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A new concept, called synchronized Lissajous curve , is proposed to study synchro-waveform measurements that are obtained from waveform measurement units (WMUs) in power distribution systems. Two types of synchronized Lissajous curves are discussed. The first one is created by plotting the difference of two synchronized voltage waveforms versus the difference of two synchronized current waveforms. The second one is created by plotting the difference of two synchronized voltage waveforms versus the difference of the derivatives of two synchronized current waveforms. These two curves carry valuable and complementary information about the state of the power system and the root cause of events and disturbances. We characterize the area , rotational angle , and shape of the introduced synchronized Lissajous curves to study events and disturbances in power distribution feeders.

The use of deep UV resist hardening for high temperature resist-masked processes is becoming more widespread throughout the semiconductor industry. In this report we present data on the wavelength and thermal dependence of the deep UV crosslinking process in novolak resin obtained through dissolution studies and SEM micrographs. An excimer laser was used to provide light of differing wavelengths. The results indicate that longer wavelengths near 308 nm promote crosslinking throughout the 1.5 micron thick resin, while shorter wavelengths induce crosslinking to limited depths. Heating of the wafer during the deep UV exposure is shown to greatly accelerate the crosslinking process, compared to a sequential expose and bake process. SEM micrographs of various stages of hardening in resist are shown. The resist hardening process employed by one commercial system is discussed in terms of the findings of this study.

The moisture origins and associated physical mechanisms for tornadoes of various climatic regions of the United States were investigated. The NOAA Air Resources Laboratory HYSPLIT model and a moisture attribution algorithm were used in conjunction with statistical analyses to explore these relationships on a climate scale (1981–2017). It was found that moisture sources of United States tornadic convection exhibit distinctive regionality. For example, moisture contributions to east coast tornadoes primarily emanate from the Atlantic Ocean as opposed to the Gulf of Mexico. Moisture sources of a class of non-significant severe thunderstorms also show regionality based on the location of storm occurrence with latitudinal differences to that of tornadic storms. Moisture increases for tornadic and non-significant severe storms were shown to be closely related to the respective air parcel's temperature, with underlying sea surface temperatures playing a less important role. Long-term trends of moisture uptake and horizontal advection were also explored using the Mann-Kendall test and linear regressions. Both statistical analyses demonstrate that the magnitude of advective fluxes and the rate of moisture increases have been rising since the 1980s, which can increase the complexity of forecasting downstream convection. The potential ramifications of these trends on storm development and prediction are discussed to conclude the article.

Electricity price forecasting is critical to numerous tasks in the power system such as strategic bidding, generation scheduling, optimal scheduling of storage reserves and system analysis. Most existing price forecasting models focus on hourly prediction for the day ahead market. This work focuses on the real-time, 5-minute market, with the goal of developing a model able to capture both the long- and short-term temporal distribution of the data. Extending the recent advances in deep learning models of time series forecasting, the proposed model - named HFnet - is a novel multi-branch Gated Recurrent Unit (GRU) architecture for electricity price forecasting. Extensive empirical analyses using real-time data from the New York Independent System Operator (NYISO) illustrate the value of the proposed model when compared to state-of-art prediction models, with an average reduction in error of 10%.

Abstract A novel multi-scale domain overlapping coupling methodology designed to couple a computational fluid dynamics (CFD) code with a system thermal hydraulic (STH) code was developed and its performance was investigated. The methodology has been implemented in the coupling infrastructure code Janus, developed at the University of Michigan, providing methods for the on-the-fly data transfer through memory between the commercial CFD code STAR-CCM+ and the US NRC best-estimate thermal hydraulic system code TRACE. Coupling between these two software packages is motivated by the desire to extend the range of applicability of TRACE to scenarios in which local momentum and energy transfer are important, such as three-dimensional mixing of localized slugs of deborated or cold water in the downcomer and lower plenum of a reactor pressure vessel. The intra-fluid shear forces necessary to correctly capture these effects are neglected in the TRACE equations of motion, but are readily calculated from CFD solutions. CFD/STH coupling implementations therefore have applications in reactor transients such as boron dilution scenarios, Anticipated Transient Without SCRAM (ATWS) and Main Steam Line Break (MSLB). The proposed method is based on aliasing all spatial sources and sinks of momentum in the CFD domain as frictional losses in the system code domain. The internal velocity fields and, consequently, the inertial component of the pressure field are maintained consistent between the CFD and STH domains through a complementary velocity-matching interface. In this paper, coupled simulations are performed on Cartesian and cylindrical geometry with emphasis on consistency, convergence, and stability during transient scenarios. Results show that the presented domain overlapping coupling method is capable of adjusting pressure and velocity profiles of multi-dimensional system code solutions to match CFD solutions accurately. Important characteristics of transient simulations were found to include the background flow rate, specifically the stabilizing effect of viscous forces, as well as the time derivative of the flow rate. Under certain adverse conditions, the basic coupling method is found to produce unstable behavior. A stabilization method for adjusting CFD data is laid out and found to significantly improve the method’s performance under the most challenging conditions. Recommendations are laid out for further improving the coupling via advanced time stepping methods.

Abstract We demonstrate the importance of stock turnover on industrial energy efficiency through a literature review and a case study of energy-intensive equipment, i.e. the electric arc furnace in the US steel industry. We describe the common methods for assessing stock turnover. We have found that both stock turnover and retrofit are important elements to explain the energy efficiency improvement rates. We investigated the development of electricity use in electric arc furnaces in the United States by tracking the development of individual furnaces over the period 1990–2002. This provides a detailed picture of changes in the stock or fleet of furnaces through turnover and/or retrofit. Our results confirm the results of other empirical studies that there is no clear lifetime of equipment. However, retired furnaces are distinctly less efficient than furnaces remaining in the stock, while new furnaces are distinctly more efficient than the average stock. We found an annual average improvement in specific electricity consumption of 1.3%/year over the studied period, of which 0.7%/year was due to stock turnover and 0.5%/year due to retrofit of stock in service throughout the period.

A new state estimation method is proposed for power distribution networks that suffer from low-observability. The proposed distribution system state estimation (DSSE) method leverages the high reporting rate of only a small number of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, to unmask and characterize sparsity among the state variables. The DSSE problem is formulated over differential synchrophasors as an adaptive group sparse recovery problem to track the changes that are made in the states of the system due to the events that are captured in D-PMU measurements. The formulated DSSE is further augmented to use adequate side information on the support of the vector of unknowns that is obtained from the outcome of an event-zone identification analysis prior to solving the DSSE problem. The sufficient conditions for the uniqueness of the obtained sparse recovery solution are derived with respect to the available side information. Moreover, a calibration mechanism is developed to address drifting in the tracking state estimation to enhance robustness.

Abstract Electricity markets, for the purpose of dispatch, approximate non-linear generator dynamics by a constant ramp rate R MW/10 min. The scalar R enables generator serving entity (GSE) to submit convex energy bids. Given the high penetration of renewable energy resources in US utilities, particularly California and Mid-West Independent System Operators (ISO), hard-to-predict wind ramps within the operating hour necessitate frequent ramping of generators, also known as load-following. A generator’s non-linear energy conversion dynamics varies widely over the operating hour. Consequently, the assumption of a constant rate R is often violated under fast changing operating conditions. In this paper, we propose a smart local automation on a non-linear energy conversion model of a coal-fired steam turbine. The proposed controller enables an improved and a near-linear dynamic response, ensuring fulfillment of convex bids.

To meet the increasing challenges on global warming and customer demands, the current hierarchical structure of electric power grid is undergoing a rapid change by focusing on high efficiency, reliability and flexibility. To address such challenges, the new concept of smart grid with a sophisticated communication infrastructure has emerged. While power grid systems are based on traditional information architectures at present, the evolving smart grid has a strong need on communication connections among a huge amount of distributed elements, such as generators, substations, monitoring sensors and customers. However, the power industry is facing dilemma that communication resources are limited due to the lack of wireless spectrum resources and the restrictions of wired applications. This paper firstly provides the architecture of the wide-area monitoring system for smart grid by applying a spectrum sensing and sharing technique of cognitive radio, based on an overview of the current communication technologies. Afterwards, the paper presents the feasible application of cognitive communication architecture on the existing power quality monitoring system by combining long-term and short-term monitoring approaches.