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Parameter Estimation of BLDC Motors by SVM for UAV Propulsion Systems

Yıl 2022, Cilt: 15 Sayı: 2, 406 - 419, 31.08.2022
https://doi.org/10.18185/erzifbed.930222

Öz

Küreselleşen 21. yüzyıl günümüz dünyasında, bilişim işletmeleri ile endüstriyi bir araya getiren Endüstri 4.0 teknolojilerinde, her geçen gün önemi gittikçe artan insansız hava araçları, askeri, sivil ve bilimsel amaçlı projelerde oldukça yoğun olarak kullanılmaktadır. Bu nedenle uçuş kontrol sistemine güç sağlayan insansız hava aracı motorlarının yüksek verimde ve güvenli bir şekilde kullanılması büyük önem taşımaktadır. Bu çalışmada, insansız hava araçlarının önemli bir ekipmanı olan fırçasız doğru akım motorundan ve ilgili uçuş kontrol sisteminden alınan veriler Matlab Simulink’te SVM, KNN, karar ağaçları, en küçük kareler yöntemleri kullanılarak analiz edilmiştir. BLDC motorun giriş parametrelerine göre, sistemin maksimum verime ulaşabileceği tork değeri çapraz doğrulama yöntemi kullanılarak tahmin edilmiştir. Yapılan analizler ışığında insansız hava araçlarının uçuş performansını ve konforunu önemli ölçüde arttıran %98 başarı oranına sahip SVM tabanlı bir parametre tahminlemesi yöntemi geliştirilmiştir. Dolayısıyla, insansız hava araçlarının BLDC motorları için parametre tahminlemesinde %98 güvenilirlik oranıyla, en yüksek doğruluk oranına sahip olan quadratik SVM ve kübik SVM algoritmalarının kullanılmasının daha uygun olacağı saptanmıştır.

Kaynakça

  • Outay, F., Mengash, H. A., Adnan, M. 2020. “Applications of Unmanned Aerial Vehicle (UAV) in Road Safety, Traffic and Highway Infrastructure Management: Recent Advances and Challenges”, Transportation Research Part A: Policy and Practice, 141, 116-129.
  • Özbek, E., Yalin, G., Ekici, S., Karakoc, T. H., 2020. “Evaluation of Design Methodology, Limitations, and Iterations of a Hydrogen Fuelled Hybrid Fuel Cell Mini UAV”, Energy, 213 (15).
  • Sabanci, K. 2020. “Artificial Intelligence Based Power Consumption Estimation of Two-Phase Brushless DC Motor According to FEA Parametric Simulation”, Measurement, 155, 1-9.
  • Goswamia, R., Joshib, D. 2018. “Performance Review of Fuzzy Logic Based Controllers Employed in Brushless DC Motor”, Procedia Computer Science, 132 (2018), 623-631.
  • Chen, Q., Liu, G., Zhao, W., Qu, L., Xu., G. 2017. “Asymmetrical SVPWM Fault-Tolerant Control of Five-Phase PM Brushless Motors”, IEEE Trans. Energy Convers., 32(1), 12–22.
  • Megahed, F. T. 2020. “Analytical Approach to Estimate the Polyphase Induction Machine Performance”, J Magn Magn Mater, 514 (15), 167119.
  • Kelek, M. M., Çelik, İ., Fidan, U., & Oğuz, Y. The Simulation of Mathematical Model of Outer Rotor BLDC Motor. Simulation, 2687, 5527.
  • Filippini, M., Hunt, L. C. 2012. “US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach”, Energy Economics, 34 (5), 1484-1491.
  • Gupta, S. K., Singh, O., Khan, M. A., Kushwaha, A. K. 2020. “A Review on Developments of Polyphase Machines”, J Inform Optim Sci, 41 (1), 327-343.
  • Figueroa, J., Brocart, C., Cros, J., Viarouge, P. 2003. “Simplified Simulation Methods for Polyphase Brushless DC Motors”, Math Comput Simulation, 63 (3), 209-224.
  • Khadar, S., Kouzou, A., Hafaifa, A., Atif, I. 2019. “Investigation on SVM-Backstepping Sensorless Control of Five-Phase Open-End Winding Induction Motor Based on Model Reference Adaptive System and Parameter Estimation,” Eng Sci Technol, 22 (4), 1013-1026.
  • Palanisamy, R., Vijayakumar, K. 2020. “ SVPWM Control Strategy for a Three-Phase Five-Level Dual Inverter Fed Open-End Winding Induction Motor,” ISA Trans, 102 (2020), 105-116.
  • Anih, L.U., Obe, E. S. 2019. “Performance Analysis of a Composite Dual-Winding Reluctance Machine”, Energy Convers Manage, 50 (12), 3056-3062.
  • Xin, C., Qin, S., Chenhao, L., Heng Z., Zhiquan, D. 2017. “Direct Control of Torque and Levitation Force for Dual-Winding Bearingless Switched Reluctance”, MotorcElectr Power Syst Res, 145 (2017), 214-222.
  • Sachin, J., Chinthamalla, R., Sanjeevikumar, P., Olorunfemi O., J., Ertas, A. H. 2016. “Dual MPPT Algorithm for Dual PV Source Fed Open-End Winding Induction Motor Drive for Pumping Application,” Eng Sci Technol, 19 (4), 1771-1780.
  • Padmanaban S., Pecht, M. 2016. “An Isolated/Non-Isolated Novel Multilevel Inverter Configuration for a Dual Three-Phase Symmetrical/Asymmetrical Star-Winding Converter”, Eng Sci Technol, 19 (4), 1763-1770.
  • Ogunjuyigbe, A., S., O., Ayodele, T., R., Adetokun, B., B. 2018. “Modelling and Analysis of Dual Stator-Winding Induction Machine Using Complex Vector Approach”, Eng Sci Technol, 21 (3), 351-363.
  • Chatterjee, A., Chatterjee, D. 2015. “An Improved Excitation Control Technique of Three-Phase Induction Machine Operating as Dual Winding Generator for Micro-Wind Domestic Application”, Energy Convers. Manage., 87 (2015), 98-106.
  • Fu, Z., L., Xing, Z., 2020. “Performance Analysis of Dual-Redundancy Brushless DC Motor”, Energy Reports, 6 (9), 829-833.
  • Matlab Simulink. The BLDC data set has been analyzed by transferring it from Matlab Simulink's database to the workspace. Date of last access: 15-01-2021 https://www.mathworks.com/help/physmod/sps/ug/brushless-dc-motor.html
  • Hentati, A. I., Fourati, L., C. 2020. “Comprehensive Survey of UAVs Communication Networks”, Computer Standards & Interfaces, 72 (2020), 103451.
  • Zhang, H., Song, B., Li, F., Xuan J., 2021. “Multidisciplinary Design Optimization of an Electric Propulsion System of a Hybrid UAV Considering Wind Disturbance Rejection Capability in the Quadrotor Mode”, Aerospace Science and Technology, 110, 106372.
  • Dai, B., H., Yang, L. 2020. “Throughput and Energy Efficiency Maximization for UAV-Assisted Vehicular Network”, Physical Communication, 42 (2020), 101136.
  • Hoang, T., M., Nguyen, N., M., Duong, T., Q. 2019. "Detection of Eavesdropping Attack in Uavaided Wireless Systems: Unsupervised Learning with One-Class Svm and K-Means Clustering", IEEE Wireless Commun. Lett. 9 (2), 139–142.
  • Lin, J., Chen, H., Li, S., Liu, Y., Li, X., Yu, B. 2019. "Accurate Prediction of Potential Druggable Proteins Based on Genetic Algorithm and Bagging-SVM Ensemble Classifier", Artificial Intelligence in Medicine, 98 (2019), 35-47.
  • Gültepe, Y. (2019). Makine Öğrenmesi Algoritmaları ile Hava Kirliliği Tahmini Üzerine Karşılaştırmalı Bir Değerlendirme. Avrupa Bilim ve Teknoloji Dergisi, (16), 8-15, DOI: 10.31590/ejosat.53034
Yıl 2022, Cilt: 15 Sayı: 2, 406 - 419, 31.08.2022
https://doi.org/10.18185/erzifbed.930222

Öz

Kaynakça

  • Outay, F., Mengash, H. A., Adnan, M. 2020. “Applications of Unmanned Aerial Vehicle (UAV) in Road Safety, Traffic and Highway Infrastructure Management: Recent Advances and Challenges”, Transportation Research Part A: Policy and Practice, 141, 116-129.
  • Özbek, E., Yalin, G., Ekici, S., Karakoc, T. H., 2020. “Evaluation of Design Methodology, Limitations, and Iterations of a Hydrogen Fuelled Hybrid Fuel Cell Mini UAV”, Energy, 213 (15).
  • Sabanci, K. 2020. “Artificial Intelligence Based Power Consumption Estimation of Two-Phase Brushless DC Motor According to FEA Parametric Simulation”, Measurement, 155, 1-9.
  • Goswamia, R., Joshib, D. 2018. “Performance Review of Fuzzy Logic Based Controllers Employed in Brushless DC Motor”, Procedia Computer Science, 132 (2018), 623-631.
  • Chen, Q., Liu, G., Zhao, W., Qu, L., Xu., G. 2017. “Asymmetrical SVPWM Fault-Tolerant Control of Five-Phase PM Brushless Motors”, IEEE Trans. Energy Convers., 32(1), 12–22.
  • Megahed, F. T. 2020. “Analytical Approach to Estimate the Polyphase Induction Machine Performance”, J Magn Magn Mater, 514 (15), 167119.
  • Kelek, M. M., Çelik, İ., Fidan, U., & Oğuz, Y. The Simulation of Mathematical Model of Outer Rotor BLDC Motor. Simulation, 2687, 5527.
  • Filippini, M., Hunt, L. C. 2012. “US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach”, Energy Economics, 34 (5), 1484-1491.
  • Gupta, S. K., Singh, O., Khan, M. A., Kushwaha, A. K. 2020. “A Review on Developments of Polyphase Machines”, J Inform Optim Sci, 41 (1), 327-343.
  • Figueroa, J., Brocart, C., Cros, J., Viarouge, P. 2003. “Simplified Simulation Methods for Polyphase Brushless DC Motors”, Math Comput Simulation, 63 (3), 209-224.
  • Khadar, S., Kouzou, A., Hafaifa, A., Atif, I. 2019. “Investigation on SVM-Backstepping Sensorless Control of Five-Phase Open-End Winding Induction Motor Based on Model Reference Adaptive System and Parameter Estimation,” Eng Sci Technol, 22 (4), 1013-1026.
  • Palanisamy, R., Vijayakumar, K. 2020. “ SVPWM Control Strategy for a Three-Phase Five-Level Dual Inverter Fed Open-End Winding Induction Motor,” ISA Trans, 102 (2020), 105-116.
  • Anih, L.U., Obe, E. S. 2019. “Performance Analysis of a Composite Dual-Winding Reluctance Machine”, Energy Convers Manage, 50 (12), 3056-3062.
  • Xin, C., Qin, S., Chenhao, L., Heng Z., Zhiquan, D. 2017. “Direct Control of Torque and Levitation Force for Dual-Winding Bearingless Switched Reluctance”, MotorcElectr Power Syst Res, 145 (2017), 214-222.
  • Sachin, J., Chinthamalla, R., Sanjeevikumar, P., Olorunfemi O., J., Ertas, A. H. 2016. “Dual MPPT Algorithm for Dual PV Source Fed Open-End Winding Induction Motor Drive for Pumping Application,” Eng Sci Technol, 19 (4), 1771-1780.
  • Padmanaban S., Pecht, M. 2016. “An Isolated/Non-Isolated Novel Multilevel Inverter Configuration for a Dual Three-Phase Symmetrical/Asymmetrical Star-Winding Converter”, Eng Sci Technol, 19 (4), 1763-1770.
  • Ogunjuyigbe, A., S., O., Ayodele, T., R., Adetokun, B., B. 2018. “Modelling and Analysis of Dual Stator-Winding Induction Machine Using Complex Vector Approach”, Eng Sci Technol, 21 (3), 351-363.
  • Chatterjee, A., Chatterjee, D. 2015. “An Improved Excitation Control Technique of Three-Phase Induction Machine Operating as Dual Winding Generator for Micro-Wind Domestic Application”, Energy Convers. Manage., 87 (2015), 98-106.
  • Fu, Z., L., Xing, Z., 2020. “Performance Analysis of Dual-Redundancy Brushless DC Motor”, Energy Reports, 6 (9), 829-833.
  • Matlab Simulink. The BLDC data set has been analyzed by transferring it from Matlab Simulink's database to the workspace. Date of last access: 15-01-2021 https://www.mathworks.com/help/physmod/sps/ug/brushless-dc-motor.html
  • Hentati, A. I., Fourati, L., C. 2020. “Comprehensive Survey of UAVs Communication Networks”, Computer Standards & Interfaces, 72 (2020), 103451.
  • Zhang, H., Song, B., Li, F., Xuan J., 2021. “Multidisciplinary Design Optimization of an Electric Propulsion System of a Hybrid UAV Considering Wind Disturbance Rejection Capability in the Quadrotor Mode”, Aerospace Science and Technology, 110, 106372.
  • Dai, B., H., Yang, L. 2020. “Throughput and Energy Efficiency Maximization for UAV-Assisted Vehicular Network”, Physical Communication, 42 (2020), 101136.
  • Hoang, T., M., Nguyen, N., M., Duong, T., Q. 2019. "Detection of Eavesdropping Attack in Uavaided Wireless Systems: Unsupervised Learning with One-Class Svm and K-Means Clustering", IEEE Wireless Commun. Lett. 9 (2), 139–142.
  • Lin, J., Chen, H., Li, S., Liu, Y., Li, X., Yu, B. 2019. "Accurate Prediction of Potential Druggable Proteins Based on Genetic Algorithm and Bagging-SVM Ensemble Classifier", Artificial Intelligence in Medicine, 98 (2019), 35-47.
  • Gültepe, Y. (2019). Makine Öğrenmesi Algoritmaları ile Hava Kirliliği Tahmini Üzerine Karşılaştırmalı Bir Değerlendirme. Avrupa Bilim ve Teknoloji Dergisi, (16), 8-15, DOI: 10.31590/ejosat.53034
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Büşra Hasılcı 0000-0003-2322-5045

Tarık Veli Mumcu 0000-0002-8995-9300

Erken Görünüm Tarihi 29 Ağustos 2022
Yayımlanma Tarihi 31 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 15 Sayı: 2

Kaynak Göster

APA Hasılcı, B., & Mumcu, T. V. (2022). Parameter Estimation of BLDC Motors by SVM for UAV Propulsion Systems. Erzincan University Journal of Science and Technology, 15(2), 406-419. https://doi.org/10.18185/erzifbed.930222