Browsing by Author "Wang, Qinling"

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    Anomaly Detection in Data-Driven Coherency Identification Using Cumulant Tensor
    Sun, Bo; Xu, Yijun; Wang, Qinling; Lu, Shuai; Yu, Ruizhi; Gu, Wei; Mili, Lamine M. (IEEE, 2023-12-04)
    As a model reduction tool, coherency identification has been extensively investigated by power researchers using various model-driven and data-driven approaches. Model-driven approaches typically lose their accuracy due to linear assumptions and parameter uncertainties, while data-driven approaches inevitably suffer from bad data issues. To overcome these weaknesses, we propose a data-driven cumulant tensor-based approach that can identify coherent generators and detect anomalies simultaneously. More specifically, it converts the angular velocities of generators into a fourth-order cumulant tensor that can be decomposed to reflect the coherent generators. Also, using co-kurtosis in the cumulant tensor, anomalies can be detected as well. The simulations reveal its excellent performance.
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    A Data-Driven Koopman Approach for Power System Nonlinear Dynamic Observability Analysis
    Xu, Yijun; Wang, Qinling; Mili, Lamine M.; Zheng, Zongsheng; Gu, Wei; Lu, Shuai; Wu, Zhi (IEEE, 2023-08-15)
    A prerequisite to dynamic state estimation of a stochastic nonlinear dynamic model of a power system is its observability analysis. However, due to the model nonlinearity, the traditional methods either suffer from a poor estimation accuracy if a linear approximation is performed or yield an extremely complicated procedure if the Lie-derivative method is applied to a large-scale power system. To address these weaknesses, we propose a new data-driven Koopman-based observability method by revealing the link that exists between the Koopman operator and the Lie-derivative in the Koopman canonical coordinates. This enables the proposed data-driven method not only to be fully derivative-free, which alleviates its implementation complexity but also overcomes the model nonlinearity and inaccuracy of the system. Furthermore, as an important byproduct, the proposed observability analysis scheme provides a valuable guide for the selection of the observables of the Koopman operator, which is a major difficulty for the application of this operator. Finally, we demonstrate the excellent performance of the proposed method on several IEEE standard test systems.