Orthogonal Components Forming of the Microprocessor-Based Protection Input Signals

Abstract

The use of orthogonal components (OS) is the main direction of determining information parameters in microprocessor relay protection and automation of electric power systems. Most of the measuring devices used in modern protection and automation devices can be implemented using known operating systems. Digital non-recursive frequency filters based on discrete Fourier transform are used for OS selection. The main disadvantage of these filters is their low performance that exceeds the period of industrial frequency. For the construction of high-speed measuring devices, this time of establishing the true output signal is often unacceptable. The article proposes to form the equivalent signal OS in microprocessor defenses based on the values of the cosine and sine axes of the main harmonic formed using a discrete Fourier transform, by multiplying them by a correction factor, which is a function of the values of the input signal amplitude and its main harmonic. The proposed algorithm for generating OS input signals in microprocessor defenses is characterized by high performance in transient modes and has wide functionality. A block diagram of an OS equivalent signal generator has been developed, all blocks of which can be implemented according to known schemes on a microelectronic and microprocessor element base. The OS shaper model is implemented in the MatLab-Simulink dynamic modeling environment. The model functioning was checked using two types of test actions, viz. a sinusoidal signal with a frequency of 50 Hz (idealized action) and a signal close to the real secondary current of a short-circuit current transformer. As a result of the performed calculations, a significant (up to two times) in the speed of the proposed method of OS formation in comparison with the formers based on the discrete Fourier transform, frequency properties of both formers being identical.

Analysis of the Effect of Emergency Ventilators on the Treatment of Critical Illness Based on Smart Medical Big Data

“…The one-dimensional periodic B-spline breathing motion model makes the anatomical structure motion follow the circular trajectory [ 10 ], which also means that it can simulate hysteresis but cannot simulate the error between cycles. The two-dimensional B-spline respiratory motion model can simulate the error and hysteresis between cycles, but because there are more model parameters that need to be fitted, there is a risk of data overfitting.…”

“…e one-dimensional periodic B-spline breathing motion model makes the anatomical structure motion follow the circular trajectory [10], which also means that it can simulate hysteresis but cannot simulate the error between cycles.…”