State Estimation for Hybrid VSC Based HVDC/AC: Unified Bad Data Detection Integrated with Gaussian Mixture Model

Abstract

The High Voltage Direct Current (HVDC) is an emerging technology that transmits power over long distances and at a higher capacity than the traditional AC systems. Integration of HVDC into modern power networks requires vital modification to the Supervisory, Control and Data Acquisition (SCADA) system, particularly in power system applications. For instance, the state estimator toolbox is an essential software to estimate the network AC and DC systems states. However, the state estimator may fail due to severely corrupted data, a.k.a bad data; hence, an additional data treatment is needed. This paper presents a unified bad data detection block for Weighted Least Squares (WLS) state estimation algorithm. The bad data detection block works for hybrid Voltage Source Converter (VSC)-HVDC/AC transmission systems. It improves the traditional Largest Normalized Residual (LNR) method by integrating the Gaussian Mixture Model (GMM) algorithm. This method reduces the time needed for bad data detection, increases the algorithm robustness, and enhances estimation accuracy. The Cigre B4 network is used as a test case to validate this work on a hybrid VSC-HVDC/AC network. Also, grid load and generation data from the UK is used to construct the simulation measurements and the GMM model. Simulation results show that the modified GMM-LNR has considerably reduced the detection time and improved the WLS accuracy.