QoS-enabled middleware for smart grids

Emerging smart grid systems must be able to react quickly and predictably, adapting their operation to changing energy supply and demand, by controlling energy consuming and energy storage devices. An intrinsic problem with smart grids is that energy produced from in-house renewable sources is affected by fluctuating weather factors. The applications driving smart grids operation must rely on a solid communication network that is secure, highly scalable, and always available. Thus, any communication infrastructure for smart grids should support its potential of producing high quantities of real-time data, with the goal of reacting to state changes by actuating on devices in real-time, while providing Quality of Service (QoS)

Parallel algorithms for interactive manipulation of digital terrain models

Interactive three-dimensional graphics applications, such as terrain data representation and manipulation, require extensive arithmetic processing. Massively parallel machines are attractive for this application since they offer high computational rates, and grid connected architectures provide a natural mapping for grid based terrain models. Presented here are algorithms for data movement on the massive parallel processor (MPP) in support of pan and zoom functions over large data grids. It is an extension of earlier work that demonstrated real-time performance of graphics functions on grids that were equal in size to the physical dimensions of the MPP. When the dimensions of a data grid exceed the processing array size, data is packed in the array memory. Windows of the total data grid are interactively selected for processing. Movement of packed data is needed to distribute items across the array for efficient parallel processing. Execution time for data movement was found to exceed that for arithmetic aspects of graphics functions. Performance figures are given for routines written in MPP Pascal

Experimental Validation and Deployment of Observability Applications for Monitoring of Low-voltage Distribution Grids

Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes

A Framework for Synthetic Power System Dynamics

Information on power grids is confidential and thus real data is often inaccessible. This necessitates the use of synthetic power grid models in research. So far the models used, for example, in machine learning had to be very simple and homogeneous to produce large ensembles of robust grids. We present a modular framework to generate synthetic power grids that considers the heterogeneity of real power grid dynamics but remains simple and tractable. This enables the generation of large sets of synthetic grids for a wide range of applications. We also include the major drivers of fluctuations on short-time scales. The synthetic grids generated are robust and show good synchronization under all evaluated scenarios, as should be expected for realistic power grids. This opens the door to future research that studies grids under severe stress due to extreme events which could lead to destabilization and black-outs. A software package that includes an efficient Julia implementation of the framework is released as a companion to the paper

Adaptive Learning Terrain Estimation for Unmanned Aerial Vehicle Applications

For the past decade, terrain mapping research has focused on ground robots using occupancy grids and tree-like data structures, like Octomap and Quadtrees. Since flight vehicles have different constraints, ground-based terrain mapping research may not be directly applicable to the aerospace industry. To address this issue, Adaptive Learning Terrain Estimation algorithms have been developed with an aim towards aerospace applications. This thesis develops and tests Adaptive Learning Terrain Estimation algorithms using a custom test benchmark on representative aerospace cases: autonomous UAV landing and UAV flight through 3D urban environments. The fundamental objective of this thesis is to investigate the use of Adaptive Learning Terrain Estimation algorithms for aerospace applications and compare their performance to commonly used mapping techniques such as Quadtree and Octomap. To test the algorithms, point clouds were collected and registered in simulation and real environments. Then, the Adaptive Learning, Quadtree, and Octomap algorithms were applied to the data sets, both in real-time and offline. Finally, metrics of map size, accuracy, and running time were developed and implemented to quantify and compare the performance of the algorithms. The results show that Quadtree yields the computationally lightest maps, but it is not suitable for real-time implementation due to its lack of recursiveness. Adaptive Learning maps are computationally efficient due to the use of multiresolution grids. Octomap yields the most detailed maps, but it produces a high computational load. The results of the research show that Adaptive Learning algorithms have significant potential for real-time implementation in aerospace applications. Their low memory load and variable-sized grids make them viable candidates for future research and development

Fundamentals of 3-D Neutron Kinetics and Current Status

This lecture includes the following topics: 1) A summary of the cell and lattice calculations used to generate the neutron reaction data for neutron kinetics, including the spectral and burn up calculations of LWR cells and fuel assembly lattices, and the main nodal kinetics parameters: mean neutron generation time and delayed neutron fraction; 2) the features of the advanced nodal methods for 3-D LWR core physics, including the treatment of partially inserted control rods, fuel assembly grids, fuel burn up and xenon and samarium transients, and ex core detector responses, that are essential for core surveillance, axial offset control and operating transient analysis; 3) the advanced nodal methods for 3-D LWR core neutron kinetics (best estimate safety analysis, real time simulation); and 4) example applications to 3-D neutron kinetics problems in transient analysis of PWR cores, including model, benchmark and operational transients without, or with simple, thermal-hydraulics feedback

Frequency Monitoring Network (FNET) Data Center Development and Data Analysis

Frequency Monitoring Network (FNET) is an Internet-based, wide-area phasor measurement system that collects power system data using Frequency Disturbance Recorders (FDRs) that are installed at the distribution level. The FNET data center enables the monitoring of bulk power systems, and provides wide-area situational awareness and disturbance analysis for understanding power system disturbances and system operations. Therefore, the data center plays a very critical role in the entire FNET system framework. In recent years, many potential challenges brought by the rapid expansion of the FNET system have underlined the importance of designing the next-generation FNET data center. More discussions about the motivation and guidelines to design the next-generation FNET data center will be presented in Chapter 2, along with a brief introduction of the new infrastructure composing of multiple data storage and application layers. A distributed alarming agent that communicates between real-time applications and near-real-time applications is discussed in detail. Chapter 3 proposes the data storage solutions for FNET time-series measurement data, configuration data and analysis records. Chapter 4 addresses the challenges of the real-time application development. The algorithm, configuration parameters and data processing procedures of the real-time event detection, oscillation detection, and islanding detection are presented in detail. Chapter 5 introduces the implementation of the FNET map-based web display using the measurement data feed provided by the openHistorian data publisher service. Besides contributing to the situation awareness applications, the researches presented here explore novel data analysis perspectives to investigate power grids’ behavior. Chapter 6 introduces a frequency distribution probability calculation method, applies this method to frequency measurement data from 2005-2013 collected by the FNET system, investigates the distribution probability of frequency data over North American and also worldwide power grids, and compares the distribution patterns during different years, seasons, days of a week and periods of a day. Chapter 7 presents a solution method to produce replay videos based on FDRs’ normalized voltage magnitude data and investigates the voltage magnitude pattern changes over the Eastern Interconnection (EI) during events and days by using historical FNET measurement data. Conclusions and possible future research topics are given in Chapter 8

Improving Real-Time Data Dissemination Performance by Multi Path Data Scheduling in Data Grids

The performance of data grids for data intensive, real-time applications is highly dependent on the data dissemination algorithm employed in the system. Motivated by this fact, this study first formally defines the real-time splittable data dissemination problem (RTS/DDP) where data transfer requests can be routed over multiple paths to maximize the number of data transfers to be completed before their deadlines. Since RTS/DDP is proved to be NP-hard, four different heuristic algorithms, namely kSP/ESMP, kSP/BSMP, kDP/ESMP, and kDP/BSMP are proposed. The performance of these heuristic algorithms is analyzed through an extensive set of data grid system simulation scenarios. The simulation results reveal that a performance increase up to 8 % as compared to a very competitive single path data dissemination algorithm is possible

Heterogeneous data reduction in WSN: Application to Smart Grids

International audienceThe transformation of existing power grids into Smart Grids (SGs) aims to facilitate grid energy automation for a better quality of service by providing fault tolerance and integrating renewable energy resources in the power market. This evolution towards a smarter electricity grid requires the ability to transmit in real time a maximum of data on the network usage. A Wireless Sensor Network (WSN) distributed across the power grid is a promising solution, given the reduced cost and ease of deployment of such networks. These advantages come up against the unstable radio links and limited resources of WSN. In order to reduce the amount of data sent over the network, and thus reduce energy consumption, data prediction is a potent solution of data reduction. It consists on predicting the values sensed by sensor nodes within certain error threshold, and resides both at the sensors and at the sink. The raw data is sent only if the desired accuracy is not satisfied, thereby reducing data transmission. We focus on time series estimation with Least Mean Square (LMS) for data prediction in WSN, in a Smart Grid context, where several applications with different data types and Quality of Service (QoS) requirements will exist on the same network. LMS proved its simplicity and robustness for a wide variety of applications, but the parameters selection (step size and filter length) can directly affect its global performance, choosing the right ones is then crucial. Having no clear and robust method on how to optimize these parameters for a variety of applications, we propose a modification of the original LMS that consists of training the filter for a certain time with the data itself in order to customize the aforementioned parameters. We consider different types of real data traces for the photo voltaic cells monitoring. Our simulation results provide a better data prediction while minimizing the mean square error compared to an existing solution in literatur