Visible to the public Biblio

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Yang, Jiahui, Yuan, Yao, Wang, Shuaibing, Bao, Lianwei, Wang, Ren.  2020.  No-load Switch-in Transient Process Simulation of 500kV Interface Transformer Used in HVDC Flexible. 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE). :1–4.
Interface transformer used in asynchronous networking was a kind of special transformer which's different from normal power transformer. During no-load switch-in, the magnitude of inrush current will be high, and the waveform distortion also be severity. Maybe the protections will be activated, even worse may lead the lockdown of the DC system. In this paper, field-circuit coupled finite element method was used for the study of transient characteristic of no-load switch-in, remanence simulation methods were presented. Quantitative analysis of the effect of closing making angle and core remanence on inrush current peak value, meanwhile, the distribution of magnetic field inside the tank during the transient process. The result indicated that the closing making angle and core remanence have obvious effect on inrush current peak value. The research results of this paper can be used to guide the formulation of no-load switch-in strategy of interface transformer, which was of great significance to ensure the smooth operation of HVDC Flexible system.
Wang, Meng, Chow, Joe H., Hao, Yingshuai, Zhang, Shuai, Li, Wenting, Wang, Ren, Gao, Pengzhi, Lackner, Christopher, Farantatos, Evangelos, Patel, Mahendra.  2019.  A Low-Rank Framework of PMU Data Recovery and Event Identification. 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA). :1–9.

The large amounts of synchrophasor data obtained by Phasor Measurement Units (PMUs) provide dynamic visibility into power systems. Extracting reliable information from the data can enhance power system situational awareness. The data quality often suffers from data losses, bad data, and cyber data attacks. Data privacy is also an increasing concern. In this paper, we discuss our recently proposed framework of data recovery, error correction, data privacy enhancement, and event identification methods by exploiting the intrinsic low-dimensional structures in the high-dimensional spatial-temporal blocks of PMU data. Our data-driven approaches are computationally efficient with provable analytical guarantees. The data recovery method can recover the ground-truth data even if simultaneous and consecutive data losses and errors happen across all PMU channels for some time. We can identify PMU channels that are under false data injection attacks by locating abnormal dynamics in the data. The data recovery method for the operator can extract the information accurately by collectively processing the privacy-preserving data from many PMUs. A cyber intruder with access to partial measurements cannot recover the data correctly even using the same approach. A real-time event identification method is also proposed, based on the new idea of characterizing an event by the low-dimensional subspace spanned by the dominant singular vectors of the data matrix.