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Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver. [ABSTRACT FROM AUTHOR]

Flood inundation probability is critical for situation awareness, flood mitigation, emergency response, and postevent damage assessment. Current flood inundation mapping approaches can be categorized into real-time (RT) and near-RT (NRT) processes based on the timing of data acquisition. However, the intrinsic limitations of each category largely hamper their applications for flood mapping. Taking the 2015 South Carolina flood in downtown Columbia as a case study, this paper proposes a flood inundation reconstruction model by enhancing the NRT normalized difference water index (NDWI) derived from remote sensing imagery with the RT data including stream gauge readings and social media (tweets). Splitting into three modules: water height module, global enhancement module, and local enhancement module, the proposed model first incorporates the gauge readings and the NDWI image to reconstruct a macroscale flood probability layer, which is then locally enhanced using the verified flood-related tweets. The final output of the model matches well with the U.S. Geological Survey inundation map and its surveyed high-water marks. Results suggest that by enhancing NRT imagery with RT data sources, the proposed flood inundation probability reconstruction model renders a more robust, spatially enhanced flood probability index for emergency responders to quickly identify areas in need of urgent attention. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of the Antennas and Propagation Society (AP-S) Administrative Committee (AdCom) of the Institute of Electrical and Electronics Engineers (IEEE) Summer Meeting held in Charleston, South Carolina on May 31, 2009. The financial performance of the IEEE in 2008 was tackled. Other issues addressed were the need to improve membership outside the U.S., the IEEE budget for 2009 and 2010 and future conferences.

The article provides information on the AdCom Winter Meeting of the Administrative Committee (AdCom) of the Institute of Electrical and Electronics Engineers's (IEEE's) Antennas and Propagation Society (AP-S) in Charleston, South Carolina from February 21 to 22, 2009. Among the topics discussed in the meeting include the awards to be given by AP-S, the reports of its past and present presidents, and its publications. The event featured some professionals such as Jennifer Bernhard and Bob Nevels.

The article reports on the appointment of Ian Walker of Clemson University in Clemson, South Carolina as Robotics and Automation Society vice president of Financial Activities. He has previously served on the editorial of the "IEEE Robotics & Automation magazine and has been an associate vice president for publications.

Accurate fault event diagnosis with incomplete and conflicting alarms given sensor malfunctions is a challenging problem for power system operators. To solve this problem, this study proposes a data-driven approach based on Mixed Integer Linear Programming (MILP) for fast determination of fault event scenarios with uncertainties. The uncertainties include failures and malfunction of relays and circuit breakers (CBs) as well as incomplete/incorrect sensor alarms at the control center. To improve the accuracy for fault event diagnosis, redundant alarms from multiple sources, i.e., Phasor Measurement Units (PMUs), Supervisory Control and Data Acquisition (SCADA), and Sequence of Events Recorders (SERs) are jointly used in this study. The temporal correlation of sensor alarms is incorporated in the constraints of the MILP model. The resulting data-driven algorithm determines the most credible fault scenario that is well supported by the available sensor alarms at the control center. Simulation results of the IEEE 14-bus system, the synthetic South Carolina 500-bus system, and a real-world complex event scenario demonstrate the effectiveness of the proposed approach for accurate and efficient fault event diagnosis. [ABSTRACT FROM AUTHOR]

Presents a case study to illustrate how outcomes-based assessment tools can be adapted and applied to develop a continuous quality improvement (CQI) process for an undergraduate mechanical engineering program. Mechanical engineering curriculum at the University of South Carolina, Columbia; Need for the systemic and systematic reform of educational programs.

The article reports on the Student Reception which is part of the Institute of Electrical and Electronics Engineers (IEEE) International Symposium on Antennas and Propagation and United States National Committee/Union Radio Science International (USNC/URSI) National Radio Science Meeting in Charleston, South Carolina on May 31, 2009. The annual Student Reception was attended by more than 100 participants. Details of the first annual AP-S Antenna Design Contest is also offered.