A New Method for Processing and Application of Wide Area Measurement Big Data in a Power System

Song, Dunwen; Wen, Boying; Yang, Xuetao; Gu, Yubao; Wei, Shiquan; Ma, Shiying

doi:10.1109/cyberc.2015.69

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…Real-time applications, such as bilateral voice transmission, also exist in cellular technology. In the domain of power systems, data compression was a niche topic most of the time, but the recent rise in the complexity of modern power systems, along with the need for detailed measurements, calls for optimization of data transfer [9]. The types of information that must be known about the system to be managed precisely are presented in Figure 2.…”

Section: Data In Power Systemsmentioning

confidence: 99%

Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

Prokop,

Bień,

Barczentewicz

2023

Energies

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Significant amounts of data need to be transferred in order to optimize the operation of power grids. The development of advanced metering and control infrastructure ensures a growth in the amount of data transferred within smart grids. Data compression is a strategy to reduce the burden. This paper presents current challenges in the field of time-series data compression. This paper’s novel contribution is the division of data in smart grids to real-time data used for control purposes and big data sets used for non-time-critical analysis of the system. Both of these two applications have different requirements for effective compression. Currently used algorithms are listed and described with their advantages and drawbacks for both of these applications. Details needed for the implementation of an algorithm were also provided. Comprehensive analysis and comparison are intended to facilitate the design of a data compression method tailored for a particular application. An important contribution is the description of the influence of data compression methods on cybersecurity, which is one of the major concerns in modern power grids. Future work includes the development of adaptive compression methods based on artificial intelligence, especially machine learning and quantum computing. This review will offer a solid foundation for the research and design of data compression methods.

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Section: Data In Power Systemsmentioning

confidence: 99%

Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

Prokop,

Bień,

Barczentewicz

2023

Energies

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“…A similar study is also conducted on the security and resilience assessment in largescale grids using big data techniques [5]. Many researchers have also addressed the applications of big data and cloud computing in the areas of wide-area measurement, wide-area monitoring, and wide-area protection [6,7]. Some authors have also proposed common big data techniques used in smart grids [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Moradi

Shahinzadeh

Nafisi

et al. 2019

2020 14th International Conference on Protection and Automation of Power Systems (IPAPS)

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Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to localscale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions.

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Correlation data analysis for low-frequency oscillation source identification

Zuo

Xiang

Zhang

et al. 2017

2017 4th International Conference on Systems and Informatics (ICSAI)

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A New Method for Processing and Application of Wide Area Measurement Big Data in a Power System

Cited by 4 publications

References 1 publication

Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Correlation data analysis for low-frequency oscillation source identification

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