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.

As a power system evolves in size and complexity due to energy transition, the focus on the stability of the power system becomes more imperative than ever. Power system stability is tested when the system is subjected to large disturbances. Consequently, a conventional protection scheme is always deployed to take quick and automatic actions based on localized measurements, to restore the power system to a steady operating state. However, in many cases, further remedial actions by the system operator are necessary to avoid cascading failures and eventual instability problems. The introduction of phasor measurement units in the form of WAMS has led to improved awareness and
hence, better, capability to take remedial actions. In particular, the vast amount of data provided by the PMUs have led to the development of data-driven machine learning applications to aid the system operator in maintaining stability. One such application is power system event classification. In general, event classification is achieved using supervised and unsupervised methods. A review of pioneering literature reveals that offline models trained using both these methods can achieve a high order of classification accuracy. However, in a realistic, online setting with streaming PMU data, there is a possibility
of new, unknown events appearing in the data stream. In such a case, Offline
models which are trained on a limited number of known classes are unable to adapt to new class data. Therefore, there is a necessity for event learning applications that are capable of incrementally identifying new classes that appear in the PMU data stream. Hence, this thesis proposes the event clustering algorithm which performs in two stages – label correction stage and new class labeling stage. In the first stage, the algorithm corrects the class labels predicted by a supervised model, and flags and collects new incoming event instances through a shape-based similarity measure, i.e. DTW measure. In the subsequent second stage, it incrementally clusters the collected new event instances using Time-Series k-means clustering, where the hyperparameter of the number of clusters is automatically determined using a silhouette score clustering metric. The results of this algorithm show successful detection of new power system events with high classification accuracy. Also, final improvements are suggested to overcome certain drawbacks of the algorithm listed in the thesis.

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.

The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). This paper investigates the improvements of frequency responses of fully decoupled wind power generators (FDWG) by proposing a novel implementing of ultracapacitors (UC) within a hybrid scheme in real-time simulations of wind power plants. UCs are selected as ideal power sources in fast active power-frequency control due to their high power density and fast-reacting speed. Batteries and UCs combined hybrid energy storage systems (HESS) are formed to complement their characteristics. Droop-based and derivative-based control and virtual synchronous power (VSP) are the selected strategies to control power system frequency stability. The best frequency performance trading off with HESS cost is found by solving an optimization problem. The proposed optimization algorithm is used to define the HESS size and controller parameters. The optimization results are analysed to illustrate the improvements of frequency stability control comparing the results of droop and derivative-based control with the VSP control strategy.