Affiliation:
1. Department of Electric Power and Electromechanics, Saint Petersburg Mining University, 199106 St. Petersburg, Russia
2. Department of General Electrical Engineering, Saint Petersburg Mining University, 199106 St. Petersburg, Russia
Abstract
This paper discusses the issues of assessing the influence of external distortion sources on the functioning of a shunt passive harmonic filter. In this study, we evaluated the overload of a passive harmonic filter based on determining the contributions of distortion sources. A method was proposed for assessing the contributions of distortion sources, which allowed us, regardless of background distortions, to determine the contributions of consumer loads, as well as the contribution of background distortions. The simulation was carried out using the Simulink MatLab software (version R2023a). Several scenarios were considered in which the following values were varied: supply feeder impedance, level of background distortions, consumer electrical load composition, and passive filter parameters. It was found that the contribution of the background distortion source decreases with increasing impedance of the supply grid. It was determined that the consumer load contribution is independent of background voltage harmonics and can be used to estimate the overload of a passive harmonic filter. It was shown that it is necessary to take into account the overload of the passive filter by currents from background distortion sources, which did not exceed 135% of the rated rms current for the conditions under consideration. A mathematical model was proposed to estimate the overload of a passive filter in the presence of background voltage distortions. This model was obtained during analytical studies and allows one to evaluate the overload of a passive filter, taking into account the short circuit ratio, detuning frequency and power of the passive filter, and the share contribution of background distortion sources.
Funder
Russian Science Foundation
Reference39 articles.
1. Bagheri, A., de Oliveira, R.A., Bollen, M.H.J., and Gu, I.Y.H. (2022). A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances. Energies, 15.
2. Kanálik, M., Margitová, A., Beňa, Ľ., and Kanáliková, A. (2020). Power System Impedance Estimation Using a Fast Voltage and Current Changes Measurements. Energies, 14.
3. Improving the efficiency of autonomous electrical complexes of oil and gas enterprises;Bogdanov;J. Min. Inst.,2021
4. Morenov, V., Leusheva, E., Lavrik, A., Lavrik, A., and Buslaev, G. (2022). Gas-Fueled Binary Energy System with Low-Boiling Working Fluid for Enhanced Power Generation. Energies, 15.
5. Study of the Kinetics of the Process of Producing Pellets from Red Mud in a Hydrogen Flow;Khalifa;J. Min. Inst.,2022