YSA Ve DVM Yöntemlerinin Bir Metro Hattında Gerilim Düşümünün Tahmini İçin Karşılaştırılması

Yıl 2018, Cilt: 10 Sayı: 1, 56 - 65, 29.01.2017

İlhan Kocaarslan Mehmet taciddin Akçay Abdurrahim Akgündoğdu Hasan Tiryaki

https://doi.org/10.29137/umagd.352946

Cited By: 2

Öz

Bu çalışmada, 1500 V DC beslemeli bir raylı sistemde cer gücünün meydana getirdiği gerilim düşümünün maksimum değeri Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM) yardımıyla belirlenmiştir. YSA ve DVM yöntemleriyle hatta oluşan gerilim düşümü işletmesel parametrelere bağlı olarak hesaplanmıştır. YSA ve DVM teknikleri açıklanarak elde edilen sonuçlar karşılaştırılmıştır. YSA modeli için levenberg marquardt (LM) algoritması kullanılmıştır. Levenberg-Marquardt algoritması yapay sinir ağlarının eğitiminde sağladığı hız ve kararlılık nedeni ile tercih edilmektedir. Raylı sistemlerde elektrifikasyon sistemi işletmesel verilere ve hat parametrelerine bağlı olarak tasarlanmaktadır. Elektrifikasyon sistemi oluşturulurken işletme esnasında cer gücünün gereksinimi olan minimum besleme gerilim değerinin sağlanması gerekmektedir. Cer gücü geriliminin en düşük değerini hatta oluşan gerilim düşümünün en yüksek değeri belirlemektedir. Bu değerin işletme sürekliliği için belirli limitler içinde tutulması gerekmektedir. Benzetim için tek yönlü ve çift yönlü beslenme durumlarına ait oluşturulan veriler incelenmiştir. Bu çalışma ile demiryolu elektrifikasyon sistemine ait cer gücü simülasyonuna ait sonuçlar yapay zeka yoluyla tahmin edilmektedir. Bu sayede sisteme ait değişkenler farklı olsa dahi tekrar tekrar benzetim yapılmasının önüne geçilmektedir. Tasarlanan sistem ile %95 üzeri başarı oranı elde edilmiştir.

Anahtar Kelimeler

Cer Gücü, DVM, Elektrifikasyon, Hat, Raylı Sistem, YSA

The Comparison of the ANN and SVM Methods for the Prediction of Voltage Drop on a Subway Line

Öz

In this
study, the determination of the maximum value of the voltage drop created by
the traction force was performed for a 1500 V DC-fed rail system by means of
the artificial neural networks (ANN) and support vector machines (SVM). The
voltage drop occurring on the line was calculated with regard to the operating
parameters by means of the ANN and SVM. The ANN and SVM were explained and a
comparison was made. The Levenberg-Marquardt (LM) algorithm was used for the
ANN model. The Levenberg-Marquardt algorithm was preferred due to the speed and
stability it provides for the training of artificial neural networks. The
electrification system in the rail systems is designed with regard to the
operating data and design parameters. The minimum voltage rating that the
traction force requires during the operation needs to be provided in the
electrification system. The highest value of the voltage drop on the line
determines the lowest value of the traction force voltage. This value must be in
certain limits for the continuity of the operation. The created datas regarding
one-way and two-way supply conditions were examined for simulation. In this way
railway electrification traction power simulation was done by artificial
intelligence methods and traction power simulation results were predicted
without doing traction simulation. With the designed system, the success rate
is over %95.

Anahtar Kelimeler

ANN, Electrification, Line, Rail System, SVM, Traction Force

Kaynakça

• IEEE, Limbong, F., G., 2016. The use of neural network (NN) to predict voltage drop during starting of medium voltage induction motor. 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia.
• IEEE, Nuzzo, S., Galea, M., Gerada, C., Brown, N., L., 2016. Prediction of the voltage drop due to the diode commutation process in the excitation system of salient-pole synchronous generators. 19th International Conference on Electrical Machines and Systems(ICEMS), Chiba, Japan
• Ibrahem, A., Elrayyah, A, Sozer, Y., Garcia, J., A., A., 2017. DC Railway System Emulator for Stray Current and Touch Voltage Prediction. IEEE Transactions on Industry Applications, 53, pp. 439-446.
• IEEE, Meghwani, A., Chakrabarti, S., Srivastava, S., C., 2016. A fast scheme for fault detection in DC microgrid based on voltage prediction. National Power Systems Conference (NPSC), Bhubaneswar, India
• Abrahamsson, L., Kjellqvist, T., Ostlund, S., 2012. High-voltage DC-feeder solution for electric railways. IET Power Electronics, 5, pp. 1776-1784.
• Afsharizadeh, M., Mohammadi, M., 2016. Prediction-Based Reversible İmage Watermarking Using Artificial Neural Networks. Turk J Elec Eng & Comp Sci., 24, pp. 896-910.
• Alamuti, M., M., Nouri, H., Jamali, S., 2011. Effects of earthing systems on stray current for corrosion and safety behaviour in practical metro systems. IET Electrical Systems in Transportation, 1, pp. 69-79.
• Askin, D., Iskender, I., Mamızadeh, A., 2011. Dry Type Transformer Wındıng Thermal Analysıs Usıng Dıfferent Neural Network Methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 26, pp. 905-913.
• Ayhan, S., Erdogmus, S., 2014. Kernel Function Selection for the Solution of Classification Problems via Support Vector Machines. Eskisehir Osmangazi University Journal of IIBF, 9, pp. 175-198.
• Bayindir, R., Sesveren, Ö., 2008. Desıgn Of A Vısual Interface For Ann Based Systems. Pamukkale University Engineering Faculty Journal of Engineering Science, 14, pp. 101-109.
• Cakir, S., Ertunc, H., M., Ocak, H., 2009. A Case Study For Identification of Texture in Carbonate Rocks Using Artificial Neural Networks: Akveren Formation. Journal of Earth Science With Application, 2, pp. 71-79.
• Ceylan, M., Ozbay, Y., Ucan, O., N., Yildirim, E., 2010. A Novel Method For Lung Segmentation On Chest CT İmages: Complex-Valued Artificial Neural Network With Complex Wavelet Transform. Turk J Elec Eng & Comp Sci, 18, pp. 613-623.
• Chai, T., Draxler, R., R., 2014. Root Mean Square Error (RMSE) Or Mean Absolute Error (MAE), Geoscientific Model Development Discussions, 7, pp. 1247-1250.
• Dalkiran, İ., Danisman, K., 2010. Artificial Neural Network Based Chaotic Generator For Cryptology. Turk J Elec Eng & Comp Sci, 18, pp. 225-240.
• Guran, A., Uysal, M., Dogrusoz, O., 2014. Effects Of Support Vector Machines Parameter Optimization On Sentiment Anaylsis. DEÜ Engineering Faculty The Journalof Engineering Sciences, 16, pp. 86-93.
• He, J., Yu, L., Wang, X., Song, X., 2013. Simulation of Transient Skin Effect of DC Railway System Based on MATLAB/Simulink. IEEE Transactions On Power Delivery, 28, pp. 145-152.
• IEEE, Calderaro, V., Galdi, V., Graber, G., Piccolo, A., Capasso, A., Lamedica, R., Ruvio, A., 2015, November. Energy Management of Auxiliary Battery Substation Supporting High-Speed Train on 3 kV DC Systems. Renewable Energy Research and Applications ( Icrera), Palermo, Italy.
• IEEE, Jia, Z., Yang, Z., Lin, F., Fang, X., 2014, September. Dynamic Simulation of the DC Traction Power System Considering Energy Storage Devices. Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China.
• IEEE, Tian, Z., Hillmansen, S., Roberts, C, Weston, P., Chen, L., Zhao, N., Su, S., Xin, T. 2014, October. Modeling and Simulation of DC Rail Traction Systems for Energy Saving. 17th International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.
• Jashfar, S., Esmaeili, S., Jahromi, M., Z., Rahmanian, M., 2013. Classification of Power Quality Disturbances Using S-Transform And TT-Transform Based On The Artificial Neural Network. Turk J Elec Eng & Comp Sci., 21, pp. 1528-1538.
• Jung, S., Lee, H., Song, C.,S., Han, J., H., Han, W., K., Jang, G., 2013. Optimal Operation Plan of the Online Electric Vehicle System Through Establishment of a DC Distribution System. IEEE Transactions On Power Electronics, 28, pp. 5878-5889.
• Kavzaoglu, T., Colkesen, I., 2010. Investigation of the Effects of Kernel Functions in Satellite Image Classification Using Support Vector Machines. Gebze High Technology Institute The Journal of Map, 144, pp. 73-82.
• Lao, K., W., Wong, M., C., Dai, N., Y., Liu, W., G., Wong, M., C., 2013. Hybrid Power Quality Compensator With Minimum DC Operation Voltage Design for High-Speed Traction Power Systems. IEEE Transactions On Power Electronics, 28, pp. 2024-2036.
• Lao, K., W., Wong, M., C., Dai, N., Y., Wong, C., K., Lam, C., S., 2016. Analysis of DC-Link Operation Voltage of a Hybrid Railway Power Quality Conditioner and Its PQ Compensation Capability in High-Speed Cophase Traction Power Supply. IEEE Transactions On Power Electronics, 31, pp. 1643-1656.
• Ogunsola, A., Sandrolini, L., Mariscotti, A., 2015. Evaluation of Stray Current From a DC Electrified Railway With Integrated Electric–Electromechanical Modeling and Traffic Simulation. IEEE Transactions On Industry Applications, 51, pp. 5431-5441.
• Ozdemir, H., 2013. Artificial Neural Networks and Their Usage in Weaving Technology. Electronic Journal of Textile Technologies, 7, pp. 51-68.
• Park, J., D., 2015. Ground Fault Detection And Location For Ungrounded DC Traction Power Systems. IEEE Transactıons on Vehicular Technology, 64, pp. 5667-5676.
• Partal, S., Senol, İ., Bakan, A., F., Bekiroglu, K., N., 2011. Online Speed Control of a Brushless AC Servomotor Based On Artificial Neural Networks. Turk J Elec Eng & Comp Sci., 19, pp. 373-383.
• Sahin, M., Buyuktumturk, F., Oguz, Y., 2013. Light Quality Control with Artificial Neural Networks. Afyon Kocatepe University Journal of Science and Engineering, 13, pp. 1-10.
• Smidl, V., Janous, S., Peroutka, Z. , 2015. Improved Stability of DC Catenary Fed Traction Drives Using Two-Stage Predictive Control. IEEE Transactions On Industrial Electronics, 62, pp. 3192-3201.
• Takagi, R. , 2012. Preliminary evaluation of the energy-saving effects of the introduction of superconducting cables in the power feeding network for DC electric railways using the multi-train power network simulator. IET Electrical Systems in Transportation, 2, pp. 103-109.
• Takagi, R., Amano, T., 2014. Optimisation of reference state-of-charge curves for the feed-forward charge/discharge control of energy storage systems on-board DC electric railway vehicles. IET Electrical Systems in Transportation, 5, pp. 33-42.
• Torreglosa, J., P., García, P., Fernández, L., M., 2014. Predictive Control for the Energy Management of a Fuel-Cell–Battery–Supercapacitor Tramway. IEEE Transactıons On Industrıal Informatıcs,10, pp. 276-285.
• Tzeng, Y., S., Lee, C., H., 2010. Analysis of Rail Potential and Stray Currents in a Direct Current Transit System. IEEE Transactions On Power Delivery, 25, pp. 1516-1525.
• Wang, W., Cheng, M., Wang, Y., Zhang, B., Zhu, Y., Ding, S.,Chen, W., 2014. A Novel Energy Management Strategy of Onboard Supercapacitor for Subway Applications With Permanent-Magnet Traction System. IEEE Transactions On Power Electronics, 63, pp. 2578-2588.
• Willmott, C., J., Matsuura, C., 2005. Advantages Of The Mean Absolute Error (MAE) Over The Root Mean Square Error (RMSE) İn Assessing Average Model Performance, Climate Research, 30, pp. 79-82.
• Xu, S., Y., Li, W., Wang, Y., Q., 2013. Effects of Vehicle Running Mode on Rail Potential and Stray Current in DC Mass Transit Systems. IEEE Transactions On Vehicular Technology, 62, pp. 3569-3580.
• Yakut, E., Elmas, B., Yavuz, S., 2014. Predıctıng Stock-Exchange Index Usıng Methods Of Neural Networks And Support Vector Machınes. Suleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, 19, pp. 139-157.
• Yurtcu, S., Ozocak, A., 2016. Prediction Of Compression İndex Of Fine-Grained Soils Using Statistical And Artificial İntelligence Methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 31, pp. 597-608.