Research Article
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Year 2024, Volume: 9 Issue: 1, 34 - 41, 30.12.2024
https://doi.org/10.55088/ijesg.1586449

Abstract

References

  • Akil, M., Dokur, E., and Bayindir, R., “Impact of electric vehicle charging profiles in data-driven framework on distribution network”, Proceeding of 9th International Conference on Smart Grid, icSmartGrid 2021, pp. 220–225, 2021.
  • Mishra, M., “Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review”, International Transactions on Electrical Energy Systems, 29(8), pp. 1–42 , 2019.
  • Gong, J., Li, D., Wang, T., Pan, W., and Ding, X., “A comprehensive review of improving power quality using active power filters”, Electric Power Systems Research, 199, pp. 1-15, 2021.
  • Özer, İ., Efe, S. B., and Özbay, H., “CNN / Bi-LSTM-based deep learning algorithm for classification of power quality disturbances by using spectrogram images”, International Transactions on Electrical Energy Systems, 31(12), pp. 1–16 , 2021.
  • Efe, S. B., “Analysis of power system interharmonics”, International Engineering, Science and Education Conference, pp. 1039–1042 , 2016.
  • Efe, S. B., Özbay, H., and Özer, İ., “Dynamic Voltage Restorer Application to Eliminate Power System Harmonics”, International Engineering and Natural Sciences Conference (IENSC 2019), pp. 705–709, 2019.
  • Clement Veliz, F., Varricchio, S. L., and de Oliveira Costa, C., “Determination of harmonic contributions using active filter: Theoretical and experimental results”, International Journal of Electrical Power and Energy Systems, 137, pp. 1–11,2022.
  • Senol, M., Safak Bayram, I., Campos-Gaona, D., Sevdari, K., Gehrke, O., Pepper, B., and Galloway, S., “Measurement-based Harmonic Analysis of Electric Vehicle Smart Charging”, Proceeding of 2024 IEEE Transportation Electrification Conference and Expo, ITEC 2024, Institute of Electrical and Electronics Engineers Inc. , 2024.
  • Chen, C. I. and Chen, Y. C., “Comparative study of harmonic and interharmonic estimation methods for stationary and time-varying signals”, IEEE Transactions on Industrial Electronics, 61(1), pp. 397–404 , 2014.
  • Severoglu, N. and Salor, O., “Amplitude and phase estimations of power system harmonics using deep learning framework”, IET Generation, Transmission and Distribution, 14(19), pp. 4089–4096, 2020.
  • Fatima, S. S. W. and Rahimi, A., “A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems”, Machines, 12(6), pp.1-30, 2024.
  • Al Hadi, F. M. and Aly, H. H., “Harmonics Forecasting of Renewable Energy System using Hybrid Model based on LSTM and ANFIS”, IEEE Access , 12, pp. 50966-50985, 2024.
  • Wang, N., Sun, M., and Xi, X., “Identification of power quality disturbance characteristic based on deep learning”, Electric Power Systems Research, 226, pp. 1–8, 2024.
  • Adak, S. and Cangi, H., “Elimination of Harmonic Components in Solar System With L And LC Passive Filters”, International Journal of Energy and Smart Grid, 6(1–2), pp. 14–27 ,2021.
  • Adak, S. and Cangi, H., “Harmonic Distortion of Input Current Induction Motor According To Switching Frequency in Off-Grid Photovoltaic Systems”, International Journal of Energy and Smart Grid, 5(1–2), pp. 27–40 , 2020.
  • Kılıç, L., “Assessing and Improving Recommendations For Local Power Quality Efficiency For Industrial Plants With The Help Of Real Data”, International Journal of Energy and Smart Grid, 4(1), pp. 12-20, 2019.
  • Obut, N. and Tür, R., “Measurement and Evaluation of Power Quality Parameters Batman Province Application”, International Journal of Energy and Smart Grid, 6(1–2), pp. 37–45, 2021.
  • Kuyumani, E. M., Hasan, A. N., and Shongwe, T., “A Hybrid Model Based on CNN-LSTM to Detect and Forecast Harmonics: A Case Study of an Eskom Substation in South Africa”, Electric Power Components and Systems, 51(8), pp.746-760, 2023.
  • Severoglu, N. and Salor, O., “Statistical Models of EAF Harmonics Developed for Harmonic Estimation Directly from Waveform Samples Using Deep Learning Framework”, IEEE Trans Ind Appl, 57(6), pp. 6730–6740 ,2021.
  • Sahoo, H. K. and Subudhi, U., “Power System Harmonics Estimation Using Adaptive Filters”, Compendium of New Techniques in Harmonic Analysis, 2018, ch.6, pp. 117-137.
  • Bayram, I. S., (2024, Sep. 16). “Data for: ‘Measurement-based Harmonic Analysis of Electric Vehicle Smart Charging’”, [Online]. Available: https://pureportal.strath.ac.uk/en/datasets/data-for-measurement-based-harmonic-analysis-of-electric-vehicle-.
  • Hatata, A. Y. and Eladawy, M., “Prediction of the true harmonic current contribution of nonlinear loads using NARX neural network”, Alexandria Engineering Journal, 57(3), pp. 1509–1518, 2018.
  • Boussaada, Z., Curea, O., Remaci, A., Camblong, H., and Bellaaj, N. M., “A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation”, Energies (Basel), 11(3), pp. 1-21, 2018.

HARMONIC PREDICTION FOR ELECTRIC VEHICLES IN DIFFERENT CHARGING CONDITIONS

Year 2024, Volume: 9 Issue: 1, 34 - 41, 30.12.2024
https://doi.org/10.55088/ijesg.1586449

Abstract

This paper proposes a machine learning approach to predict the harmonics that occur during charging electric vehicles. Since charging of electric vehicles has a negative impact on the distribution system, this study uses a real-world dataset for harmonic estimation to ensure that all parameters during charging are taken into account and an accurate analysis is performed. An open-source dataset that consist of various charging currents and the resulting harmonics associated with these currents was used. In the study, the effective nonlinear autoregressive exogenous model approach was used. In order to make detailed prediction and to reveal the actual performance of the model, separate applications were made for each harmonic level.

References

  • Akil, M., Dokur, E., and Bayindir, R., “Impact of electric vehicle charging profiles in data-driven framework on distribution network”, Proceeding of 9th International Conference on Smart Grid, icSmartGrid 2021, pp. 220–225, 2021.
  • Mishra, M., “Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review”, International Transactions on Electrical Energy Systems, 29(8), pp. 1–42 , 2019.
  • Gong, J., Li, D., Wang, T., Pan, W., and Ding, X., “A comprehensive review of improving power quality using active power filters”, Electric Power Systems Research, 199, pp. 1-15, 2021.
  • Özer, İ., Efe, S. B., and Özbay, H., “CNN / Bi-LSTM-based deep learning algorithm for classification of power quality disturbances by using spectrogram images”, International Transactions on Electrical Energy Systems, 31(12), pp. 1–16 , 2021.
  • Efe, S. B., “Analysis of power system interharmonics”, International Engineering, Science and Education Conference, pp. 1039–1042 , 2016.
  • Efe, S. B., Özbay, H., and Özer, İ., “Dynamic Voltage Restorer Application to Eliminate Power System Harmonics”, International Engineering and Natural Sciences Conference (IENSC 2019), pp. 705–709, 2019.
  • Clement Veliz, F., Varricchio, S. L., and de Oliveira Costa, C., “Determination of harmonic contributions using active filter: Theoretical and experimental results”, International Journal of Electrical Power and Energy Systems, 137, pp. 1–11,2022.
  • Senol, M., Safak Bayram, I., Campos-Gaona, D., Sevdari, K., Gehrke, O., Pepper, B., and Galloway, S., “Measurement-based Harmonic Analysis of Electric Vehicle Smart Charging”, Proceeding of 2024 IEEE Transportation Electrification Conference and Expo, ITEC 2024, Institute of Electrical and Electronics Engineers Inc. , 2024.
  • Chen, C. I. and Chen, Y. C., “Comparative study of harmonic and interharmonic estimation methods for stationary and time-varying signals”, IEEE Transactions on Industrial Electronics, 61(1), pp. 397–404 , 2014.
  • Severoglu, N. and Salor, O., “Amplitude and phase estimations of power system harmonics using deep learning framework”, IET Generation, Transmission and Distribution, 14(19), pp. 4089–4096, 2020.
  • Fatima, S. S. W. and Rahimi, A., “A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems”, Machines, 12(6), pp.1-30, 2024.
  • Al Hadi, F. M. and Aly, H. H., “Harmonics Forecasting of Renewable Energy System using Hybrid Model based on LSTM and ANFIS”, IEEE Access , 12, pp. 50966-50985, 2024.
  • Wang, N., Sun, M., and Xi, X., “Identification of power quality disturbance characteristic based on deep learning”, Electric Power Systems Research, 226, pp. 1–8, 2024.
  • Adak, S. and Cangi, H., “Elimination of Harmonic Components in Solar System With L And LC Passive Filters”, International Journal of Energy and Smart Grid, 6(1–2), pp. 14–27 ,2021.
  • Adak, S. and Cangi, H., “Harmonic Distortion of Input Current Induction Motor According To Switching Frequency in Off-Grid Photovoltaic Systems”, International Journal of Energy and Smart Grid, 5(1–2), pp. 27–40 , 2020.
  • Kılıç, L., “Assessing and Improving Recommendations For Local Power Quality Efficiency For Industrial Plants With The Help Of Real Data”, International Journal of Energy and Smart Grid, 4(1), pp. 12-20, 2019.
  • Obut, N. and Tür, R., “Measurement and Evaluation of Power Quality Parameters Batman Province Application”, International Journal of Energy and Smart Grid, 6(1–2), pp. 37–45, 2021.
  • Kuyumani, E. M., Hasan, A. N., and Shongwe, T., “A Hybrid Model Based on CNN-LSTM to Detect and Forecast Harmonics: A Case Study of an Eskom Substation in South Africa”, Electric Power Components and Systems, 51(8), pp.746-760, 2023.
  • Severoglu, N. and Salor, O., “Statistical Models of EAF Harmonics Developed for Harmonic Estimation Directly from Waveform Samples Using Deep Learning Framework”, IEEE Trans Ind Appl, 57(6), pp. 6730–6740 ,2021.
  • Sahoo, H. K. and Subudhi, U., “Power System Harmonics Estimation Using Adaptive Filters”, Compendium of New Techniques in Harmonic Analysis, 2018, ch.6, pp. 117-137.
  • Bayram, I. S., (2024, Sep. 16). “Data for: ‘Measurement-based Harmonic Analysis of Electric Vehicle Smart Charging’”, [Online]. Available: https://pureportal.strath.ac.uk/en/datasets/data-for-measurement-based-harmonic-analysis-of-electric-vehicle-.
  • Hatata, A. Y. and Eladawy, M., “Prediction of the true harmonic current contribution of nonlinear loads using NARX neural network”, Alexandria Engineering Journal, 57(3), pp. 1509–1518, 2018.
  • Boussaada, Z., Curea, O., Remaci, A., Camblong, H., and Bellaaj, N. M., “A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation”, Energies (Basel), 11(3), pp. 1-21, 2018.
There are 23 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Research Article
Authors

Serhat Berat Efe 0000-0001-6076-4166

Publication Date December 30, 2024
Submission Date November 16, 2024
Acceptance Date December 16, 2024
Published in Issue Year 2024 Volume: 9 Issue: 1

Cite

IEEE S. B. Efe, “HARMONIC PREDICTION FOR ELECTRIC VEHICLES IN DIFFERENT CHARGING CONDITIONS”, IJESG, vol. 9, no. 1, pp. 34–41, 2024, doi: 10.55088/ijesg.1586449.

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