Abstract
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.
Petar Sarajčev
Full Professor | Department of Power Grids and Substations
Goran Petrović
Full Professor | Department of Electrical Measurements
Prof. dr. sc. Goran Petrović is a full professor at the Faculty of Electrical Engineering, Mechanical Engineering and Architecture in Split. His research interests include measurement of electrical and process quantities, analysis of geoelectrical and geothermal features of the soil, instrumentation for smart grids, measurement and application of synchrophasors. He is the author of numerous papers published in top-tier scientific journals and contributed to valuable international and national scientific projects.
Marin Despalatović
Full Professor | Department of Electrical Drives and Industrial Control
Full professor at the Faculty of Electrical Engineering, Mechanical Engineering, and Naval Architecture in Split, where he teaches courses Electric Machines, Electric Drive Systems, and Electromechanical System Modeling. His research focuses on power systems, energy storage, and smart grid technologies, with active participation in multiple national and international projects aimed at advancing energy infrastructure and improving system stability.