Application of ANN load model for transient stability analysis

Jong-Pil Lee, Dae-Jong Lee, Sung-Soo Kim, Jae-Woon Park, Jae-Yoon Lim, P. Ji
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引用次数: 1

Abstract

Precise load modeling is an essential factor in the simulation and evaluation of power system performance. However, conventional load modeling techniques have some limitations with respect to accuracy for nonlinear and composite loads. Thus, an intelligent load modeling method based on neural network and application technique for transient stability analysis are proposed in this research. ANN with Levenberg-Marquardt back-propagation learning rule was used for load modeling. The constructed ANN models are used to transient stability analysis. Voltage and frequency calculated by stability process are used for input of ANN load model and output of ANN load model are inputted to stability process. Validity of proposed method is verified through case studies.
神经网络负荷模型在暂态稳定分析中的应用
精确的负荷建模是电力系统性能仿真和评估的重要因素。然而,传统的载荷建模技术在非线性和复合载荷的精度方面存在一定的局限性。为此,本研究提出了一种基于神经网络的智能负荷建模方法和暂态稳定分析的应用技术。采用Levenberg-Marquardt反向传播学习规则的人工神经网络进行负载建模。将构建的人工神经网络模型用于暂态稳定分析。将稳定过程计算出的电压和频率作为人工神经网络负荷模型的输入,将人工神经网络负荷模型的输出输入稳定过程。通过实例验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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