Decision trees using synchronized phasor measurements for wide-area response-based control

Gao, Qun; Rovnyak, S.M.

doi:10.1109/pes.2011.6039400

Cited by 12 publications

(33 citation statements)

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“…Analyzing data retrieved from DSA tools like TSAT [18] shows a gradual change in bus angles and hence a simple extrapolation methods like ZOH and FOH are found better for missing data recoveryin a response-based control scheme as in [1], [2].…”

Section: Discussionmentioning

confidence: 99%

“…Reference [10], [11] extends the method of using decision trees to trigger one-shot stabilizing control. In [1], the method of using DTs and wide area one shot controls are studied in a 176-bus model of Western Electrical Coordinating Council (WECC).…”

Section: Response Based System and One Shot Controlsmentioning

confidence: 99%

“…Subsections below discuss more about the power system model and methodology for one shot controls used in [1], [2] which are also the same methodologies used for this paper. Fig.…”

Section: Response Based System and One Shot Controlsmentioning

confidence: 99%

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Methods of Missing Data Handlingin One Shot Response based Power System Control

Dahal¹

2019

IJEAT

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The work done in this paper addresses various methods of handling missing phasor samples obtained from power flow simulations using DSA tools like TSAT and PSAT. Pseudorandom numbers in MATLAB are used to simulate 0-10% of missing samples and are recovered using different extrapolation techniques. After recovery, samples are subjected to decision trees to assess the performance of one shot stabilizing controls like in [1], [2].The power system model used is the 176 bus model of Western Electrical Coordinating Council (WECC).

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Section: Discussionmentioning

confidence: 99%

Section: Response Based System and One Shot Controlsmentioning

confidence: 99%

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Methods of Missing Data Handlingin One Shot Response based Power System Control

Dahal¹

2019

IJEAT

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“…Synchrophasor data quality problem has been addressed in [17]for data conditioning. Decision Trees are used to detect the loss of synchronism within PMU data in [18]. Using software platforms such as Real time Dynamic Monitoring System (RTDMS), a methodology has been proposed for assessing the static and dynamic stresses in large interconnected power grids [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Using phasor data for visualization and data mining in smart-grid applications

Mukherjee

Vallakati

Lachenaud

et al. 2015

2015 IEEE First International Conference on DC Microgrids (ICDCM)

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This paper presents a density based clustering (DBSCAN) technique to visualize and analyze the smart-grid data. The technique will aid in detecting bad-data, various fault types, deviation on frequency, voltage or current values for better situational awareness. Synchrophasors (or a PMU) is a sensor placed on a transmission line that tracks voltage, current, phase and frequency of the line. To improve situational awareness of the smart grid monitoring in real-time, the utility must monitor the phasor data measurement delivered by the sensors. Time-stamped synchronized measurements offer tremendous benefit for pre and post-event analysis. The paper uses data from openPDC framework to aid system operators in carrying various predictive analytics, decisions.

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“…Some promising results of response-based TSA by data mining have been reported in [10][11][12][13][14][15][16][17][18][19][20][21][22]. Decision trees (DTs) have the superiority of transparency [11] and do not rely on the backpropagation (BP) training process [12] in comparison with neural networks (NNs).…”

Section: Introductionmentioning

confidence: 99%

Non‐parametric statistics‐based predictor enabling online transient stability assessment

Liu

et al. 2018

IET Generation, Transmission & Distribution

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Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.

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Decision trees using synchronized phasor measurements for wide-area response-based control

Cited by 12 publications

References 0 publications

Methods of Missing Data Handlingin One Shot Response based Power System Control

Methods of Missing Data Handlingin One Shot Response based Power System Control

Using phasor data for visualization and data mining in smart-grid applications

Non‐parametric statistics‐based predictor enabling online transient stability assessment

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