Transient stability simulation of a large-scale and interconnected electric power system involves solving a large set of differential algebraic equations (DAEs) at every simulation time-step. With the ever-growing size and complexity of power grids, dynamic simulation becomes more time-consuming and computationally difficult using conventional sequential simulation techniques. To cope with this challenge, this paper aims to develop a fully distributed approach intended for implementation on High Performance Computer (HPC) clusters. A novel, relaxation-based domain decomposition algorithm known as Parallel-General-Norton with Multiple-port Equivalent (PGNME) is proposed as the core technique of a two-stage decomposition approach to divide the overall dynamic simulation problem into a set of subproblems that can be solved concurrently to exploit parallelism and scalability. While the convergence property has traditionally been a concern for relaxation-based decomposition, an estimation mechanism based on multiple-port network equivalent is adopted as the preconditioner to enhance the convergence of the proposed algorithm. The proposed algorithm is illustrated using rigorous mathematics and validated both in terms of speed-up and capability. Moreover, a complexity analysis is performed to support the observation that PGNME scales well when the size of the subproblems are sufficiently large.

Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm's effectiveness at identifying unique paths for each type of event and the accompanying classifier's ability to accurately discern each type of event.