Accurate Interpolation of Terrain Surfaces from Over-Sampled Grid Data

2008 ◽  
pp. 351-368
Author(s):  
Narcís Coll ◽  
Marité Guerrieri ◽  
Maria-Cecilia Rivara ◽  
J. Antoni Sellarès
Keyword(s):  
Grid Data

scholarly journals The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3731 ◽  
Author(s):  
Mohamad El Hariri ◽  
Eric Harmon ◽  
Tarek Youssef ◽  
Mahmoud Saleh ◽  
Hany Habib ◽  
...  
Keyword(s):  
Smart Grid ◽  
Protocol Analysis ◽  
Data Detection ◽  
Detection Accuracy ◽  
Wide Spread ◽  
Grid Data ◽  
Iec 61850 ◽  
Forecasting Error ◽  
Heterogeneous Nature ◽  
Statistical Indicators

The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.

scholarly journals Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

2016 ◽  
Vol 8 (4) ◽  
pp. 409-419 ◽  
Author(s):  
Tobias Dalhaus ◽  
Robert Finger
Keyword(s):  
Crop Production ◽  
Market Failure ◽  
Weather Station ◽  
Yield Losses ◽  
Grid Data ◽  
Weather Index ◽  
Basis Risk ◽  
Promising Alternative ◽  
Station Data ◽  
Adverse Weather

Abstract Adverse weather events occurring at sensitive stages of plant growth can cause substantial yield losses in crop production. Agricultural insurance schemes can help farmers to protect their income against downside risks. While traditional indemnity-based insurance schemes need governmental support to overcome market failure caused by asymmetric information problems, weather index–based insurance (WII) products represent a promising alternative. In WII the payout depends on a weather index serving as a proxy for yield losses. However, the nonperfect correlation of yield losses and the underlying index, the so-called basis risk, constitutes a key challenge for these products. This study aims to contribute to the reduction of basis risk and thus to the addition of risk-reducing properties of WII. More specifically, the study tests whether grid data for precipitation (vs weather station data) and phenological observations (vs fixed time windows for index determination) that are provided by public institutions can reduce spatial and temporal basis risk and thus improve the performance of WII. An empirical example of wheat production in Germany is used. No differences were found between using gridded and weather station precipitation, whereas the use of phenological observations significantly increases expected utility. However, even if grid data do not yet reduce basis risk, they enable overcoming several disadvantages of using station data and are thus useful for WII applications. Based on the study’s findings and the availability of these data in other countries, a massive potential for improving WII can be concluded.

scholarly journals Storage Aware Resource Allocation for Grid Data Streaming Pipelines

2008 ◽  
Author(s):  
Wen Zhang ◽  
Junwei Cao ◽  
Yisheng Zhong ◽  
Lianchen Liu ◽  
Cheng Wu
Keyword(s):  
Resource Allocation ◽  
Data Streaming ◽  
Grid Data
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