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47 results on '"Li, Chongtao"'

6. Analysis on parameter switching methods for hydropower dominant system

Author

She Shengjing, Li Chongtao, Chen Yiping, Yang Rongzhao, Xiao Liang, Zeng Kehan, and Gao Qin

Subjects
Environmental sciences, GE1-350
Abstract

With the rapid development of DC asynchronous interconnection, in order to ensure the frequency quality, higher requirements are put forward for the primary frequency control of hydropower units. In the high proportion hydropower system, it is difficult for the governor parameters to meet the requirements of primary frequency control and dynamic stability simultaneously. In order to solve the problem, this paper proposes a recommended parameter switching method by referring to the Nordic system. The parameter optimization methods before and after switching are proposed, and the effectiveness of the method is verified by time domain simulation.

Published
2021
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11. An Exponential Transform Based Dual-Layer Krylov Subspace Method for Computing Unstable Modes of Power Systems

Author

Cao, Yulei, Li, Chongtao, Yan, Chenguang, He, Tingyi, and Li, Shengnan

Abstract

The detection and calculation of the rightmost modes are essential in the small signal stability assessment and control of power systems. This paper proposes an exponential transform based dual-layer Krylov subspace (DLKS) method for computing all unstable modes of power systems. Firstly, the exponential transform is introduced to map the unstable modes to the dominant modes outside the unit circle of the transformed spectrum. Then, based on the transformation, a Krylov decomposition is implemented to form the outer-layer Krylov subspace (OLKS) for solving the transformed dominant modes. In the process of forming the OLKS, the exponential matrix-vector multiplication (EMVM) needs to be solved. Next, combined with an accelerating convergence technique, the inner-layer Krylov subspace (ILKS) is formed to calculate the EMVM. Finally, four public source systems, namely the Xingo, Xingo6u, Juba5723 and Bauru5727 systems, are applied to verify the correctness and efficiency of the proposed method. Numerical experiments indicate that the proposed method can calculate all the unstable modes with high accuracy under the premise of ensuring calculation efficiency.

Published
2023
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12. A Quasi-Exponential Transform for Unstable Modes Calculation of Large-Scale Power Systems

Author

Cao, Yulei and Li, Chongtao

Abstract

The calculation of the unstable eigenvalues without omission is vital in small signal stability analysis of power systems. In this letter, a quasi-exponential transform is proposed to compute all unstable eigenvalues with improved efficiency and accuracy. Numerical experiments on two public source systems, namely the Xingo6u and Juba5723 systems, show the effectiveness of the proposed method.

Published
2023
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45. Nonlinear model reduction-based induction motor aggregation.

Author

Zhang, Gang, Du, Zhengchun, Ni, Yu., and Li, Chongtao

Subjects
INDUCTION motors, NONLINEAR statistical models, GENETIC algorithms
Abstract

This paper focuses on the problem of aggregating induction motors into a low-order equivalent model, which is very essential in large power system simulations. A new aggregating method is proposed based on nonlinear model reduction technique. The proposed method is developed directly from the detailed dynamic model of induction motors. By using empirical Gramians-based nonlinear model reduction techniques, a low-order load model, which can closely represent the dynamic characteristics of the detailed system, is obtained. The absence of drastic simplifications and the proper preservation of the input-output behavior of the original motors group allow the aggregated model to attain good accuracy. Two case studies are conducted to verify the validity of the proposed aggregating method. The simulation results show that this new method could attain high accuracy of simulation of all the individual induction motors under various operating conditions, especially when system is facing large disturbances. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2016
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