Visual Analytics for Power Grid Contingency Analysis
TL;DR: A proposed visual-analytics pipeline for power grid management can transform approximately 100 million contingency scenarios to a manageable size and form so that grid operators can examine individual scenarios and devise preventive or mitigation strategies in a timely manner.
Abstract: Contingency analysis employs different measures to model scenarios, analyze them, and then derive the best response to any threats. A proposed visual-analytics pipeline for power grid management can transform approximately 100 million contingency scenarios to a manageable size and form. Grid operators can examine individual scenarios and devise preventive or mitigation strategies in a timely manner. Power grid engineers have applied the pipeline to a Western Electricity Coordinating Council power grid model.
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01 Jan 2016
TL;DR: The power system analysis and design is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you for downloading power system analysis and design. As you may know, people have search numerous times for their favorite novels like this power system analysis and design, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful bugs inside their laptop. power system analysis and design is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the power system analysis and design is universally compatible with any devices to read.
222 citations
13 Oct 2015
TL;DR: This work presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive process of integrating NoSQL data stores to manage large amounts of data.
Abstract: With more and more data generated, it has become a big challenge for traditional architectures and infrastructures to process large amounts of data within an acceptable time and resources. In order...
85 citations
TL;DR: In this article, the authors considered a two-stage stochastic mixed-integer program to seek the optimal islanding operations under severe contingency states and presented an efficient decomposition method to solve this problem for large-scale power systems.
Abstract: Recently, there has been an increasing concern regarding the security and reliability of power systems due to the onerous consequences of cascading failures. Among many emergency control operations, controlled power grid islanding is a last resort yet powerful method to prevent large-scale blackouts. Islanding operations split the power grid into self-sufficient operational subnetworks and avoid cascading failures by isolating the failed elements of the power system into a non-operational island. In this paper, we consider a two-stage stochastic mixed-integer program to seek the optimal islanding operations under severe contingency states. Line switching and controlled load shedding are the main tools for the islanding operations and load shedding is considered as a measurement to gauge system’s inability to respond to disruption. The number of possible extreme contingencies grows exponentially as the size of the grid increases, and this results in a large-scale mixed-integer program, which is a computationally challenging problem to solve. We present an efficient decomposition method to solve this problem for large-scale power systems.
18 citations
22 Sep 2016
TL;DR: This paper presents a parallelization of the exact k-means++ algorithm, with a proof of its correctness, and develops implementations for three distinct shared-memory architectures: multicore CPU, high performance GPU, and the massively multithreaded Cray XMT platform.
Abstract: In recent years k-means++ has become a popular initialization technique for improved k-means clustering. To date, most of the work done to improve its performance has involved parallelizing algorithms that are only approximations of k-means++. In this paper we present a parallelization of the exact k-means++ algorithm, with a proof of its correctness. We develop implementations for three distinct shared-memory architectures: multicore CPU, high performance GPU, and the massively multithreaded Cray XMT platform. We demonstrate the scalability of the algorithm on each platform. In addition we present a visual approach for showing which platform performed k-means++ the fastest for varying data sizes.
9 citations
Cites methods from "Visual Analytics for Power Grid Con..."
...The choice of input sizes in these experiments was largely driven by our earlier research that led us to parallelize these algorithms [26], [11]....
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...The goal of this initial work was to perform data clustering on power grid contingency scenarios [26], [11], in which there were millions of contingency scenarios (n), with thousands of values for each scenario (m)....
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TL;DR: This paper uses the crossfilter.js library to achieve real-time computation of data cube aggregations for constantly changing user-defined filters, resulting in a fluid visualization of demand parameters aggregated according to many different factors or dimensions.
8 citations
Cites methods from "Visual Analytics for Power Grid Con..."
...Successful applications of VA for energy analytics have been presented in last years, including household and residential demand analytics [4, 5, 6], as well as analysis of large electric power grids using network analysis, such as force directed algorithms [7, 8] and other visualization techniques [9]....
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References
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TL;DR: New algorithms for betweenness are introduced in this paper and require O(n + m) space and run in O(nm) and O( nm + n2 log n) time on unweighted and weighted networks, respectively, where m is the number of links.
Abstract: Motivated by the fast‐growing need to compute centrality indices on large, yet very sparse, networks, new algorithms for betweenness are introduced in this paper. They require O(n + m) space and run in O(nm) and O(nm + n2 log n) time on unweighted and weighted networks, respectively, where m is the number of links. Experimental evidence is provided that this substantially increases the range of networks for which centrality analysis is feasible. The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require ?(n 3) time and ?(n 2) space, where n is the number of actors in the network.
4,190 citations
TL;DR: A fast technique has been developed for the automatic ranking and selection of contingency cases for a power system contingency analysis study and results of this technique applied to different test systems are presented.
Abstract: A fast technique has been developed for the automatic ranking and selection of contingency cases for a power system contingency analysis study. A contingency list is built containing line and generator outages which are ranked according to their expected severity as reflected in voltage level degradation and circuit overloads. An adaptive contingency processorcan be set up by performing sequential contingency tests starting with the most severe contingencies at the top of the list and proceeding down the list, stopping when the severity goes below a threshold. Computational results of this technique applied to different test systems are presented.
466 citations
TL;DR: An efficient method is proposed to obtain a good initial codebook that can accelerate the convergence of the generalized Lloyd algorithm and achieve a better local minimum as well.
Abstract: The generalized Lloyd algorithm plays an important role in the design of vector quantizers (VQ) and in feature clustering for pattern recognition. In the VQ context, this algorithm provides a procedure to iteratively improve a codebook and results in a local minimum that minimizes the average distortion function. We propose an efficient method to obtain a good initial codebook that can accelerate the convergence of the generalized Lloyd algorithm and achieve a better local minimum as well. >
374 citations
01 Jan 2016
TL;DR: The power system analysis and design is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you for downloading power system analysis and design. As you may know, people have search numerous times for their favorite novels like this power system analysis and design, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful bugs inside their laptop. power system analysis and design is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the power system analysis and design is universally compatible with any devices to read.
222 citations
19 Apr 2010
TL;DR: A novel application of parallel betweenness centrality to power grid contingency selection is presented and is expected to provide a quick and efficient solution to massive contingency selection problems to help power grid operators to identify and mitigate potential widespread cascading power grid failures in real time.
Abstract: In Energy Management Systems, contingency analysis is commonly performed for identifying and mitigating potentially harmful power grid component failures. The exponentially increasing combinatorial number of failure modes imposes a significant computational burden for massive contingency analysis. It is critical to select a limited set of high-impact contingency cases within the constraint of computing power and time requirements to make it possible for real-time power system vulnerability assessment. In this paper, we present a novel application of parallel betweenness centrality to power grid contingency selection. We cross-validate the proposed method using the model and data of the western US power grid, and implement it on a Cray XMT system – a massively multithreaded architecture – leveraging its advantages for parallel execution of irregular algorithms, such as graph analysis. We achieve a speedup of 55 times (on 64 processors) compared against the single-processor version of the same code running on the Cray XMT. We also compare an OpenMP-based version of the same code running on an HP Superdome shared-memory machine. The performance of the Cray XMT code shows better scalability and resource utilization, and shorter execution time for large-scale power grids. This proposed approach has been evaluated in PNNL's Electricity Infrastructure Operations Center (EIOC). It is expected to provide a quick and efficient solution to massive contingency selection problems to help power grid operators to identify and mitigate potential widespread cascading power grid failures in real time.
83 citations