Estimation of rotor speed of synchronous generators for small-signal stability assessment in smart grids

• Grant number: 20/06333-5
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2020
• End date: August 31, 2021
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Tatiane Cristina da Costa Fernandes
• Grantee: Lucas de Paula Ferreira Dias Cardoso
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

In the planning and operating of a power system, accurate information about the dynamic behavior of this system is essential for its effective control and reliable operation. For example, the monitoring of dynamic oscillatory behaviors resulting from electromechanical interactions provides evidence of a possible overloading of the network and its stability. In the context of smart grids, to know the dynamic behavior of different sources and load, since this monitoring provides information to ensuring stability in normal or atypical operating conditions and allows energy resources to be properly managed. Specifically, in small-signal stability analysis, an accurate measurement of synchronous machine dynamic states, such as its rotor speed, is very important to mitigate the detrimental effects of poorly damped electromechanical oscillations in electric power systems. However, the direct measurement of synchronous generator speed is a difficult task, since the procedure requires the installation of moving parts around the rotor. To overcome this issue, this work proposes an approach to estimate the rotor speed of synchronous generators connected into smart grids. The proposed method will use only signals that can be easily sampled by measuring equipment, such as current and voltage transformers at the high voltage side of the step-up transformer of the power plant. The estimation of generator speed from the measured signals is performed using a Kalman filter. (AU)