Next Article in Journal
On the Application of Small-Scale Turbines in Industrial Steam Networks
Next Article in Special Issue
Determination of Maximum Acceptable Standing Phase Angle across Open Circuit Breaker as an Optimisation Task
Previous Article in Journal
A Component-Sizing Methodology for a Hybrid Electric Vehicle Using an Optimization Algorithm
Article

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

Department of Power Engineering, University of Split, FESB, 21000 Split, Croatia
*
Author to whom correspondence should be addressed.
Presented at the 5th International Conference on Smart and Sustainable Technologies, Split, Croatia, 23–26 September 2020.
Academic Editors: Ying-Yi Hong and Adolfo Dannier
Energies 2021, 14(11), 3148; https://doi.org/10.3390/en14113148
Received: 10 March 2021 / Revised: 15 April 2021 / Accepted: 26 May 2021 / Published: 27 May 2021
(This article belongs to the Special Issue Machine Learning in Power System Dynamic Security Assessment)
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. View Full-Text
Keywords: power system stability; transient stability assessment; transient stability index; machine learning; deep learning; autoencoder; transfer learning; ensemble; dataset power system stability; transient stability assessment; transient stability index; machine learning; deep learning; autoencoder; transfer learning; ensemble; dataset
Show Figures

Figure 1

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.4521886
    Link: https://www.zenodo.org/record/4521886
    Description: Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case
MDPI and ACS Style

Sarajcev, P.; Kunac, A.; Petrovic, G.; Despalatovic, M. Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble. Energies 2021, 14, 3148. https://doi.org/10.3390/en14113148

AMA Style

Sarajcev P, Kunac A, Petrovic G, Despalatovic M. Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble. Energies. 2021; 14(11):3148. https://doi.org/10.3390/en14113148

Chicago/Turabian Style

Sarajcev, Petar, Antonijo Kunac, Goran Petrovic, and Marin Despalatovic. 2021. "Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble" Energies 14, no. 11: 3148. https://doi.org/10.3390/en14113148

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop