An Algorithm for Circuit Parameter Identification in Lightning Impulse Voltage Generation for Low-Inductance Loads

Abstract

This paper presents an effective technique based on an artificial neural network algorithm utilized for circuit parameter identification in lightning impulse generation for low inductance loads such as low voltage windings of a power transformer, a large distribution transformer and an air core reactor. The limitation of the combination between Glaninger’s circuit and the circuit parameter selection from Feser’s suggestions in term of producing an impulse waveform to be compliant with standard requirements when working with a low inductance load is discussed. In Feser’s approach, the circuit parameters of the generation circuit need to be further adjusted to obtain the waveform compliant with the standard requirement. In this process, trial and error approaches based on test engineers’ experience are employed in the circuit parameter selection. To avoid the unintentional damage from electrical field stress during the voltage waveform adjustment process, circuit simulators, such as Pspice and EMTP/ATP, are very useful to examine the generated voltage waveform before the experiments on the test object are carried out. In this paper, a system parameter identification based on an artificial neural network algorithm is applied to determine the appropriate circuit parameters in the test circuit. This impulse voltage generation with the selected circuit parameters was verified by simulations and an experiment. It was found that the generation circuit gives satisfactory impulse voltage waveforms in accordance with the standard requirement for the maximum charging capacitance of 10 µF and the load inductance from 400 µH to 4 mH. From the simulation and experimental results of all cases, the approach proposed in this paper is useful for test engineers in selection of appropriate circuit components for impulse voltage tests with low inductance loads instead of employing conventional trial and error in circuit component selection.

Systematic Design and Circuit Analysis of Lightning Impulse Voltage Generation on Low-Inductance Loads

“…As shown in Figure 4, the waveform parameters, i.e., T 1 of 1.07 µs, T 2 of 40.4 µs, the overshoot rate of +0.17%, and the undershoot rate of +38.4%, accord with the standard requirement. The complex approach based on neural networks for waveform adjustment was proposed in [10], but physical meaning is lost in the approach. In this paper, based on circuit analysis, the effectiveness of the simple approach is proposed.…”

“…The proper equivalent circuit should be an inductor and a capacitor in parallel connection as shown in Figure 1b, otherwise, it causes the conventional circuit generating the distort the LIV waveform from the standard requirement [4][5][6][7][8][9]. There are some studies [10][11][12] utilizing rigorous approaches for adjusting the waveform parameters according to the standard requirements. A crucial problem is that it is very hard to adjust the circuit components to obtain T 2 longer than 40 µs, even though a charging capacitance (C s ) of over 10 µF, and a very high tail-time resistance (R e ) of over 1 MΩ, are employed in the generation circuit.…”

Effective Simulation Approach for Lightning Impulse Voltage Tests of Reactor and Transformer Windings

“…the charging capacitance (C s ), the front-time resistance (R d ), the additional inductance (L d ), and the additional parallel resistance (R p ), as given in Equations ( 3) to (7). In addition, the appropriate tail-time resistor (R e ) has to be selected to obtain the undershoot voltage less than 50% of the peak voltage [7][8][9][10][11].…”

“…Energies 2020, 13, x FOR PEER REVIEW 3 of 19 capacitance (Cs), the front-time resistance (Rd), the additional inductance (Ld), and the additional parallel resistance (Rp), as given in Equations ( 3) to (7). In addition, the appropriate tail-time resistor (Re) has to be selected to obtain the undershoot voltage less than 50% of the peak voltage [7][8][9][10][11]. However, the distortion in the waveform generated by the circuit with parameters from K. Feser's suggestion was noted.…”

“…A trial and error approach, then, is employed to adjust the circuit parameters to mitigate the waveform distortion and to obtain the waveform parameters, according to the standard requirement. An alternative method was proposed based on a neural network for the selection of appropriate Glaninger's circuit parameters [11].…”