Transient Stability Assessment of Power System based on ISSA-ELM

10.6919/ICJE.202205_8(5).0041

Xiuci Li；Jianmei Xiao

Transient Stability Assessment ； Sparrow Algorithm ； Extreme Learning Machine ； Rough Set

International Core Journal of Engineering

8卷5期（2022 / 05 / 01）

327 - 337

英文

One of the prerequisites for the stable operation of the power system is to ensure the transient stability of the power system. At present, many intelligent algorithms are applied to the transient stability assessment of power systems, but there are still some problems, such as poor effectiveness and low accuracy due to huge data. Aiming at these problems, this paper uses the information entropy-based rough set for attribute dimensionality reduction, filters unnecessary attributes, and obtains a simplified data set. Since the prediction accuracy of the traditional extreme learning machine is not very high, this paper adopts the improved sparrow algorithm to optimize the extreme learning machine, and obtains a high accuracy. Finally, the IEEE39 system simulation results show that the method proposed in this paper can effectively reduce the data dimension, and can quickly and accurately discriminate the transient and stable state of the power system.

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