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Supervisory Control and Data Acquisition (SCADA) systems have proved to be one of the important ingredients for substation automation. Legacy SCADA architectures are based on proprietary hardware and software-based Remote Terminal Units (RTUs) with little scope for modifications and expansions. Current SCADA architectures are based on distributed systems with Intelligent Electronic Devices (IEDs) based on IEC 61850. These SCADA systems are implemented with a centralized server located at control centers for performing various substation automation functions. With this kind of architecture, a local Human Machine Interface (HMI) is normally implemented to have access to the operational data at the substation level. Certain specific software applications catering to asset management functions, which are useful for the local operators, are also implemented at the substation level. All such local applications have to access data from IEDs, legacy RTUs, etc., called hardware nodes. Since these hardware nodes follow different communication interface methods, the software applications running in the desktop computers also have to adopt hardware-specific communication interface routines. Fast and reliable data transfer among these heterogeneous systems coupled with seamless integration to host applications is a technical challenge. The paper presents a generic Soft Data Bus (SDB), a communication interface framework between hardware nodes and software applications. SDB provides a common interface method on both sides which enables seamless access of data from RTUs which can easily be integrated with the local HMI and other asset management applications. It can be scaled up for a number of hardware nodes and data points without sacrificing much of performance. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of Greenhouse Gas (GHG) emission projections for grid-connect thermal power generation in India till 2030. Future power generation projections are obtained by utilizing an integrated power and energy planning tool. The optimal mix of thermal power generation from coal, natural gas and diesel is found by using a multiperiod generalized network flow model. Using a novel hybrid approach, the study, comprising different scenarios of thermal power generation efficiencies, highlights an important national strategy for low carbon growth [ABSTRACT FROM AUTHOR]

This article presents a simple reconfiguration algorithm that is specially suited for balanced and unbalanced radial distribution systems. The proposed algorithm involves less complex mathematical expressions, and a more efficient network configuration can be obtained with reduced power losses. The choice of the switches to be closed is based on the calculation of bus voltages and the minimum total system losses using a power flow program. This method takes a smaller number of switchings and less CPU time. Results obtained on one balanced system and two unbalanced systems are also presented and compared with the existing methods. [ABSTRACT FROM AUTHOR]

Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard approach to estimating the coil sensitivity maps prior to the reconstruction stage. In this work, we proceed differently and introduce a new calibration-less multi-coil CS reconstruction method. Calibration-less techniques no longer require the prior extraction of sensitivity maps to perform multi-coil image reconstruction but usually alternate estimation sensitivity map estimation and image reconstruction. Here, to get rid of the nonconvexity of the latter approach we reconstruct as many MR images as the number of coils. To compensate for the ill-posedness of this inverse problem, we leverage structured sparsity of the multi-coil images in a wavelet transform domain while adapting to variations in SNR across coils owing to the OSCAR (octagonal shrinkage and clustering algorithm for regression) regularization. Coil-specific complex-valued MR images are thus obtained by minimizing a convex but nonsmooth objective function using the proximal primal-dual Condat-Vù algorithm. Comparison and validation on retrospective Cartesian and non-Cartesian studies based on the Brain fastMRI data set demonstrate that the proposed reconstruction method outperforms the state-of-the-art (ℓ1-ESPIRiT, calibration-less AC-LORAKS and CaLM methods) significantly on magnitude images for the T1 and FLAIR contrasts. Additionally, further validation operated on 8 to 20-fold prospectively accelerated high-resolution ex vivo human brain MRI data collected at 7 Tesla confirms the retrospective results. Overall, OSCAR-based regularization preserves phase information more accurately (both visually and quantitatively) compared to other approaches, an asset that can only be assessed on real prospective experiments.