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Steady State Stability Assessment Using Extreme Learning Machine Based on Modal Analysis

机译:基于模态分析的极限学习机稳态稳定性评估

摘要

The growth of electricity market led to increase utilization and higher loading of the electric transmission grids worldwide. This situation made power system operate close to steady-state stability limit (SSSL). It could trigger a voltage instability or a voltage collapse phenomenon. An assessment approach on steady state stability analysis was provided using Extreme Learning Machine taking the Modal Analysis as an assessment index. The nonlinear problem between voltage, power flow and participation factor in power system could be solved by Extreme Learning Machine. The method was tested on the IEEE 14 bus and Java-Bali system. The simulation results showed that the proposed method could accurately predict the weakest bus in power system.
机译:电力市场的增长导致全球电力传输网的利用率提高和负荷增加。这种情况使电力系统的运行接近稳态稳定极限(SSSL)。它可能会触发电压不稳定或电压崩溃的现象。提出了一种以模态分析为评估指标的极限学习机对稳态稳定性分析的评估方法。极限学习机可以解决电力系统中电压,潮流和参与因子之间的非线性问题。该方法已在IEEE 14总线和Java-Bali系统上进行了测试。仿真结果表明,该方法可以准确预测电力系统中最弱的母线。

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  • 作者

    Gunadin Indar Chaerah;

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  • 年度 2013
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