Application of Artificial Intelligence for Modeling of Power Systems

本课题提出了一种基于人工智能特别是人工神经网络的电力系统建模新方法。基于真实的测量数据，采用人工神经网络对学习系统进行建模。所提出的人工神经网络是一种多层网络，在输出层到输入层之间增加了具有时延的反馈回路。通过使用所提出的人工神经网络，非线性系统可以相当准确地建模与相对较低的阶非线性差分方程。该建模方法已应用于负荷动态、LNG热力机组调速器-汽轮机系统以及500 kV输电线路上真实的功率与系统电压之间的动态建模。所提出的模型的准确性已被证明通过比较研究，使用实际测量数据的研究系统。并与传统的线性模型进行了比较研究。建议的人工神经网络模型给出了相当准确的响应给定的干扰。此外，所提出的模型是稳健的，因此，该模型在一定程度上适用于不同的情况下，从一个实际的数据进行测量。通过将所提出的模型与常规模型相结合，将进行更准确的稳定性分析。

In this project, an artificial intelligence, especially artificial neural network, based new method has been proposed for the modeling of electric power systems. Study systems are modeled by using artificial neural networks based on themeasured real data. The proposed artificial neural networks are multi-layered ones with additional feedback loops from the output layer to the input layer with time delay. By using the proposed artificial neural networks, non-linear systems can be modeled quite accurately with relatively lower order non-linear difference equations. The proposed modeling method has been applied to the modeling of the load dynamics, the governor-turbine system foe a LNG thermal unit, and the dynamics between the real power and the system voltage on 500kV transmission lines. The accuracy of the proposed models have been demonstrated through comparison studies using actual measured data on the study systems. The comparison studies have also been performed between the proposed models and the conventional linear models. The proposed artificial neural network based models give quite accurate responses for given disturbances. In addition, the proposed models are robust ones, therefore, the models are available to some extent for the different situations from ones when the actual data were measured. By combining the proposed models with conventional models, more accurate stability analysis will be performed.