Ensemble Learning Approach to Power System Transient Stability Assessment

Kunac, Antonijo; Sarajčev, Petar

doi:10.23919/splitech49282.2020.9243849

Cited by 5 publications

(11 citation statements)

References 16 publications

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“…A fully automatic data processing pipeline follows (Section 2.2), with features engineering, statistical post-processing, stratified shuffle split and data scaling. Figure 2 presents a single-line diagram of the IEEE New England 39-bus test case power system that serves as a benchmark for the TSA analysis [6,29,30,37,42]. Power system features a total of 10 synchronous machines of different nominal powers, a number of transmission lines (TLs), three-phase power transformers and loads.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…where t 0 is the moment of the fault initiation (1 s), f is the frequency and ψ is the phase angle [37]. Hence, faults are initiated after the power system steady state has been attained.…”

Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

“…); Refs. [5,6,11,16,34,37]. Features that are used for the ML are extracted from these time-domain signals at two distinct time instants: (a) pickup time (pre-fault value) and (b) trip time (post-fault value) of the associated distance protection relays.…”

Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

“…Table 1 presents select features, extracted from the time-domain signals, for the New England 39-bus test case power system [6,11,37,45]. All of the features are directly measured.…”

Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

“…Baltas et al in [36] propose boosting and voting ensembles for the TSA classification problem. Another approach to ensemble multiple models, using the soft voting, is proposed in [37] for the same type of problem.…”

Section: Introductionmentioning

confidence: 99%

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Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

et al. 2021

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Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.

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Section: Dataset Preparationmentioning

confidence: 99%

Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

“…Table 1 presents select features, extracted from the time-domain signals, for the New England 39-bus test case power system [6,11,37,45]. All of the features are directly measured.…”

Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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