Control Center Implementation of Advanced Optimization and Decision Support Applications

Sass, Florian; Sennewald, Tom; Brosinsky, Christoph; Westermann, Dirk; Mangold, Martin; Heyde, Chris; Becher, Silvio; Krebs, R.

doi:10.1109/sest.2018.8495658

Cited by 3 publications

(2 citation statements)

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“…After the feature extraction phase, z-scores z k are computed for each feature using the actual feature value x k F,N , the mean and the standard deviation of all past N features of the current measurement channel x k F . The z-score vector z contains the z-scores from all feature values-see (5) and (6).…”

Section: Disturbance Detectionmentioning

confidence: 99%

“…To address these challenges, synchronized phasor measurements and wide area monitoring (WAM) systems are deployed worldwide in power system control rooms [4]. Being a valuable resource to observe and understand the dynamics of power systems, they additionally enable a new quality of operator decision support functions, assistant systems and automated control [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems

et al. 2020

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Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level.

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Section: Disturbance Detectionmentioning

confidence: 99%