A Framework-Based Approach to Utility Big Data Analytics

Zhu, Junxing; Zhuang, Eric; Jian, Fu; Baranowski, John L.; Ford, Andrew; Shen, Jacson K.

doi:10.1109/tpwrs.2015.2462775

Cited by 40 publications

(18 citation statements)

References 7 publications

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“…The State Grid Corporation of China owns over 2.4 hundred million smart meters, making the total amount of collected data reach 200 TB for a year, while the total number of data in information centers can achieve up to 15 PB. Big Data is also often recognized as challenging in data volume, variety, velocity, and value in many applications [19,20], and the "4V"characteristics are reflected in the following aspects considering applications in the power system, which is illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

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Section: Introductionmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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“…4. CIM-based utility big data integration [16]. sharing between transmission system operators (TSOs), third parties and service providers alike.…”

Section: The European Network Of Transmission System Operators (Entsomentioning

confidence: 99%

“…In [16], an electric utility company utilizes big data via analytics and the proposed software framework is based on CIM IEC standards, in order to convert utility big data into operational decision support, promote efficiency and save costs. Figure 4 illustrates how utility big data applications can interact with each other using a CIM-based integration architecture, through a common enterprise service bus.…”

Section: The European Network Of Transmission System Operators (Entsomentioning

confidence: 99%

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Visualization Techniques for Electrical Grid Smart Metering Data: A Survey

Stefan

Lopez²,

Andreasen³

et al. 2017

2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)

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One of the considerable initiatives towards creating a smart society could be the guarantee of a smart, resilient and reliable power grid. As an attempt to improve the electricity supply service, it would be meaningful for the distributed system operators (DSOs) to be able to monitor the current status of the grid. The prediction of future possible critical situations would then be feasible using the available information, whereas, based on historical data, further grid expansion and reinforcement may be planned. A proper presentation and visualization of the near-real time metering data may constitute the baseline for bringing improvements to the power grid. This paper presents an approach to build an efficient visualization system so that the extracted smart meters information can be used in a meaningful manner. An overview of the use cases related to the visualization features is first presented, as a motivation for the choice of the relevant state of the art research. In relation to the knowledge provided by the metering data, a definition of the big data concept will be further introduced, according to the requirements established by the project definition. Geographic Information System (GIS) tools are useful to help visualize the collected big data in near-real time. For this reason, a survey of existing GIS software will be made so that the choice of the most suitable tool can be justified. Also, the integration of GIS technologies into the Common Information Model (CIM) aims to improve the visualization efficiency. As a consequence, investigating methods for adapting CIM standards to the GIS platform are also important.

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“…The authors in [2] suggest Electrical utilities increase inter-operability among intelligent electronic devices (IEDs) and systems to advance grid operation and control efficiency. Data analytics is the process of examining data sets in order to draw conclusions about the information they contain [3]. The implementation of DA is applied between electrical utilities and customers (residential, industrial, commercial and government) by collecting data, and making decisions [2].…”

Section: Introductionmentioning

confidence: 99%

Data Analytics for Electrical Distribution Systems with Micro PMUs (GMLC 1.4.9 Technical Report)

Hink

Piesciorovsky

2019

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A Framework-Based Approach to Utility Big Data Analytics

Cited by 40 publications

References 7 publications

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Visualization Techniques for Electrical Grid Smart Metering Data: A Survey

Data Analytics for Electrical Distribution Systems with Micro PMUs (GMLC 1.4.9 Technical Report)

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