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Real-Time Event Classification in Power System with Renewables Using Kernel Density Estimation and Deep Neural Network
, S. Raj, A.K. Pradhan
Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 10
Issue: 6
Pages: 6849 - 6859
Real-time classification of events facilitates corrective control strategies, supervisory protection schemes, and on-line transient stability assessment of a power system. The synchrophasor-based event classification techniques face challenges like similar responses for different classes of events, i.e., inter-class similarity (ICS), applicability to limited classes of events, and moderate real-time performance for a large power system. In addition, the enhanced ICS effect of increased renewable penetration on events classification needs to be addressed. This paper proposes a kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data. The proposed method uses a diffusion type kernel density estimator (DKDE) to characterize the shape of 3-D voltage and frequency distribution along time in terms of probability density functions (PDFs). That have distinct scale, shape, and orientation for different classes of events. Thereafter, a set of statistical features is derived from PDFs to train a multi-layered deep neural network for event classification. The proposed method is validated for renewables in IEEE-39 bus system and real transmission system of India grid using DIgSILENT/PowerFactory and also on a real phasor measurement unit data for India grid, where it showed better performance for ICS and renewable integration cases. © 2010-2012 IEEE.
About the journal
JournalData powered by TypesetIEEE Transactions on Smart Grid
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.