A Comparative Analysis of Artificial Intelligence and Machine learning Approach to Estimate Currents in Electrical Power Transmission Lines
Keywords:
machine learning, power, high voltageAbstract
Using pyro-sensors, machine learning (ML) methods, and artificial intelligence, this suggested study enlightens an idea to monitor current in high voltage transmission lines. Data can be collected in the form of heat waves (infrared waves) created by the electric current in the transmission/distribution line using pyro-sensors installed around the transmission/distribution lines. The suggested approach processes this data using a neural network-based artificial intelligence algorithm to determine the transmission line's current. MATLAB simulation neural network toolkit is used to test and validate the suggested technique's validity with backward forwarding propagation (FFBP) type and with feed-forward distributed time delay (FFDTD) and compare it to get the best validation performance for the proposed approach. It is validated that feed-forward distributed time delay (FFDTD) gives the best validation performance (0.98256) at epoch 0 as compared to the forward, backward propagation (FFBP), which offers the best validation performance at (1.984), it means validation performance of FFDTD is better than FFBP. It also tells us that these simulation findings compare projected current to actual current, implying that the existing CT current measuring technology at the grid station may be replaced.
References
Y. Wang, Z. Yang, H. Zhou, W. Cao, X. Yin, and Q. Guo. Study on transformer inrush current and CT saturation as well as the impact of their coupling effect on protection, IEEE Transportation Electrification Conference Expo, Asia-Pacific, 1, 2017.
M. Kellermeier, C. Bert, and R. G. Müller. A novel concept for CT with fixed anodes (FACT): Medical imaging based on the feasibility of thermal load capacity, Physics Medica, vol. 31(5), pp. 425–434, 2015.
J. Li and Y. Zhao. Co-simulation Research Based on Electromagnetic Induction of Wireless Power Transfer, 7th Int. Conf. Mechatronics, Computation, Education, Informationization (MCEI 2017), Vol. 75, pp. 577–580, 2018.
J. Lorincz, I. Bule, and M. Kapov. Performance analyses of renewable and fuel power supply systems for different base station sites, Energies, Vol. 7(12), pp. 7816–7846, 2014..
M. Mansoor, I. Haneef, S. Akhtar, A. De Luca, and F. Udrea. Silicon diode temperature sensors - A review of applications, Sensors Actuators, A Physics, vol. 232, pp. 63–74, 2015.
P. R. N. Childs, J. R. Greenwood, and C. A. Long. Review of temperature measurement, Review of Scientifc. Instrument, Vol. 71(8), pp. 2959–2978, 2000.
D. M. Nierman. Core temperature measurement in the intensive care unit, Critical Care Medicine, Vol. 19(6), pp. 818–823, 1991.
A. Milewski, K. L. Ferguson, and T. E. Terndrup. Comparison of pulmonary artery, rectal, and tympanic membrane temperatures in adult intensive care unit patients, Clinicalrics Pediatr, Vol. 30(4), pp. 13–17, 1991.
E. B. Soyer. Pyroelectric Infrared Sensor Based Event Detection, Engineering, pp. 1–84, 2009.
J. R. Howell and M. P. Mengüç. Challenges for radiative transfer 1: Towards the effective solution of conjugate heat transfer problems, Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 221, pp. 253–259, 2018.
I. N. T. Lines. IEEE Transactions on Power Delivery, Vol. 3(1), pp. 288–297, 1988.
J. M. Carrasco et al. Power-electronic systems for the grid integration of renewable energy sources: A survey, IEEE Transanctions on Industrial Electronics, Vol. 53(4), pp. 1002–1016, 2006,
D. Jovcic, N. Pahalawaththa, and M. Zavahir. Analytical modelling of HVDC-HVAC systems, IEEE Transanctions on. Power Delivery, Vol. 14(2), pp. 506–511, 1999..
M. Wang, A. J. Vandermaar, and K. D. Srivastava. Improved detection of power transformer winding movement by extending the FRA high frequency range, IEEE Transanctions on Power Delivery, Vol. 20(3), pp. 1930–1938, 2005.
D. S. Pierce. A high precision non-contact gauge for distance measurement A high precision non-contact guage for distance measurement, 2016.
T. Haiguo, Z. Jiran, G. Fangliang, L. Hua, F. Min, and H. Qi. Research on a rail-robot based remote three-dimensional inspection system for switch stations in power distribution network, Chinese Automation Congrress, Vol. 2017, pp. 7925–7930, 2017.
K. Zhu, W. K. Lee, and P. W. T. Pong. Non-contact capacitive-coupling-based and magnetic-field-Sensing-assisted technique for monitoring voltage of overhead power transmission lines, IEEE Sensors Journals, Vol. 17(4), pp. 1069–1083, 2017.
Z. Yanchao, J. Dong, K. Deshan, and C. Lin. Remote monitoring of power quality of high-voltage transmission lines, IEEE International Conference on Electronics Measurement Instruments, pp. 984–991, 2019.
J. Meseguer, I. Pérez-Grande, and A. Sanz-Andrés. Thermal radiation heat transfer, Space Thermal Control, Vol. 2, pp. 73–86, 2012.
P. Castro, R. Lecuna, M. Manana, M. J. Martin, and D. Del Campo. Infrared temperature measurement sensors of overhead power conductors, Sensors, Vol. 20(24), pp. 1–13, 2020.
I. H. Choi and K. D. Wise. Silicon-thermopile-based infrared sensing array for use in automated manufacturing, IEEE Transactions Electronic Devices, vol. 33(1), pp. 72–79, 1986.
R. Lenggenhager, H. Baltes, and T. Elbel. Thermoelectric infrared sensors in CMOS technology, Sensors Actuators A: Physics, Vol. 37–38, pp. 216-220, 1993.
F. Völklein, A. Wiegand, and V. Baier. High-sensitivity radiation thermopiles made of BiSbTe films, Sensors Actuators A: Physics, Vol. 29(2), pp. 87–91, 1991.
A. Dehé, K. Fricke, and H. L. Hartnagel. Infrared thermopile sensor based on AlGaAs—GasAs micromachining, Sensors and Actuators, A: Physical, Vol. 47(1-3), pp. 432–436, 1995.
J. A. Young, S. Borror, A. W. Obst, J. R. Payton, and A. Seifter. Evaluation and comparison of three IR detectors and three amplifier designs for a new high-speed IR pyrometer, Ultrafast X-Ray Detectectors High-Speed Imaging and Applications, Vol. 5920, p. 592011, 2005.
L. Chen, K. Sun, D. V. Shalashilin, M. F. Gelin, and Y. Zhao. Efficient simulation of time- And frequency-resolved four-wave-mixing signals with a multiconfigurational Ehrenfest approach, Journal of Chemical Physics, Vol. 154(5), 2021.
S. A. Haider, S. R. Naqvi, T. Akram, and M. Kamran, Prediction of critical currents for a diluted square lattice using artificial neural networks, Applied Sciience, Vol. 7(3), 2017.
A. Teymourifar. A comparison between two approaches to optimize weights of connections in artificial neural networks, Universse Journal of Applied Mathematics, Vol. 9(2), pp. 17–24, 2021.
