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An Improved Method of Busbar Voltage Reconstruction from Signals of Electric Field Sensors Installed in an Indoor MV Substation

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Języki publikacji
EN
Abstrakty
EN
This paper presents an improved method for the reconstruction of busbar voltage waveforms from signals acquired by a system of electric field (EF) sensors located in an indoor medium voltage substation. In the previous work [8], the authors proposed the use of black-box models in the form of artificial neural networks (ANNs) for this task. In this paper it is shown that a parametric model of the system of EF sensors can reconstruct voltages with much lower errors, provided that it is accurately identified. The model identification is done by minimization of a nonlinear goal function, i.e. mean squared error (MSE) of voltage reconstruction. As a result of examining several optimization techniques, the method based on simulated annealing extended with a simplex search, is proposed. The performance of the model identified with this method is at least 8 times better in terms of MSE and at least 12 times better in terms of frequency domain errors than the best one of concurrent ANNs.
Rocznik
Strony
71--86
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr., wzory
Twórcy
autor
  • AGH - University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Al. A. Mickiewicza 30, 30-059 Cracow, Poland
Bibliografia
  • [1] Borkowski, D. (2015). A new method for noninvasive measurement of grid harmonic impedance with data selection. Int. Trans. Electr. Energ. Systems. 25(12), 3772-3791.
  • [2] Locci, N., Muscas, C., Sulis, S. (2009). Experimental comparison of MV voltage transducers for power quality applications. IEEE Instrumentation and Measurement Technology Conference, 2009. I2MTC ’09. 92-97.
  • [3] Bobowski, J.S., Ferdous, M.S., Johnson T. (2015) Calibrated single contact voltage sensor for high-voltage monitoring applications. IEEE Trans. Instrum. and Meas., 64(4).
  • [4] Tsang, K., Chan, W. (2011). Dual capacitive sensors for non-contact{AC}voltage measurement. Sensors and Actuators A: Physical, 167(2), Solid-State Sensors, Actuators and Microsystems Workshop, 261-266.
  • [5] Chen, K.L., Xu, W. (2016). Voltage distortion measurement using a contactless sensor. 2016 IEEE Power and Energy Society General Meeting (PESGM).
  • [6] Brunelli, D., Villani, C., Balsamo, D., Benini, L. (2016). Non-invasive voltage measurement in a three-phase autonomous meter. Microsystem Technologies, 1-12.
  • [7] Wu, L., Wouters, P., van Heesch, E., Steennis, E. (J 2011). On-site voltage measurement with capacitive sensors on high voltage systems. PowerTech 2011 IEEE Trondheim, 1-6.
  • [8] Borkowski, D., Wetula, A., Bien, A. (2015). Contactless measurement of substation busbars voltages and waveforms reconstruction using electric field sensors and artificial neural network. IEEE Trans. Smart Grid. 6(3), 1560-1569.
  • [9] Morawski, R. (1991). Unified approach to measurement signal reconstruction. Measurement. 9(3), 140-144.
  • [10] Jakubiec, J., Makowski, P., Roj, J. (2009). Error model application in neural reconstruction of nonlinear sensor input signal. IEEE Trans. Instrum. Meas., 58(3), 649-656.
  • [11] Faifer, M., Ottoboni, R., Prioli, M., Toscani, S. (2016 ). Simplified modelling and identification of nonlinear systems under quasi-sinusoidal conditions. IEEE Trans. Instrum. Meas., 65(6).
  • [12] Adeniran, A.A., Ferik, S.E. (2016) Modeling and identification of nonlinear systems: A review of the multimodel approach - part 1. IEEE Trans. Systems, Man, and Cybernetics: Systems, PP(99).
  • [13] Nelles, O. (2001). Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Berlin: Springer-Verlag.
  • [14] Li, D.H., Fukushima, M. (1999). On the global convergence of BFGS method for nonconvex unconstrained optimization problems. SIAM Journal on Optimization, 11(4), 1154-1164.
  • [15] Wright, H.M., (September 2004). The interior-point revolution in optimization: History, recent developments, and lasting consequences. Bulletin of the American Mathematical Society, 42(1), 39-56.
  • [16] Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E. (1998). Convergence properties of the neldermead simplex method in low dimensions. SIAM Journal of Optimization, 9(1), 112-147.
  • [17] Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. (1983). Optimization by simulated annealing. SCIENCE, 220(4598), 671-680.
  • [18] Ingber, L. (1993). Simulated annealing: Practice versus theory. Mathematical and Computer Modelling. 18(11) 29-57.
  • [19] Neto, A.S., Junior, J.L., Soeiro, F.J.C.P., Neto, L.B., Santana, C.C., Lobato, F.S., Valder, Junior, S. (2010). Application of simulated annealing and hybrid methods in the solution of inverse heat and mass transfer problems. Simulated Annealing, Theory with Applications.
  • [20] Borkowski, D., Wetula, A., Kowalski, J. (2015). Uncertainty estimation in noninvasive measurement of harmonic impedance - laboratory studies. 2015 International School on Nonsinusoidal Currents and Compensation (ISNCC).
  • [21] Borkowski, D., Barczentewicz, S. (2014). Power grid impedance tracking with uncertainty estimation using two stage weighted least squares. Metrol. Meas. Syst., 21(1), 99-110.
  • [22] Hartman, M., Hashad, M., Mindykowski, J., Hanzelka, Z., Klempka, R. (2005). A new concept of the electrical distributed system for power quality improvement. CPE’05 Conference Proceedings, Gdańsk.
  • [23] Borkowski, D., Wetula, A., Bien, A. (2012). Design, optimization, and deployment of a waterworks pumping station control system, ISA Transactions. 51(4), 539-549.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8bb90665-1cad-45b1-b160-a68ec2982571
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