Data-driven Research Method For Power System Stability Detection

Tianxia, Jia; Gu, Zhaoliang; Sun, Haoyu; Gao, Pengfei; Jiang, Yi; Xu, Shengyuan; Zhao, Bing

doi:10.1109/powercon.2018.8601601

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…A data-fitting method for rapid STVI evaluation was presented in [39]. The authors in [40] summarized many data-driven methods, some of which are applicable for the issue of short-term instability detection. Finally, in [41], a STVI assessment method that can cope with the missing PMU data is proposed.…”

Section: Data-driven Methodsmentioning

confidence: 99%

Comprehensive Review of Short-Term Voltage Stability Evaluation Methods in Modern Power Systems

2021

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The possibility to monitor and evaluate power system stability in real-time is in growing demand. Whilst most stability-related studies focus on long-term voltage stability and frequency stability, very little attention is given to the issue of short-term (voltage) instability. In this paper, the most common evaluation methods present in the literature are summarized, with a focus on their applicability to modern power systems with a large amount of renewable energy integration. The paper presents a first-of-a-kind structured review of this topic. We find that all existing methods have noteworthy limitations that necessitate further improvements. Additionally, the need of having an inclusive short-term instability prediction method is demonstrated, due to strong interactions between various short-term instability mechanisms. These findings provide a good foundation for further research and advancement in the field of real-time stability monitoring.

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Section: Data-driven Methodsmentioning

confidence: 99%

Comprehensive Review of Short-Term Voltage Stability Evaluation Methods in Modern Power Systems

2021

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“…With the development of supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) networks, the precision of a resolution of the measured data has increased from 1/3600 Hz up to 100 Hz, significantly increasing the volume of generated data [4]. This growth on the measured information has led to the development of simplified models with less physical meaning but more accurate representation of actual data, normally known as data-driven models [5][6][7]. Power system data-driven models have captured particular attention in terms of modeling diverse elements of the grid, such as generators, power electronics devices, and loads to improve the accuracy of physically driven models [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven, Web-Based Parameter Identification for a Reduced-Order Model of the Chilean Power System

et al. 2022

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The current need for power-system control-room operational tools to increase real-time situational awareness has led to various proposals on data analytics algorithms to obtain operational information from SCADA and PMU power system networks. Most of the proposals are implemented in simulation, with reduced practical significance for system operators. This work proposes a web-based simple algorithm to identify the dynamic parameters of a reduced-order model of power system frequency dynamics that is implemented with industrial hardware on a PMU network in the Chilean power system. To demonstrate the applicability of the proposed tool, the algorithm is implemented in a web page with real-time monitoring of the frequency of the Chilean power system, so that utilities and academic institutions with access to PMU measurements can apply the proposed concepts regardless of the topology of the monitored system.

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