Toward a reliable, secure and fault tolerant smart grid state estimation in the cloud

Maheshwari, Ketan; Lim, Marcus; Wang, L.; Birman, Kenneth P.; Renesse, Robbert van

doi:10.1109/isgt.2013.6497831

Cited by 35 publications

(17 citation statements)

References 21 publications

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“…Alternatively the work in [14], [16] proposes the cloud computing platform for smart grid data storage and real-time analysis. They parallelize the processing in cloud computing environments to achieve faster computation.…”

Section: Overview Of Hpc and Big Data Analyticsmentioning

confidence: 99%

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Khan

Ashton

et al. 2015

IEEE Trans. Smart Grid

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Abstract-Phasor Measurement Units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronised measurements. The devices high data reporting rates present major computational challenges, in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents PDFA, a parallel detrended fluctuation analysis approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain, from the aspects of speedup, scalability and accuracy. The speedup of the PDFA in computation is initially analysed through Amdahl's Law, a revision to the law is then proposed, suggesting enhancements to its capability to analyse the performance gain in computation when parallelizing data intensive applications in a cluster computing environment.

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Section: Overview Of Hpc and Big Data Analyticsmentioning

confidence: 99%

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Khan

Ashton

et al. 2015

IEEE Trans. Smart Grid

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“…Debugging in such scenarios is one of the biggest challenges for users. However, the returns on investment are expected to be positive as many applications in the smart grid domain exhibit similar patterns [27].…”

Section: A Usabilitymentioning

confidence: 99%

Evaluating Cloud Computing Techniques for Smart Power Grid Design Using Parallel Scripting

Maheshwari

Birman

Wozniak

et al. 2013

2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing

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Abstract-Applications used to evaluate next-generation electrical power grids ("smart grids") are anticipated to be compute and data-intensive. In this work, we parallelize and improve performance of one such application which was run sequentially prior to the use of our cloud-based configuration. We examine multiple cloud computing offerings, both commercial and academic, to evaluate their potential for improving the turnaround time for application results. Since the target application does not fit well into existing computational paradigms for the cloud, we employ parallel scripting tool, as a first step toward a broader program of adapting portable, scalable computational tools for use as enablers of the future smart grids. We use multiple clouds as a way to reassure potential users that the risk of cloud-vendor lock-in can be managed. This paper discusses our methods and results. Our experience sheds light on some of the issues facing computational scientists and engineers tasked with adapting new paradigms and infrastructures for existing engineering design problems.

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“…However, this kind of data flow abstraction is essential in SG real time monitoring services. Accurate state estimation has become a key function in supervisory control and planning of electric power grids and is widely expected to play a significant role in the advance of smart grid [8]. Traditional methods compute state estimations by the least squares solution of linear equations.…”

Section: Langliers Specification Of a Cloud Based Operational Model Omentioning

confidence: 99%

A Specification Language for Performance and Economical Analysis of Short Term Data Intensive Energy Management Services

Merino

Tolosana-Calasanz

Bañares

et al. 2016

Economics of Grids, Clouds, Systems, and Services

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Abstract. Requirements of Energy Management Services include short and long term processing of data in a massively interconnected scenario. The complexity and variety of short term applications needs methodologies that allow designers to reason about the models taking into account functional and non-functional requirements. In this paper we present a component based specification language for building trustworthy continuous dataflow applications. Component behaviour is defined by Petri Nets in order to translate to the methodology all the advantages derived from a mathematically based executable model to support analysis, verification, simulation and performance evaluation. The paper illustrates how to model and reason with specifications of advanced data flow abstractions such as smart grids.

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Toward a reliable, secure and fault tolerant smart grid state estimation in the cloud

Cited by 35 publications

References 21 publications

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Evaluating Cloud Computing Techniques for Smart Power Grid Design Using Parallel Scripting

A Specification Language for Performance and Economical Analysis of Short Term Data Intensive Energy Management Services

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