Regression Model Extractions of a T-Equivalent Circuit Modelling for Medium-Length Transmission Line Based-on the Parametric Simulation Approach

Year 2024, Volume: 27 Issue: 4, 1649 - 1658, 25.09.2024

Selami Balcı , Mustafa Akkaya

https://doi.org/10.2339/politeknik.1456959

Cited By: 1

Abstract

In medium-length power transmission line models, the difference between the end-of-line and head-of-line voltage can be calculated with classical mathematical expressions. However, since the line parameters are not linear, these calculations can be approximated according to certain assumptions. The parametric data analysis approach proposed in this study obtained a data set for different variations by changing the line length and line parameters (transmission line specific parameters such as resistance, inductance, and capacitance) with certain steps. Then, using this data set, a classification is made with machine learning. In addition, data analysis is carried out with the end-of-line voltage value graphs obtained with different line parameters and the proposed approach is verified by constructing a test simulation circuit of a three-phase 200 km length with 154 kV line voltage value. Thus, a parametric simulation study has been presented, especially in electrical engineering education. In addition, Support Vector Regression (SVR) and Decision Tree Regression (DTR) models in the field of machine learning were used to measure the consistency of the data set created for 5 pF, 8 pF and 10 pF capacity values. With the figures and numerical data presented comparatively, it is clearly seen that the Long Short-Term Memory (LSTM) algorithm produces more successful scores in all three categories. In this context, the prediction accuracy was between 97% and 98% with DTR, while the accuracy was between 81% and 85% with SVR. Thus, prediction results in the range of 98% - 99% were obtained in the LSTM model.

Keywords

Transmission line parameters, T-equivalent circuit modelling, DTR, SVR, LSTM

Parametrik Simülasyon Yaklaşımına Dayalı Orta Uzunluktaki İletim Hattı için T-Eşdeğer Devre Modellemesinin Regresyon Modeli Çıkarımları

Abstract

Orta uzunlukta enerji nakil hattı modellerinde hat sonu ve hat başı gerilimi arasındaki fark klasik matematiksel ifadelerle hesaplanabilmektedir. Ancak hat parametreleri doğrusal olmadığından bu hesaplamalara belirli varsayımlara göre yaklaşılabilir. Bu çalışmada önerilen parametrik veri analizi yaklaşımı, hat uzunluğunu ve hat parametrelerini (direnç, endüktans, kapasitans gibi iletim hattına özgü parametreler) belirli adımlarla değiştirerek farklı varyasyonlar için bir veri seti elde etmiştir. Daha sonra bu veri seti kullanılarak makine öğrenmesi ile bir sınıflandırma yapılmıştır. Ayrıca farklı hat parametreleri ile elde edilen hat sonu gerilim değeri grafikleri ile veri analizi yapılmış ve önerilen yaklaşım, 200 km uzunluğunda, 154 kV hat gerilim değerine sahip üç fazlı bir test simülasyon devresi kurularak doğrulanmıştır. Böylece özellikle elektrik mühendisliği eğitiminde parametrik bir simülasyon çalışması ortaya konmuştur. Ayrıca makine öğrenimi alanında Destek Vektör Regresyonu (SVR) ve Karar Ağacı Regresyonu (DTR) modelleri kullanılarak 5 pF, 8 pF ve 10 pF kapasite değerleri için oluşturulan veri setinin tutarlılığı ölçüldü. Karşılaştırmalı olarak sunulan rakamlar ve sayısal verilerle Uzun Kısa Süreli Bellek (LSTM) algoritmasının her üç kategoride de daha başarılı puanlar ürettiği açıkça görülüyor. Bu bağlamda DTR ile tahmin doğruluğu %97 ile %98 arasında, SVR ile ise doğruluk %81 ile %85 arasında gerçekleşti. Böylece LSTM modelinde %98 - %99 aralığında tahmin sonuçları elde edilmiştir.

Keywords

İletim hattı parametreleri, T-eşdeğer devre modellemesi, DTR, SVR, LSTM.

References

• [1] Karaer E., “An Examination of Power Transmission Line Parameters Estimation”, Thesis (M.Sc.) İstanbul Technical University, Institute of Science and Technology, Turkiye, (2005).
• [2] Gönen T., “Electric Power Transmission System Engineering, Analysis and Design”, J. Wiley, New York, USA, (1988).
• [3] Atalay H., “Transmission Technique, Karadeniz Technical University”, Mechanical-Electric Faculty Publications. 5, Turkiye, (1977).
• [4] Chen Y., Hu Z., Zhang C., “A Study of Parameters Live Measurement of Transmission Lines with Mutual-inductance Based on GPS”, IEEE Power Engineering Society Winter Meeting, 4: 2658-2663, (2000).
• [5] Mercy, E. L., & Jyosthna, G., “Fault detection and classification in transmission line using DWT and ANFIS techniques”, Advanced Research in Electrical and Electronic Engineering, 2(2): 123-129, (2014).
• [6] Hassan, T. S. K. M. M. “Adaptive neuro fuzzy inference system (ANFIS) for fault classification in the transmission lines”, Online J. Electron. Electr. Eng.(OJEEE), 2: 2551-2555, (2010).
• [7] Azriyenni, A., & Mustafa, M. W., “Application of ANFIS for Distance Relay Protection in Transmission Line”, International Journal of Electrical and Computer Engineering, 5(6): (2015).
• [8] Vlahinić, S., Franković, D., Ðurović, M. Ž., & Stojković, N., “Measurement Uncertainty Evaluation of Transmission Line Parameters”, IEEE Transactions on Instrumentation and Measurement, 70: 1-7, (2021).
• [9] Li, X., Li, F., Liu, P., Cai, W., & Cai, Z. “Modeling Approach for Short-Transmission Lines on the same Tower with Different Wire Parameters and Tower Structure”, IEEE International Conference on Power System Technology (POWERCON), 376-383, (2018).
• [10] Pal, S., Sikdar, B., & Chow, J. H., “Classification and detection of PMU data manipulation attacks using transmission line parameters”, IEEE Transactions on Smart Grid, 9(5): 5057-5066, (2017).
• [11] Huang, N., Qi, J., Li, F., Yang, D., Cai, G., Huang, G., and Li, Z., “Short-circuit fault detection and classification using empirical wavelet transform and local energy for electric transmission line”. Sensors, 17(9): 2133, (2017).
• [12] Coban, M., & Tezcan, S. S., “Detection and classification of short circuit faults on transmission line using current signal”, Bulletin of the Polish Academy of Sciences: Technical Sciences, e137630-e137630, (2021).
• [13] Akmaz, D., Mamiş, M. S., Arkan, M., and Tağluk, M. E. “Transmission line fault location using traveling wave frequencies and extreme learning machine”, Electric Power Systems Research, 155: 1-7, (2018).
• [14] Fei, C., Qi, G., and Li, C. “Fault location on high voltage transmission line by applying support vector regression with fault signal amplitudes”, Electric Power Systems Research, 160: 173-179, (2018).
• [15] Bendjabeur, A., Kouadri, A., and Mekhilef, S. “Transmission line fault location by solving line differential equations”. Electric Power Systems Research, 192: 106912, (2021).
• [16] Ghaedi, A., Golshan, M. E. H., and Sanaye-Pasand, M., “Transmission line fault location based on three-phase state estimation framework considering measurement chain error model”. Electric Power Systems Research, 178: 106048, (2020).
• [17] Özer, T., & Türkmen, Ö., “An approach based on deep learning methods to detect the condition of solar panels in solar power plants”. Computers and Electrical Engineering, 116: 109143, (2024).
• [18] Sebastian, P. K., Deepa, K., Neelima, N., Paul, R., & Özer, T., A comparative analysis of deep neural network models in IoT‐based smart systems for energy prediction and theft detection. IET Renewable Power Generation, 18(3): 398-411, 2024.
• [19] Ganguly, T., Pati, P. B., Deepa, K., Singh, T., & Özer, T., “Machine learning based comparative analysis of cervical cancer risk classifications algorithms”, In 2023 international conference on advances in computing, communication and applied informatics (ACCAI) (pp. 1-7). IEEE, (2023).
• [20] Indulkar, C. S., Ramalingam, K., “Estimation of transmission line parameters from measurements”, International Journal of Electrical Power & Energy Systems, 30(5): 337-342, (2008).
• [21] Sarajcev, P., “Monte Carlo method for estimating backflashover rates on high voltage transmission lines”, Electric Power Systems Research, 119: 247-257, (2015).
• [22] Ritzmann, D., Wright, P. S., Holderbaum W., and Potter, B., "A Method for Accurate Transmission Line Impedance Parameter Estimation," in IEEE Transactions on Instrumentation and Measurement, 65(10): 2204-2213, Oct. ,doi: 10.1109/TIM.2016.2556920, (2016).
• [23] Davis, K. R., Dutta, S., Overbye, T. J., and Gronquist, J., "Estimation of Transmission Line Parameters from Historical Data," 46th Hawaii International Conference on System Sciences, Wailea, HI, USA, 2151-2160, doi: 10.1109/HICSS.2013.206, (2013).
• [24] Asprou, M., and Kyriakides, E., "Estimation of transmission line parameters using PMU measurements," IEEE Power & Energy Society General Meeting, Denver, CO, USA, 1-5, doi: 10.1109/PESGM.2015.7285847, (2015).
• [25] Costa, E.C.M. and Kurokawa, S. “Estimation of transmission line parameters using multiple methods”, IET Gener. Transm. Distrib., 9(16): 2617–2624, (2015).
• [26] Felipe P. Albuquerque, Eduardo C. Marques Costa, Luísa H. B. Liboni, Ronaldo F. Ribeiro Pereira, and Maurício C. de Oliveira, “Estimation of transmission line parameters by using two least-squares methods”, IET Gener. Transm. Distrib. ;15:568–575, (2021).
• [27] Wang, Y., Xu, W., and Shen, J., "Online Tracking of Transmission-Line Parameters Using SCADA Data," in IEEE Transactions on Power Delivery, 31(2): 674-682, April 2016, doi: 10.1109/TPWRD.2015.2474699, (2016).
• [28] Liao, Y., "Power transmission line parameter estimation and optimal meter placement," Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon), Concord, NC, USA, 2010, 250-254, doi: 10.1109/SECON.2010.5453876, (2010).
• [29] Liao, Y., "Some algorithms for transmission line parameter estimation," IEEE 41st Southeastern Symposium on System Theory, Tullahoma, TN, USA,, 127-132, doi: 10.1109/SSST.2009.4806781, (2009).
• [30] Sivanagaraju, G., Chakrabarti, S, and Srivastava, S. C., "Uncertainty in Transmission Line Parameters: Estimation and Impact on Line Current Differential Protection," in IEEE Transactions on Instrumentation and Measurement, 63(6): 1496-1504, doi: 10.1109/TIM.2013.2292276, (2014).
• [31] Ansys Electronics 2024R1 Desktop, Academic Version, Twin Builder Examples, Transmission Line Modeling Help Datasheet.
• [32] Khodapanah, M., Zobaa, A. F., & Abbod, “Estimating power factor of induction motors at any loading conditions using support vector regression (SVR)”, Electrical Engineering, 100(4): 2579-2588, (2018).
• [33] Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., and Wang, K. “Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings”, Applied Energy, 195: 659-670, (2017).
• [34] Ahmad, M. W., Mourshed, M., and Rezgui, Y. “Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression”. Energy, 164: 465-474, (2018).
• [35] Ahmad, M. W., Reynolds, J., and Rezgui, Y. “Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees”, Journal of cleaner production, 203, 2018, 810-821, (2018).
• [36] Wang, J., Li, P., Ran, R., Che, Y., and Zhou, Y.. A “Short-term photovoltaic power prediction model based on the gradient boost decision tree”, Applied Sciences, 8(5): 689, (2018).
• [37] Persson, C., Bacher, P., Shiga, T., and Madsen, H. “Multi-site solar power forecasting using gradient boosted regression trees”, Solar Energy, 150: 423-436, (2017).
• [38] Zahid, M., Ahmed, F., Javaid, N., Abbasi, R. A., Zainab Kazmi, H. S., Javaid, A., and Ilahi, M. “Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids”, Electronics, 8(2): 122, (2019).
• [39] Smola, A. J. and Schölkopf, B. ,”A tutorial on support vector regression”, Statistics and computing, 14(3): 199-222, (2004).
• [40] Sarkar, A., Maity, P. P., Ray, M., Chakraborty, D., Das, B., and Bhatia, A. “Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil”, Ecological Informatics, 74: 101959, (2023).
• [41] Tso, G. K. and Yau, K. K. “Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks”. Energy, 32(9): 1761-1768, (2007).
• [42] Shaikh, M.S., Raj, C.H.S., Kumar, S., Hassan, M., Ansari, M.M., and Jatoi, M.A., “Optimal parameter estimation of 1-phase and 3-phase transmission line for various bundle conductor’s using modified whale optimization algorithm”, International Journal of Electrical Power & Energy Systems, 138: 107893, (2022).