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

Motaz M Ayiad,Helder Leite,Hugo Martins

doi:10.1109/access.2021.3092308

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.

Highlights

M ODERN power networks are becoming more complex due to the penetration of large-scale low-carbon generation units such as solar and wind farms
The integration of Largest Normalized Residual (LNR) in Weighted Least Squares (WLS) state estimation algorithm is shown in Fig. 5, and its processes can be described as follow: 1) Check if bad data exists in the measurements set; 2) Compute the LNR for all measurements based on the below equation: ri N LR
Further modification can be made on the conventional GMM to merge the mixtures and reduce the clusters number, this approach known as Reduced Gaussian Mixture Model (RGMM)

Summary

INTRODUCTION

M ODERN power networks are becoming more complex due to the penetration of large-scale low-carbon generation units such as solar and wind farms. Motaz et al.: State Estimation for Hybrid VSC Based HVDC/AC: Unified Bad Data Detection integrated with Gaussian Mixture Model grids becomes more essential and critical [1], [3], [5]. The work in [30] proposes a mixture Gaussian distribution learning method to detect the false data injection attacks on smart grids state estimations. Regardless of the variety of state estimation and bad data detection algorithms, the WLS method is still commonly used in the SCADA of transmission networks. The work in this paper focuses on keeping the WLS algorithm intact while improving its robustness by integrating the GMM with the LNR bad data detection block.

STATE ESTIMATION AND BAD DATA DETECTION

UNIFIED BAD DATA DETECTION

THE EXPECTATION-MAXIMIZATION ALGORITHM

STATE ESTIMATION RESULTS AND ACCURACY

Findings

CONCLUSION