基于深度学习的电力系统暂态稳定性评估方法研究

Research on Transient State Qualitative Estimating Method of the Power System Based on Depth Study

可再生能源比例增加对电力系统安全稳定运行带来更加严峻的挑战。本文围绕基于深度学习的电力系统暂态稳定性评估问题进行探索研究,应用深层卷积神经网络挖掘电力系统暂态运行数据与稳定性之间的关联关系,实现了基于暂态运行特征数据集的电力系统暂态稳定性快速评估,研究工作为电力系统暂态稳定性安全评估提供了新视角。通过算例验证,组合模型可实现电力系统暂态稳定性快速准确的评估,与其他算法相比,本文所建立的组合模型在电力系统暂态稳定性评估准确性方面更具优势。

The paper carriesout discussion and study on transient state qualitative estimating problem of power systems. Apply relevant relations between the digging power system's tranisent operation data of deep convolution nerve net and stability, realize quick estimation of the power system transient stability based on transient operation characteristic data assemble. The quick and accurate estimation for the power system tran sient stability is realized by example and integrated meodel. Comparing with other algorithm, the integrated model set up in the puper has more advantage in accurate aspect of the power system transient stability.