Estimation of dominant power oscillation modes based on ConvLSTM approach using synchrophasor data and cross-validation technique

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dc.contributor.authorSenesoulin, Fanta
dc.contributor.authorNgamroo, Issarachai
dc.contributor.authorDechanupaprittha, Sanchai
dc.date.accessioned2026-08-06T10:37:51Z
dc.date.available2026-08-06T10:37:51Z
dc.date.issued2022-09-01
dc.description.abstractThis paper proposes a deep neural network approach considering performance-based cross-validation and confidence interval analysis to estimate a power system's dominant power oscillation modes. Due to increased electricity demands, power utilities implement various generation sources in their power systems. Accordingly, a modern power system is increasingly complex as a multi-area and multi-machine power system. The electromechanical oscillation modes arise inevitably. Moreover, a major-unexpected event could excite weakly damped power oscillation modes and cause power system instability. The estimation of dominant power oscillation modes is significant for power system monitoring and control. A fast computing time of such modes estimation is essential for further actions. This paper applies the convolutional long short-term memory 2-dimension (ConvLSTM2D) approach to estimate dominant oscillation modes based on synchrophasor data. The proposed ConvLSTM2D approach provides precise estimation with a great opportunity to avoid a forced power system outage. The simulation results of the ConvLSTM2D approach show better accuracy of the dominant power oscillation modes estimated in comparison with the state-of-the-art algorithms (SOTA), i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and hybrid convolutional neural networks-long short-term memory (CNN-LSTM) algorithms. The proposed approach is a systematic approach that can be adaptively improved over time. In addition, the proposed approach can be further applied to wide-area monitoring considering the stability margin of a transmission system.
dc.identifier.citationSustainable Energy Grids and Networks, 31, 2022
dc.identifier.doi10.1016/j.segan.2022.100731
dc.identifier.issn23524677
dc.identifier.other2-s2.0-85129978987
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13444
dc.sourceSustainable Energy Grids and Networks
dc.subjectConfidence interval
dc.subjectCross-validation
dc.subjectDeep learning
dc.subjectPower oscillation modes
dc.subjectRenewable penetration
dc.subjectSynchrophasor data
dc.titleEstimation of dominant power oscillation modes based on ConvLSTM approach using synchrophasor data and cross-validation technique
dc.typeArticle

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