Wavelet transform and damped recursive least squares method for evaluation of measurement uncertainty in EV charging pile meters
DOI:
https://doi.org/10.24425/mms.2024.150286Abstract
To solve the problem of inaccurate estimation of relative errors in real-time monitoring of charging pile meters, a model is proposed based on the wavelet transform and damped recursive least squares (WT-DRLS) method to assess the measurement error and uncertainty of electric meters. An energy conservation equation for the charging pile power system is established, along with two variables representing energy conversion efficiency and measurement error. The estimated value of the energy conversion efficiency is obtained by using wavelet transform for noise reduction. Subsequently, a damped recursive least square method with a sliding window is developed to exclude disturbances from circuit load flow and external environmental factors, which enables the calculation of the measurement uncertainty of electric meters. The proposed method supports online monitoring of charging pile meter performance. Data from an actual DC charging station are collected for validation. The experimental results show that the proposed method is effective and stable and outperforms the state-of-the-art methods.References
[1] Cheng, H., Wang, Z., Cai, Q., Lu, X., Gao, Y., Song, R., Tian, Z. & Mu, X. (2019). Error analysis of the three-phase electrical energy calculation method in the case of voltage-loss failure. Metrology and Measurement Systems, 26(3), 505-516. https://doi.org/10.24425/mms.2019.129575
[2] Ying, Z., Wang, Y., He, Y. & Wang, J. (2022). Virtual sensing techniques for nonlinear dynamic processes using weighted probability dynamic dual-latent variable model and its industrial applications. Knowledge-Based Systems, 20(3), 235, 107642. https://doi.org/10.1016/j.knosys.2021.107642
[3] Wang, X., Wang, J., Yuan, R. & Jiang, Z. (2019). Dynamic error testing method of electricity meters by a pseudo random distorted test signal. Applied Energy, 249. https://doi.org/10.1016/j.apenergy.2019.04.054
[4] He, Y., Ying, Z., & Wang, Y. (2023). Enhanced dynamic dual-latent variable model for multi-rate process monitoring and its industrial application. IEEE Transactions on Instrumentation and Measurement, 72, 1-8. https://doi.org/10.1109/TIM.2022.3180436
[5] Hu, J. & Vasilakos, A. V. (2016). Energy big data analytics and security: challenges and opportunities. IEEE transactions on smart grid, 7(5), 2423-2436. https://doi.org/10.1109/TSG.2016.2563461
[6] He, Y., Lou, R., Wang, Y., Wang, J. & Fang, Y. (2022). A dual attribute weighted decision fusion system for fault classification based on an extended analytic hierarchy process. Engineering Applications of Artificial Intelligence, 114, 105066. https://doi.org/10.1016/j.engappai.2022.105066
[7] Kong, X., Zhang X. & Bai L. (2022). A remote estimation method of smart meter errors based on neural network filter and generalized damping recursive least square. IEEE Transactions on Industrial Informatics, 18(1), 219-230. https://doi.org/10.1109/TII.2021.3074420
[8] Liu, F., Liang, C., He, Q., Wang, L., Huang, C. & Hu S. (2020). An approach for online smart meter error estimation. Conference on Precision Electromagnetic Measurements (CPEM). https://doi.org/10.1109/cpem49742.2020.9191736
[9] Fan J. (2022). Using computer-aided technology to error analysis of electric energy metering system. Computer-Aided Design and Applications, 19(S4), 169-179.
[10] Yip, S. C., Wong, K., Hew, W. P., Gan, M. T., Phan, R. C. W., & Tan, S. W. (2017). Detection of energy theft and defective smart meters in smart grids using linear regression. International Journal of Electrical Power & Energy Systems, 91, 230-240. https://doi.org/10.1016/j.ijepes.2017.04.005
[11] Kong, X., Ma, Y., Zhao, X., Li Y. & Teng Y. (2019). A recursive least squares method with double-parameter for online estimation of electric meter errors. Energies, 12(5). https://doi.org/10.3390/en12050805
[12] Liu, F., Liang, C. & He, Q. (2020). Remote malfunctional smart meter detection in edge computing environment. IEEE Access, 8, 67436-67443. https://doi.org/10.1109/ACCESS.2020.2985725
[13] Ma, J., Teng, Z., Tang, Q., Guo, Z., Kang, L., Li N. & Peretto L. (2023). A Novel Multisource Feature Fusion Framework for Measurement Error Prediction of Smart Electricity Meters. IEEE Sensors Journal, 23(17), 19571-19581. https://doi.org/10.1109/ACCESS.2020.2985725
[14] Liu, M., Liu, D., Sun, G., Zhao, Y., Wang, D., Liu, F., Fang, X., He, Q., & Xu, D. (2020). Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study. IEEE Industrial Electronics Magazine, 14(4), 79-90. https://doi.org/10.1109/MIE.2020.3026197
[15] Huang Y. & Feng L. (2023). Interval State Estimation of Electricity-Gas Systems Considering Uncertainties of Network Parameters and Measurements. IEEE Transactions on Instrumentation and Measurement, 72, 1-12. https://doi.org/10.1109/TIM.2023.3287249
[16] Duan, J., Zuo, H., Bai, Y., Duan, JZ., Chang, M. & Chen, B. (2021). Short-term wind speed forecasting using recurrent neural networks with error correction. Energy, 217, 119397. https://doi.org/10.1016/j.energy.2020.119397
[17] Sehovac, L., Nesen, C. & Grolinger, K. (2019). Forecasting building energy consumption with deep learning: A sequence to sequence approach. 2019 IEEE International Congress on Internet of Things (ICIOT), 108-116. https://doi.org/10.1109/ICIOT.2019.00029
[18] Liu, M., Liu, D., Sun, G., Liu, D., Sun, G., Zhao, Y., Wang, D., Liu, F., Fang, X., He, Q. & Xu, D. (2020). Deep learning detection of inaccurate smart electricity meters: a case study. IEEE Industrial Electronics Magazine, 14(4), 79-90. https://doi.org/10.1109/MIE.2020.3026197
[19] Demerdziev, K. & Dimchev, V. (2023). Reactive power and energy instrument’s performance in non-sinusoidal conditions regarding different power theories. Measurement Science Review, 23(1), 19-31. https://doi.org/10.2478/msr-2023-0003
[20] Saunoris, M., Nakutis, Z. & Knyva, M. (2022). Estimation of energy meter accuracy using remote non-invasive observation. Measurement Science Review, 22(4), 170-176. https://doi.org/10.2478/msr-2022-0021
[21] Yaprakdal, F. & Arisoy, M. V. (2023). A multivariate time series analysis of electrical load forecasting based on a hybrid feature selection approach and explainable deep learning. Applied Sciences, 13(23). https://doi.org/10.3390/app132312946
[22] Matuszewski, J. & Kraszewski, T. (2021). Evaluation of emitter location accuracy with the modified triangulation method by means of maximum likelihood estimators. Metrology and Measurement Systems, 28(4), 751-765. https://doi.org/10.24425/mms.2021.138537
[23] Tiryaki, E., Kocahan, Ö. & Özder, S. (2021). An improved method for determination of refractive index of dielectric films from reflectance spectrum by using the Generalized Morse Wavelet. Measurement Science Review, 21(2), 61-66. https://doi.org/10.2478/msr-2021-0009
[24] Dzemic, Z., Sirok, B. & Drnovsek, J. (2019). Traceability of gas flow measurements in complex distribution systems - uncertainty approach vs error approach. Metrology and Measurement Systems, 26(2), 419-429. https://doi.org/10.24425/mms.2019.128358
[25] Pau, M., Pegoraro, P., Monti, A., Muscas, C., Ponci F. & Sulis S. (2019). Impact of current and power measurements on distribution system state estimation uncertainty. IEEE Transactions on Instrumentation and Measurement, 68(6), 1705-1713. https://doi.org/10.1109/TIM.2018.2883844
[26] He, Y., Guan, Z. & Wang, J. (2024). Virtual sensing techniques for nonstationary processes based on a multirate probabilistic Dual-Latent-Variable supervised slow feature analysis. IEEE Transactions on Industrial Informatics, 20(3), 4884-4893. https://doi.org/10.1109/TII.2023.3329679
