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Wide-area measurement systems enable the wide-area damping controller (WADC) to use remote signals to enhance the small signal stability of large scale interconnected power systems. System operating condition variations and signal transmission time delays are the major factors to worsen the damping effect and even deteriorate the system stability. This paper proposes a novel measurement-based adaptive wide-area damping control scheme using oscillation mode prediction and system identification techniques. These techniques adjust the parameters of WADC as well as the time delay compensation in an online environment. To achieve fast online implementation, an identified high order multi-input multi-output (MIMO) model is deformed into a low order single-input single-output (SISO) model according to the residue of MIMO model. The SISO model can accurately represent the power system dynamics in the form of a transfer function, capturing the dominant oscillatory behaviors in the frequency range of interest. Moreover, the WADC has been implemented on a hardware test-bed (HTB) by adding its output signal to the excitation system of a selected generator. The effectiveness of the proposed measurement-based adaptive WADC has been demonstrated in a two-area four-machine system on the HTB under various disturbance scenarios.

Large blackouts are typically caused by cascading failure propagating through a power system by means of a variety of processes. Because of the wide range of time scales, multiple interacting processes, and the huge number of possible interactions, the simulation and analysis of cascading blackouts is extremely complicated. This paper defines cascading failure for blackouts and gives an initial review of the current understanding, industrial tools, and the challenges and emerging methods of analysis and simulation.

This paper develops a computationally efficient method to bound the impact of multiple uncertain parameters in a dynamic load model. Load model trajectory sensitivity is first conducted on regional dynamics only (e.g., a large industrial load bus and its model for adequately representing the bus voltage dynamics) to identify critical and correlated load parameters. Systemwide trajectory sensitivity on the entire power system model is then evaluated for this reduced set of parameters, and finally, the impact of multiple uncertain parameters on the representation of power system dynamics is bounded. To reinforce this reasoning, we elaborate on the conceptual meaning of the load model trajectory sensitivity, its implication, and its applicability to the entire power grid analysis. This research also develops a fluctuation index of trajectory sensitivity (FI-TS) to effectively rank and select the model parameters based on the impact of their perturbations on the system's dynamic performance. Case studies for the Korean power system demonstrate the validity and efficacy of the developed methods for adequately bounding the uncertainty impacts with reference to the comprehensive time-domain dynamic simulation approach.

Cascading failures present severe threats to power grid security, and thus vulnerability assessment of power grids is of significant importance. Focusing on analytic methods, this paper reviews the state of the art of vulnerability assessment methods in the context of cascading failures. These methods are based on steady-state power grid modeling or high-level probabilistic modeling. The impact of emerging technologies including phasor technology, high-performance computing techniques, and visualization techniques on the vulnerability assessment of cascading failures is then addressed, and future research directions are presented.