Power system inter-area oscillations curtail the power transferring capabilities of the transmission lines in a large interconnected power system. Accurate identification of dominant modes and associated contributing ...
详细信息
Power system inter-area oscillations curtail the power transferring capabilities of the transmission lines in a large interconnected power system. Accurate identification of dominant modes and associated contributing generators is important to avoid power system failures by taking appropriate remedial measures. This paper proposes a multi-channel Improved dynamicmodedecomposition (IDMD) algorithm-based modal analysis technique using Synchrophasors measurement. First, a reduced-order dynamic power system model is estimated and using this model dominant oscillation modes, corresponding modes shapes, damping ratio, coherent group of generators, participation factors are determined. To improve the accuracy data stacking technique is used to capture detailed information of the system. An optimal hard threshold technique is utilized to select the most optimal model order to avoid uncertainties due to the presence of high level of measurement noise. The study results show that the proposed algorithm gives an accurate and robust solution even in systems having high level of noise in the measurement data. The performance of the proposed technique is tested on simulated data from two-area four-machine system and wNAPS 41-bus 16-generator system with PMU measurements corrupted with different levels of measurement noise. To further strengthen the viewpoint, the proposed method is validated on real-time PMU measurement from ISO New England data to validate the accuracy of the proposed work.
Because strip cross sections cannot be obtained during hot rolling in advance, traditional automatic shape control systems can only rely on the measured shape at the exit of the final mill for feedback control, which ...
详细信息
Because strip cross sections cannot be obtained during hot rolling in advance, traditional automatic shape control systems can only rely on the measured shape at the exit of the final mill for feedback control, which causes a significant lag and poor adjustment effect. To accurately predict the cross-sectional shape, an industrial Internet of things platform for steel plants is developed to collect real-time production data. A novel real-time prediction model that can determine the cross sections of strips is proposed to address the drawbacks of traditional data-driven methods that perform offline predictions. This model is established by adopting a dynamic mode decomposition algorithm (DMD) to optimize the sparse identification of nonlinear dynamics (SINDy). A practical dataset of 81 variables from a 2250-mm hot-rolling production line is utilized to validate the proposed method, and this method is compared with SINDy, EMD-optimized SINDy, and VMD-optimized SINDy. The experimental results show that the proposed method can achieve higher prediction accuracy and more minor errors.
暂无评论