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Instrument Transformers
Published in Leonard L. Grigsby, Electric Power Transformer Engineering, 2017
The burden of the instrument transformer is considered to be everything connected externally to its terminals, such as monitoring devices, relays, and pilot wiring. The impedance values of each component, which can be obtained from manufacturer data sheets, should be added algebraically to determine the total load. The units of measurement must be the same and preferably in the rectangular form R + jX. Table 7.2 shows typical ranges of burdens for various devices used.
Fault Analysis and Protection Systems
Published in Antonio Gómez-Expósito, Antonio J. Conejo, Claudio Cañizares, Electric Energy Systems, 2017
José Cidrás, José F. Miñambres, Fernando L. Alvarado
The data provided by the instrument transformers is affected by a specific error. The accuracy class is a characteristic data of each instrument transformer that refers to the maximum error that may incorporate the information provided by the transformer when it functions within the conditions for which it is defined. The lower the value of the accuracy class, the lower the maximum error, and the greater the accuracy of the data obtained using the transformer.
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Published in Leonard L. Grigsby, and Distribution: The Electric Power Engineering Handbook, 2018
Accuracy classification—Accuracy classification is the accuracy of an instrument transformer at specified burdens. The number used to indicate accuracy is the maximum allowable error of the transformer for specified burdens. For example, 0.3 accuracy class means the maximum error will not exceed 0.3% at stated burdens.
PMU-based Fault Location Technique for Three-terminal Parallel Transmission Lines with Series Compensation
Published in Electric Power Components and Systems, 2020
The studied system is a 500-kV, 50-Hz, three-terminal series-compensated untransposed double-circuit line as displayed in Figure 1. The lengths of line sections S-J, R-J, and T-J are respectively LSJ= 200 km, LRJ = 300 km, and LTJ = 500 km, where J is the tee point. Three PMUs are placed at the ends S, R, and T so that the synchronized data at these buses are directly measured. The line parameters of line sections, generator data, and instrument transformer data are obtained from [12], and all data are shown in Appendix. The three-terminal line is with 60% compensation of section T-J located at 60% of line length measured from bus T. Loads of 200 MVA are installed at terminals S, R, and T. The simulation data of the series compensation are given in [31]. In addition, the detailed simulation of PMU is given in [31]. One cycle post-fault of voltage and current data is filtered by low-pass 2nd Butterworth filter with cutoff frequency 400 Hz. After that, the data are sampled at 2.5 kHz and a digital mimic filter is employed to eliminate the DC component. The 50 Hz voltage and current components are calculated employing full-cycle discrete Fourier transform, and all synchronized data are sent to the protection center via communication links.
Wide area monitoring for energy system: a review
Published in International Journal of Ambient Energy, 2019
A. Nageswara Rao, P. Vijaya Priya, M. Kowsalya, R. Gnanadass
State estimation, seams between state estimation, instrument transformer calibration for all PMU estimators, voltage stability index monitoring and prediction, ambient and transient power oscillation monitoring, line thermal monitoring, Power damping monitoring, and phase angle monitoring and wide area frequency monitoring.
Electromagnetic field and artificial intelligence based fault detection and classification system for the transmission lines in smart grid
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
Chetan Khadse, Abhijeet A. Patharkar, Bharat S. Chaudhari
The transmission line plays a crucial role in the electrical power system. It is essential to detect the faults and isolate the fault that occurs in the transmission line. Early detection of faults in the transmission line leads to an increase in reliability and reduction in service and maintenance cost (Hewitson, Brown, and Balakrishnan 2005). The monitoring of the current and voltage plays a significant role in detection of a fault in the transmission line. The accurate detection of faults depend upon precise monitoring. The current and voltage, both signals are required for the accuracy in most of the fault identification techniques. The high impedance faults in power distribution system are diagnosed in Ghaderi, Ginn, and Mohammadpour (2017) with the help of current and voltage signals. The power line communication technique-based impedance fault is diagnosed in Milioudis, Andreou, and Labridis (2012a). The similar approach is found in Milioudis, Andreou, and Labridis (2012b). The phasor measurement unit-based impedance fault detection is done in Kargar and Zanjani (2012) which also uses current and voltage signal. The distance protection along with localization using current and voltage signal is proposed in Lee et al. (2006). Nowadays, current transformer (CT) and potential transformer are used for the acquisition of current and voltage signals at the electrical substation. For this purpose, minimum three current transformers are required at each end of the transmission line. It is not economical when it comes to high-voltage lines. The performance of CT may be limited during fault transient due to its core saturation. There is also a physical contact required between instrument transformer and high-voltage line in conventional system. It leads to the adherence of strict safety rules. To overcome such issues and to enhance the reliability and accuracy in the fault detection, magnetic field sensors are one of the promising solutions. The magnetic sensors transform the current in transmission line into horizontal and vertical magnetic fields. Instead of current, these magnetic fields can be used for analysis of the faults in the transmission lines. The different fault types give rise to different pattern of waveforms. If the patterns are recognized with artificial intelligence technique, the fault types can be detected and classified.