Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong; Zhang, Jun Jason; Zhang, Yingchen; Muljadi, Eduard

doi:10.1109/tsg.2016.2552229

Cited by 49 publications

(24 citation statements)

References 35 publications

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“…C10. SG failures fault status detection [41], [46], [61], [62], [126], [127], [142], [176], fault type classification [197], power distribution reliability [149], [195] As it can be seen, there is large variability in the aspects covered by the research. Themes that are covered by more articles are consumption prediction (69 papers), load profile clustering (19), forecast renewable power sources (19), false data injection attacks (14), consumption clustering (12), power quality disturbances classification (11), and power data compression (11).…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…Also, phasor measurement units can be used to determine the response time after the current exceeds its safe limit which helps to detect the fault in the SGS. Yet, the phasor measurement unit could not provide enough information about the power failure in the system [40] [41]. Furthermore, the Petri Net method is used to detect a power failure in the SGS.…”

Section: Techniques To Detect Different Faults In the Smart Grid Systemmentioning

confidence: 99%

Review of Fault Types, Impacts, and Management Solutions in Smart Grid Systems

Mousa¹,

Abdelwahed²,

Klüss³

2019

SGRE

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Fault management study in smart grid systems (SGSs) is important to ensure the stability of the system. Also, it is important to know the major types of power failures for the effective operation of the SGS. This paper reviews diverse types of faults that might appear in the SGS and gives a survey about the impact of renewable energy resources (RERs) on the behavior of the system. Moreover, this paper offers different fault detection and localization techniques that can be used for SGSs. Furthermore, a potential fault management case study is proposed in this paper. The SGS model in this paper is investigated using both of the Matlab/Simulink and the Real Time Digital Simulation (RTDS) to compute the fault management study. Simulation results show the fast response to a power failure in the system which improves the stability of the SGS.

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“…Several SA topics are discussed as well. We highlight anomaly detection and classification [25,26], social media such as Twitter in [27], the estimation of active ingredients such as PV installations [28,29] and finally the real-time data for online transient stability evaluation [30]. In addition, we point out researchs about the improvement in wide-area monitoring, protection and control (WAMPAC) and the utilization of PMU data [31][32][33][34], together with the fault detection and location [35][36][37].…”

Section: B the Role Of Data In Future Power Gridmentioning

confidence: 99%

Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory

Mouftah

Erol‐Kantarci²,

Rehmani³

2018

Transportation and Power Grid in Smart Cities

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A cornerstone of the smart grid is the advanced monitorability on its assets and operations. Increasingly pervasive installation of the phasor measurement units (PMUs) allows the so-called synchrophasor measurements to be taken roughly 100 times faster than the legacy supervisory control and data acquisition (SCADA) measurements, time-stamped using the global positioning system (GPS) signals to capture the grid dynamics. On the other hand, the availability of low-latency two-way communication networks will pave the way to high-precision real-time grid state estimation and detection, remedial actions upon network instability, and accurate risk analysis and post-event assessment for failure prevention.In this chapter, we firstly modelling spatio-temporal PMU data in large scale grids as random matrix sequences. Secondly, some basic principles of random matrix theory (RMT), such as asymptotic spectrum laws, transforms, convergence rate and free probability, are introduced briefly in order to the better understanding and application of RMT technologies. Lastly, the case studies based on synthetic data and real data are developed to evaluate the performance of the RMT-based schemes in different application scenarios (i.e., state evaluation and situation awareness).

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Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

Cited by 49 publications

References 35 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Review of Fault Types, Impacts, and Management Solutions in Smart Grid Systems

Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory

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