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。基于测量的实际数据，利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多层神经网络，从输出层到输入层具有附加的反馈环，具有时延。利用所提出的人工神经网络，可以用相对低阶的非线性差分方程组对非线性系统进行相当精确的建模。该建模方法已应用于500KV输电线路的负荷动态建模、LNG火电机组调速器-汽轮机系统动态建模以及有功与系统电压之间的动态建模。通过使用研究系统上的实际测量数据进行的比较研究，证明了所提出的模型的准确性。并与传统的线性模型进行了比较研究。所提出的基于人工神经网络的模型对给定的扰动给出了相当准确的响应。另外，所提出的模型具有较强的稳健性，因此对于不同于实际测量数据的情况，该模型具有一定的适用性。通过将所提出的模型与传统模型相结合，可以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.