Publication type: Conference paper
Type of review: Peer review (publication)
Title: Convolutional neural network based approach for static security assessment of power systems
Authors: Ramirez Gonzalez, Miguel
Segundo Sevilla, Felix Rafael
Korba, Petr
et. al: No
Conference details: World Automation Congress 2021, virtual, 1-5 August 2021
Issue Date: 4-Aug-2021
Language: English
Subjects: Power system stability; Static security assessment; Convolutional neural network; Data-driven model; Deep learning
Subject (DDC): 006: Special computer methods
621.3: Electrical, communications, control engineering
Abstract: Steady-state response of the grid under a predefined set of credible contingencies is an important component of power system security assessment. With the growing complexity of electrical networks, fast and reliable methods and tools are required to effectively assist transmission grid operators in making decisions concerning system security procurement. In this regard, a Convolutional Neural Network (CNN) based approach to develop prediction models for static security assessment under N-1 contingency is investigated in this paper. The CNN model is trained and applied to classify the security status of a sample system according to given node voltage magnitudes, and active and reactive power injections at network buses. Considering a set of performance metrics, the superior performance of the CNN alternative is demonstrated by comparing the obtained results with a support vector machine classifier algorithm.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Energy Systems and Fluid Engineering (IEFE)
Appears in collections:Publikationen School of Engineering

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