Large scale outages have occurred worldwide in recent decades with some impacting 15-25% of a nation’s population. The complexity of blackouts has been extensively studied but many questions remain. As there are no perfect solutions to prevent blackouts, usually caused by a complex sequence of cascading events, a number of different measures need to be undertaken to minimize impact of future disturbances. Increase in deployment of phasor measurement units (PMUs) across the grid has given power industry an unprecedented technology to study dynamic behavior of the system in real time. Integration of large scale synchronized measurements with SCADA system requires a careful roadmap and methodology. When properly engineered, tested, and implemented, information extracted from synchrophasor data streams provides realtime observability for transmission system. Synchrophasor data can provide operators with quick insight into precursors of blackout (e.g., angular divergence) which are unavailable in traditional SCADA systems. Current visualization tools and SE functions, supported by SCADA, provide some basic monitoring. Inaccuracies in measurements and system models, absence of redundancy in the measured parameters or breaker statuses in most cases, and lack of synchronization and time resolution in SCADA data result in limited functionality and precision for a typical EMS required in today’s operating environment of tighter margins that require more frequent and more precise data. Addition of synchrophasor data, typically having several orders of magnitude higher temporal resolution, (i.e., 60 to 120 measurements per second as opposed to one measurement every 4 to 8 seconds), can help detect higher speed phenomena and system oscillations. Also, time synchronization to one micro-second allows for accurate comparison of phase angles across the grid and identification of major disturbances and islanding. This dissertation proposes a more comprehensive, holistic set of criteria for optimizing PMU placement with consideration for diverse factors that can influence PMU siting decision-making process and incorporates several practical implementation aspects. An innovative approach to interoperability testing is presented and solutions are offered to address the challenges. The proposed methodology is tested to prove the concept and address real-life implementation challenges, such as interoperability among the PMUs located across a large area.

Autonomous systems necessitate fast and reliable detection capabilities. The advancement of autonomous driving has intensified the demand for sophisticated obstacle detection algorithms, resulting in the integration of various sensors like LiDAR, radar, and cameras into vehicles. LiDAR is suitable for obstacle detection since it can detect the localization and intensity information of objects more precisely than radar while handling illumination and weather conditions better than cameras. However, despite an extensive body of literature exploring object detection utilizing LiDAR data, few solutions are viable for real-time deployment in vehicles due to computational constraints. Our research begins by evaluating state-of-the-art models for fast object detection using LiDAR on the Zenseact Open Dataset, focusing particularly on how their performance varies with the distance to the object. Our analysis of the dataset revealed that distant objects where often defined by very few points, posing challenges for detection. To address this, we experimented with point cloud superimposition with 1-4 previous frames to enhance point cloud density. However, we encountered issues with the handling of dynamic objects under rigid transformations. We addressed this by the inclusion of a time feature for each point to denote its origin time step. Initial experiments underscored the crucial role of this time feature in model success. Although superimposition initially decreased mean average precision except within 210-250 m, mean average recall improved beyond 80-100 m. This observation encouraged us to explore varying the number of superimposed point clouds across different ranges, optimizing the configuration for each range. Experimentation with this adaptive approach yielded promising results, enhancing the overall mAF1 score for the model. Additionally, our research highlights shortcomings in existing datasets that must be addressed to develop robust detectors and establish appropriate benchmarks.