Intelligent wide area monitoring of power system oscillatory dynamics in real time

Gupta, Abhilash Kumar; Verma, Kusum; Niazi, K. R.

doi:10.1109/icaccs.2017.8014574

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Recently, due to the success of deep neural networks in other applications, many researchers tackled the dynamic security assessment problem with such an approach. Classical Feed-Forward Neural Networks (FFNNs) are used as classifiers to predict the small-signal stability [61], oscillatory stability [62] or transient stability [63]; or as regressors to predict the CCT or damping ratio [22], the CCT and final value of rotor angle after a fault [64], the load stability margin [65] or a voltage stability index [66]. In [67], a MapReduce algorithm is used to parallelise the learning of several networks and predict both stability status and the transient stability index.…”

Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…Wide-area measurement systems (WAMSs) have been frequently used in power systems. The measured synchrophasor data can be used for both real-time applications such as dynamic monitoring, oscillation source location, smart control, and wide-area protection [1][2][3][4][5], and off-line applications such as post-event analysis [6]. With the rapid development of interconnected power systems, renewable energy sources, and high-voltage direct current (HVDC) transmission systems, oscillation problems such as lowfrequency oscillations (LFOs) and subsynchronous oscillations (SSOs) are drawing more and more attention [7][8][9][10], which may threaten power system security and stability.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based data compression for wide-area measurement data of oscillations

Cheng

Zhang

et al. 2018

J. Mod. Power Syst. Clean Energy

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This paper proposes a wavelet-based data compression method to compress the recorded data of oscillations in power systems for wide-area measurement systems. Actual recorded oscillations and simulated oscillations are compressed and reconstructed by the waveletbased data compression method to select the best wavelet functions and decomposition scales according to the criterion of the minimum compression distortion composite index, for a balanced consideration of compression performance and reconstruction accuracy. Based on the selections, the relationship between the oscillation frequency and the corresponding optimal wavelet and scale is discussed, and a piecewise linear model of the base-2 logarithm of the frequency and the order of the wavelet is developed, in which different pieces represent different scales. As a result, the wavelet function and decomposition scale can be selected according to the oscillation frequency. Compared with the wavelet-based data compression method with a fixed wavelet scale for disturbance signals and the real-time data compression method based on exception compression and swing door trending for oscillations, the proposed method can provide high compression ratios and low distortion rates.

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Modelling & Steady State Compliance Testing of an Improved Time Synchronized Phasor Measurement Unit Based on IEEE Standard C37.118.1

Trivedi¹

2018

2018 8th IEEE India International Conference on Power Electronics (IICPE)

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Intelligent wide area monitoring of power system oscillatory dynamics in real time

Cited by 3 publications

References 25 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Wavelet-based data compression for wide-area measurement data of oscillations

Modelling & Steady State Compliance Testing of an Improved Time Synchronized Phasor Measurement Unit Based on IEEE Standard C37.118.1

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