Power systems research group
Research classification (Frascati)
Head of the research group
Research group member
power system stability
wind and solar power connections
HVDC and FACTS
Research activities in the group are focused onthe development of control and protection algorithms and applications, and performing systemanalysis considering the challenges in modernand future power systems.Key research areas: power system real-time control protection and analysis based on wide-areainformation with respect to HVDC and FACTScontrol, wind power integration, power qualityand load modelling. Emphasis is on modern power systems where the level of generation throughconverters is increasing and consequently thelevel of system inertia is decreasing.Other research activities are concentrated on thedevelopment and assessment of power qualitymitigation methods in transmission and distribution systems considering the availability ofmodern compensation devices and wide-areainformation.
Development of- new methodology and control algorithms for enabling reliable and optimal cooperation between electricalnetwork and industrial (generation)facility with respect to system services.- new system protection algorithm usingwide-area measurements with scopeof enabling coordinated power systemprotection in case of out-of-step situation- power quality monitoring methodologyin transmission networks using widearea measurements- mathematical algorithms for conversionof network busload models between different network calculation tools Analysis and development of- methodology for the coordinated cooperation of HVDC and synchronousunits in low inertia systems- methodology to determine the challenges and possible solutions withsignificant increase of wind and solarpower plants in view of Estonian powersystem Advancement of network utility assetmanagement and comparison methodology and development of mathematicalapproach for determining the actual condition of electrical network overhead lines.
Teadusgrupiga seotud publikatsioonid
- Manninen, H., Kilter, J., Landsberg, M. Health index prediction of overhead transmission lines : a machine learning approach // IEEE transactions on power delivery (2022) vol. 37, 1, p. 50-58.
- Manninen, H., Kilter, J., Landsberg, M. A holistic risk-based maintenance methodology for transmission overhead lines using tower specific health indices and value of loss load // International journal of electrical power and energy systems (2022) vol. 137, art. 107767 ; 11 p. : ill.
- Tealane, M., Kilter, J., Bagleybter, O., Heimisson, B., Popov, M. Out-of-step protection based on discrete angle derivatives // IEEE Access (2022) vol. 10, p. 78290-78305.
- Manninen, H., Ramlal, C. J., Singh, A., Rocke, S., Kilter, J., Landsberg, M. Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques // International journal of electrical power & energy systems (2021) vol. 128, art. 106726.
- Trummal, T., Sarnet, T., Kilter, J. Modelling of distribution level coreless induction furnace for rapid voltage change assessment // Electric power systems research (2021) vol. 195, art. 107151.