The increased integration of renewable energy sources and power electronic converters has transformed conventional power systems into hybrid AC-DC systems, introducing faster and more complex fault dynamics that challenge existing protection schemes. While data-driven approaches have demonstrated high accuracy in fault detection and classification, their lack of prediction validation limits their reliability in critical protection systems. This thesis proposes a real-time data-driven framework for event classification and validation for Hybrid AC-DC systems. A comprehensive dataset of AC and DC faults was generated from an RTDS based grid model, with transient measurements acquired using a Python-based communication interface and processed to extract time domain and dynamic fault features. Independent machine learning classifiers were developed for the AC and DC subsystems. An additional waveform signature validation layer that uses representative transient signatures of event classes and similarity analysis was introduced to verify classifier predictions. Performance evaluation demonstrated that the proposed framework achieves accurate and robust event classification under varying operating conditions and moderate signal degradation, while providing additional confidence assessment for machine learning-based classifier predictions. The proposed approach offers a practical and reliable solution for real-time monitoring, event identification, and supervisory protection in future converter-dominated power systems. ... Read more

Overhead power lines play a key role in the transmission and distribution of electrical power. Traditionally, the operating limits of these overhead lines are fixed and determined through static line rating. These limits are designed under conservative conditions such as high loads and extreme weather scenarios. However, the global energy demand is expected to increase substantially in a few decades. Furthermore, there will be higher levels of penetration of intermittent renewable generation in the power system due to energy transition in the future. In this thesis, a Dynamic Line Rating (DLR) method is developed to enable the determination of the varying operating limits of the overhead lines. It is realized by calculating the maximum allowable current for the line considering the real-time monitored electrical and weather quantities while meeting the requirements for the design and security criteria of the line. This is a more economical and feasible solution to improve the existing power system compared to constructing additional infrastructure. This thesis aims to develop a real-time dynamic line monitoring system by utilizing Phasor Measurement Unit (PMU) data for a 50kV distribution grid. The Real-Time Digital Simulator(RTDS) is used with an interface to MATLAB to realize the DLR model at computational speeds equivalent to real-time operations. A parametric sensitivity analysis is conducted to understand the influence of each parameter in the DLR model and its working. Case studies are conducted by using the DLR model on the 50kVdistribution grid, which showed an increase of 20% on the line ampacity. Lastly, a comparative analysis of the DLR model and the P341 MiCOM DLR relay results are performed by applying RTDS Hardware-in-the-loop (HIL) testing. The results of the DLR model are found to be in accordance with those obtained by the relay which also validates the model. Finally, recommendations and future work are proposed such as DLR implementation for cables using PMU data. ... Read more

State estimation (SE) is a crucial tool for power system state monitoring since the control center requires a process to deal with a large number of imprecise measurements. Several SE methods have been applied and developed for the electric power system in the transmission level in the past several decades. Meanwhile, SE for the distribution level remained in the background for a long time since the distribution networks were mainly radial with uni-directional power flows, making classical monitoring and control functions sufficient. Recently, due to the liberalization of the energy market, growing penetration of distributed generation, mainly renewable energy sources, and distributed energy resources such as electric vehicles, the distribution system has been gradually changing from passive into active grids. This requires more sophisticated monitoring and control of the distribution network via a distribution management system (DMS) to ensure optimal integration and maximize the grid hosting capacity. Since one of the key functions of DMS for real-time operation is the SE procedure, this calls for the following: (i) development of SE techniques for the distribution level, so-called distribution system state estimation (DSSE); (ii) more deployment of time-synchronized devices like phasor measurement units (PMU) that can directly measure and acquire accurate and time-aligned phasors with typical refresh rates up to 20-60 times per second.

Two types of DSSE algorithms were developed and implemented on the real-life 50 kV ring distribution grid composed of PMU devices by using the real-time simulation platform. The first is a static approach. The problem is formulated as a WLS problem to be solved based on the iterative Newton method, known as static state estimation (SSE). The second is a dynamic approach, which is more advanced, known as the forecasting-aided state estimation (FASE). It is one of the particular applications of the dynamic state estimation (DSE) concept based on the quasi-steady-state operating conditions. The dynamic formulation is solved using the extended Kalman filter (EKF) technique. This research aims to implement distribution system state estimation (DSSE) algorithms coupled with the auxiliary function, the so-called anomaly detection discrimination and identification (ADDI), into the distribution network. Both normal and abnormal operation scenarios of the power system are simulated to validate the algorithms.

The results reveal that the FASE is superior to the SSE algorithm in terms of estimation accuracy and computational time under normal operating conditions. However, under abnormal conditions, if there is no ADDI module, the performances of both algorithms are degraded significantly due to erroneous measurements. The FASE algorithm loses the system states' trajectory when sudden load change occurs. These issues point out the necessity of using the ADDI module against possible disturbances in real-life networks. In the end, the results show that the proposed FASE algorithm coupled with the ADDI module can accurately estimate the states under both normal and abnormal operations. One significant contribution is that the proposed algorithm can perform adequately fast so that it can process every high-speed measurement from PMU devices. ... Read more