Power system data management and analysis using synchrophasor data

Meier, Rich; Cotilla‐Sanchez, Eduardo; McCamish, Ben; Chiu, David; Histand, Miles; Landford, Jordan; Bass, R.

doi:10.1109/sustech.2014.7046248

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…Data value in power systems can provide guidance towards data acquisition, data processing, and data application. Data valuation can be determined by several factors, including data correlation, data fidelity, and data freshness [61]. To be specific, data correlation can be considered from two aspects: one is how it is related with power dispatch, fault evaluation, and risk assessment; the other one is the correlation within the data itself, where the data value will be higher when the correlation is higher.…”

Section: Big Data Technologies For Complex Power System Monitoringmentioning

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: Big Data Technologies For Complex Power System Monitoringmentioning

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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“…This model can be applied to our system to enhance the schema of electric power data by using accurate geo-information. Meier et al [18] proposed a data processing system for synchrophasor data, which processes high-granularity and high-cardinality data gathered from synchrophasor sensors, i.e., Phasor Measurement Units (PMUs), using a correlation method. The deployment of PMUs could improve the measurements of voltages and currents with accurate timestamps [19] .…”

Section: Related Workmentioning

confidence: 99%

Distributed storage system for electric power data based on Hbase

Jin,

Song,

Gong

et al. 2018

Big Data Min. Anal.

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Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status, debugging, and error records every single day. To guarantee the safety and sustainability of electric power systems, massive electric power data need to be processed and analyzed quickly to make real-time decisions. Traditional solutions typically use relational databases to manage electric power data. However, relational databases cannot efficiently process and analyze massive electric power data when the data size increases significantly. In this paper, we show how electric power data can be managed by using HBase, a distributed database maintained by Apache. Our system consists of clients, HBase database, status monitors, data migration modules, and data fragmentation modules. We evaluate the performance of our system through a series of experiments. We also show how HBase's parameters can be tuned to improve the efficiency of our system.

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“…Correlation is a well-known statistical and mathematical method for the compatibility analysis of large data sets. Meier et al [65] successfully used Pearson Product-Moment correlation to determine how well data is linearly correlated. Given two independent input data sets of X , Y with the length of N , and X , Y being either the phase data values of two PMU sites or the momentary magnitudes, the Pearson correlation submits to a correlation coefficient C (C ∈ [−1, 1]), which is based on the following equation:…”

Section: ) Data Storing and Routingmentioning

confidence: 99%

Energy big data: A survey

et al. 2016

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Energy is one of the most important parts in human life. As a significant application of energy, smart grid is a complicated interconnected power grid that involves sensors, deployment strategies, smart meters, and real-time data processing. It continuously generates data with large volume, high velocity, and diverse variety. In this paper, we first give a brief introduction on big data, smart grid, and big data application in the smart grid scenario. Then, recent studies and developments are summarized in the context of integrated architecture and key enabling technologies. Meanwhile, security issues are specifically addressed. Finally, we introduce several typical big data applications and point out future challenges in the energy domain.

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Power system data management and analysis using synchrophasor data

Cited by 12 publications

References 10 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

Distributed storage system for electric power data based on Hbase

Energy big data: A survey

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