Abstract
This paper proposes a novel continuous wavelet transform (CWT) based approach to holistically estimate the dominant oscillation using measurement data from multiple channels. CWT has been demonstrated to be effective in estimating power system oscillation modes. Using singular value decomposition (SVD) technique, the original huge phasor measurement unit (PMU) datasets are compressed to finite useful measurement data which contain critical dominant oscillation information. Further, CWT is performed on the constructed measurement signals to form wavelet coefficient matrix (WCM) at the same dilation. Then, SVD is employed to decompose the WCMs to obtain the maximum singular value and its right eigenvector. A singular value vector with the entire dilation is constructed through the maximum singular values. The right eigenvector corresponding to the maximum singular value in the singular-value vector is adopted as the input of CWT to estimate the dominant modes. Finally, the proposed approach is evaluated using the simulation data from China Southern Power Grid (CSG) as well as the actual field-measurement data retrieved from the PMUs of CSG. The simulation results demonstrate that the proposed approach performs well to holistically estimate the dominant oscillation modes in bulk power systems.
Highlights
Small signal stability is an ability of power system to maintain its synchronism when subjected to small disturbances [1,2,3,4]
At a given operating point, nonlinear differential algebraic equations (DAEs) of the system can be linearized by modal analysis
To suppress the noises in the mode estimation from ringdown data, a mode matching method based on subspace methods was developed in [8] to analyze the small signal stability of China Southern Power Grid (CSG) using phasor measurement unit (PMU) data, but the performance of this method depended on the operational experience
Summary
Small signal stability is an ability of power system to maintain its synchronism when subjected to small disturbances [1,2,3,4]. To suppress the noises in the mode estimation from ringdown data, a mode matching method based on subspace methods was developed in [8] to analyze the small signal stability of China Southern Power Grid (CSG) using PMU data, but the performance of this method depended on the operational experience It cannot track the dominant modes when the operating point of the system changes. This inconsistency may prevent system operators from taking timely actions to maintain system dynamic stability, which may further lead to an outage Motivated by these existing issues, this paper proposes a multi-channel CWT-based (MCWT) mode estimation approach. The data compression technique consists of two parts: the first part based on SVD is responsible for decomposing the covariance matrix generated by the multi-channel measurement signals; the second part is to construct measurement signal using the results of SVD with a proposed model order determination method.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
