PROJECT TITLE: Wide Area Monitoring Using Synchrophasor-Based State Estimation
PhD period: 2015.06.01 – 2018.05.31.
Section: Electric Power Systems
Research Programme: Modern Power Transmission Systems
Supervisor: Filipe Miguel Faria da Silva
Co-Supervisor: Claus Leth Bak
Funding: Dept. of Energy Technology and AAU.
State estimation is a critical part of wide-area monitoring systems WAMS, which allows operators to accurately monitor and control the power system network. Bad data detection and analysis plays a crucial role in ensuring that the state estimation tool correctly estimates the state of the power system under various operating conditions including those with large measurement errors. This study brings forward research on state estimation in both static and dynamic systems. Several methods in static and dynamic state estimation developed to enhance the performance of proposed systems. This research focuses on detection and correction of bad data in static state estimation of the power system. This PhD work presents a post-processing method for real-time power system monitoring for static state estimations (PPSSE) using synchrophasor measurements provided by PMUs for detection and analyses of multiple bad data. The state estimation issue is untangled by using a modified-weighted least squares (M-WLS) technique comprising of SCADA (supervisory control and data acquisition) measurements and the Synchrophasor-based post-processing step. In order to ensure a secure power system operation, a better link between the current stand-alone WAM systems (PMUs & PDCs) and the standard control room SCADA/EMS (and their Human Machine Interfaces (HMIs)) is required for improving power system observability and power system control. Today, worldwide power systems are undergoing dramatic changes due to a shift from synchronous connected fossil-based electric power generation to power electronic connected renewable energy sources. Changes of responsibilities of the different power system operation and market participants also result in challenges for power system stability monitoring and control. In this area an algorithm introduced for the detection and analysis of multiple bad data in critical measurements (CMs). The synchrophasor measurements obtained during the simulations carried out using the real-time digital simulator (RTDS) are afterward used as the state measurements to the PPSSE method. Results obtained demonstrate the ability of the PPSSE method to detect and reduce the effect of multiple bad data due to loss of PMU/PDC connection, malicious bad data injection and noise.
Publications in journals and conference papers may be found at VBN.