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• W4310344682 abstract "Constrained sensor placement is a significant problem relevant to various applications in power system monitoring. This paper proposes a general data-driven technique for simultaneously observing (reconstructing) the dominant system oscillatory behavior from a limited number of measurements and optimally selecting sensor locations when several sensors are fixed or pre-assigned. First, a novel framework is introduced based on the proper orthogonal decomposition (POD) technique and a greedy sampling algorithm to extract a low-dimensional subspace from a finite set of measurements. Based on this framework, a fast algorithm is developed to solve the sensor optimization problem on a reduced basis. This approach improves the ability of the POD technique to capture the most relevant features of the observed dynamics and projects the measured data onto a subspace of a few optimal coordinates. The proposed method is robust to measurement noise and can be used to identify the significant temporal and spatial scales in the observed system response. These features make the approach of particular interest for control applications, prediction, compression, and visualization of system oscillatory behavior. Simulation studies on two test systems show the applicability and effectiveness of the proposed method." @default.
• W4310344682 created "2022-12-09" @default.
• W4310344682 creator A5004857749 @default.
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• W4310344682 date "2023-03-01" @default.
• W4310344682 modified "2023-09-25" @default.
• W4310344682 title "Constrained sensor placement and state reconstruction in power systems from partial system observations" @default.
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• W4310344682 doi "https://doi.org/10.1016/j.ijepes.2022.108720" @default.
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