Considering Cross-Coupling Effects. IEEE Transactions on Power Electronics, 35 (10), pp. 9998-10002.
  • [6] Bayhan, S., Demirbaş, S. and Abu-Rub, H. (2016). Fuzzy-PI-based sensorless frequency and voltage controller for doubly fed induction generator connected to a DC microgrid. IET Renewable Power Generation, 10, (8), pp. 1069-1077.
  • [7] Mesbahi, A., Aljarhizi, Y., Hassoune, A., Khafallah, M., and Alibrahmi, E. (2020). Boost Converter implementation for Wind Generation System based on a variable speed PMSG," 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco, pp. 1-6.
  • [8] Deng, X. et al. (2019). Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine. Energy, 186 (115790).
  • [9] Wei, C., Zhang, Z., Qiao, W., and Qu, L. (2016). An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems. IEEE Transactions on Power Electronics, 31 (11), pp. 7837-7848.
  • [10] Chatri, C. and Ouassaid, M. (2018). Sensorless Control of the PMSG in WECS Using Artificial Neural Network and Sliding Mode Observer. 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Rabat, Morocco, pp. 1-6.
  • [11] Jday, M. and Haggège, J. (2017). Modeling and neural networks based control of power converters associated with a wind turbine. 2017 International Conference on Green Energy Conversion Systems (GECS), Hammamet, Tunisia, pp. 1-7.
  • [12] Adineh, B., Habibi, M. R., Akpolat, A. N. and Blaabjerg, F. (2021). Sensorless Voltage Estimation for Total Harmonic Distortion Calculation using Artificial Neural Networks in Microgrids. IEEE Transactions on Circuits and Systems II: Express Briefs, doi: 10.1109/TCSII.2021.3059410.
  • [13] Akpolat, A. N., Dursun, E. and Kuzucuoğlu, A. E. (2020). AI-Aided Control of a Power Converter in Wind Energy Conversion System. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, pp. 1-6, doi: 10.1109/ASYU50717.2020.9259877.
  • [14] Samara, S. and Natsheh, E. (2019). Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks. IEEE Access, 7, pp. 50287-50299.
  • [15] Akpolat, A. N., Habibi, M. R., Dursun, E., Kuzucuoğlu, A. E., Yang, Y., Dragicevic, T. and Blaabjerg, F. (2020). Sensorless Control of DC Microgrid Based on Artificial Intelligence. IEEE Transactions on Energy Conversion, doi: 10.1109/TEC.2020.3044270.
  • [16] Egea-Àlvarez, A., Aragüés-Peñalba, M., Gomis-Bellmunt, O., Rull-Duran, J. and Sudrià-Andreu, A. (2016). Sensorless control of a power converter for a cluster of small wind turbines. IET Renewable Power Generation, 10 (5), pp. 721-728.
  • [17] Teiar, H., Chaoui H. and Sicard, P. (2015). Almost parameter-free sensorless control of PMSM. IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, pp. 004667-004671.
  • [18] Syskakis. T. and Ordonez, M. (2019). MPPT for Small Wind Turbines: Zero-Oscillation Sensorless Strategy. 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Xi'an, China, pp. 1060-1065.
  • [19] Onar, O. C. and Khaligh, A. (2015). Alternative Energy in Power Electronics. Chapter 2 - Energy Sources, 1st ed., UK: Butterworth-Heinemann, Elsevier, 2015, pp. 81-154.
  • [20] Heier, S. (2014). Wind Energy Conversion System. Grid Integration of Wind Energy: Onshore and Offshore Conversion Systems, 3rd ed., Germany: John Wiley & Sons, pp. 31-117.
  • [21] Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts’s ‘logical calculus of ideas immanent in nervous activity. Synthese, 141, pp. 75–215.
  • [22] Rathnayaka, R. M. K. T. and Seneviratna, D. M. K. N. (2019). A Novel Hybrid Back Propagation Neural Network Approach for Time Series Forecasting Under the Volatility. Communications in Computer and Information Science, Singapore.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Alper Nabi Akpolat 0000-0002-6972-2509

Erkan Dursun 0000-0002-7914-8379

Ahmet Emin Kuzucuoğlu 0000-0002-7769-6451

Yayımlanma Tarihi 30 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 33 - ASYU 2020 Özel Sayısı

Kaynak Göster

APA Akpolat, A. N., Dursun, E., & Kuzucuoğlu, A. E. (2021). Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems. International Journal of Advances in Engineering and Pure Sciences, 33, 47-56. https://doi.org/10.7240/jeps.897076
AMA Akpolat AN, Dursun E, Kuzucuoğlu AE. Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems. JEPS. Aralık 2021;33:47-56. doi:10.7240/jeps.897076
Chicago Akpolat, Alper Nabi, Erkan Dursun, ve Ahmet Emin Kuzucuoğlu. “Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems”. International Journal of Advances in Engineering and Pure Sciences 33, Aralık (Aralık 2021): 47-56. https://doi.org/10.7240/jeps.897076.
EndNote Akpolat AN, Dursun E, Kuzucuoğlu AE (01 Aralık 2021) Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems. International Journal of Advances in Engineering and Pure Sciences 33 47–56.
IEEE A. N. Akpolat, E. Dursun, ve A. E. Kuzucuoğlu, “Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems”, JEPS, c. 33, ss. 47–56, 2021, doi: 10.7240/jeps.897076.
ISNAD Akpolat, Alper Nabi vd. “Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems”. International Journal of Advances in Engineering and Pure Sciences 33 (Aralık 2021), 47-56. https://doi.org/10.7240/jeps.897076.
JAMA Akpolat AN, Dursun E, Kuzucuoğlu AE. Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems. JEPS. 2021;33:47–56.
MLA Akpolat, Alper Nabi vd. “Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems”. International Journal of Advances in Engineering and Pure Sciences, c. 33, 2021, ss. 47-56, doi:10.7240/jeps.897076.
Vancouver Akpolat AN, Dursun E, Kuzucuoğlu AE. Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems. JEPS. 2021;33:47-56.