As the integration of High Voltage Direct Current (HVDC) systems on modern power networks continues to expand, challenges have appeared in different fields of the network architecture. In the Supervisory, Control and Data Acquisition (SCADA) field, software and toolboxes are expected to be modified to meet the new network characteristics. Therefore, this paper presents a unified Weighted Least Squares (WLS) state estimation algorithm suitable for hybrid HVDC/AC transmission systems, based on Voltage Source Converter (VSC). The mathematical formulas of the unified approach are derived for modelling the AC, DC and converter coupling components. The method couples the AC and DC sides of the converter through power and voltage constraints and measurement functions. Two hybrid power system test cases have been studied to validate this work, a 4-AC/4-DC/4-AC network and Cigre B4 DC test case network. Furthermore, comparison between the fully decentralized state estimation and the unified method is provided, which indicated an accuracy improvement and error reduction.

The growing integration of rooftop photovoltaics (PVs) and energy storage units (ESUs) in customer households has resulted in changes in the customer load profiles. This is likely to influence the accuracy of state estimation (SE) carried out based on previously assumed load profiles. In this paper, a statistical model for modern low voltage (LV) customers was developed using Gaussian mixture model (GMM). The resulting model was subsequently applied to SE using weighted least squares (WLS) algorithm. LV network with high penetration of customer-owned PV and ESUs have been simulated. Different scenarios which include load profiles: with PVs integrated but without ESUs, ESUs alone, and with hybrid systems (combination of PVs and ESUs) have been considered. The results are presented and discussed.

The High Voltage Direct Current (HVDC) is an emerging technology for transmitting power over long distances with a higher capacity than the traditional AC systems. The integration of the HVDC systems has demanded changes on the Supervisory, Control and Data Acquisition (SCADA) systems. Several power system applications and toolboxes in the SCADA have to be modified to meet the modern power network characteristics. One of the essential toolboxes is the state estimator, which estimates the network AC and DC systems states. On several occasions, the state estimator fails to deal with severely corrupted data, known as bad data. Therefore, an additional data treatment is required. This paper presents a unified bad data detection block for Weighted Least Squares (WLS) state estimation algorithm suitable for hybrid Voltage Source Converter (VSC)-HVDC/AC transmission systems. The bad data detection block improves the traditional Largest Normalized Residual (LNR) method by integrating the Gaussian Mixture Model (GMM) algorithm. The modifications aim to reduce the time performance of the bad data detection, increase the algorithm robustness, and enhance the state estimation accuracy. The Cigre B4 network is used as a test case to validate this work on a hybrid VSC-HVDC/AC network. UK national grid load profile data is used to construct the simulation measurements set and the GMM model. The work has concluded that the modified GMM-LNR has considerably reduced the bad data detection time and improved the WLS state estimation accuracy.