Application of Artificial Intelligence for Modeling of Power Systems

人工智能在电力系统建模中的应用

• 批准号: 09650328
• 负责人: HIYAMA Takashi
• 金额: $ 1.79万
• 依托单位: Kumamoto University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1997
• 资助国家: 日本
• 起止时间: 1997 至 1998
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09650328/
• 关键词: Neural Networks; Nonlinear Systems; Governor-Turbine System; Stability Evaluation; Nose Curve; Dynamic Load Modeling; ニュートラルネットワーク; 安定度評価; 負荷モデル

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.

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。基于实测数据，利用人工神经网络对研究系统进行建模。所提出的人工神经网络是多层的，从输出层到输入层附加有时滞的反馈回路。利用所提出的人工神经网络，可以用较低阶的非线性差分方程对非线性系统进行较为精确的建模。将所提出的建模方法应用于500kV输变电线路负荷动力学、LNG机组调速器-汽轮机系统以及实际功率与系统电压之间的动力学建模。通过使用研究系统上的实际测量数据进行比较研究，证明了所提出模型的准确性。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干扰能给出相当精确的响应。此外，所提出的模型是鲁棒性的，因此，该模型在一定程度上适用于与实际测量数据不同的情况。将所提出的模型与常规模型相结合，可以进行更精确的稳定性分析。

项目成果

T.Hiyama, et.al.: "Artificial Neural Network Based Modeling of PV Dynamics on 500kV Transmission Line" Proceedings of IPEC'99 (International Power Engineering Conference). (to be published). (1999)

T.Hiyama 等人：“基于人工神经网络的 500kV 输电线路光伏动力学建模”IPEC99（国际电力工程会议）会议记录。

T.Hiyama, et.al.: "Artificial Neural Network Based Dynamic Load Modeling" IEEE Trans.on Power Systems. Vol.12, No.4. 1576-1583 (1997)

T.Hiyama 等人：“基于人工神经网络的动态负载建模”IEEE Trans.on 电力系统。

T.Hiyama,et.al: "Artificial Neural Network Based Modeling of PV Dynamics on 500kV Transmission Line" Proceedings of IPEC'99. =C37発表予定. (1999)

T. Hiyama 等人：“基于人工神经网络的 500kV 输电线路光伏动力学建模”IPEC99 论文集 = C37（1999 年）。

T.Hiyama, et al.: "Artificial Neural Network Based Modeling of PV Dynamics on 500kv Transmission Line" Proceedings of IPEC '99. 発表予定. (1999)

T. Hiyama 等人：“基于人工神经网络的 500kv 输电线路光伏动力学建模”IPEC 99 会议记录（1999 年）。

T.Hiyama, et.al.: "Artificial Neural Network Based Modeling of Governor-Turbine System" Proceedings of IEEE Power Engineering Society 1999 Winter Meeting. Vol.1. 129-133 (1999)

T.Hiyama 等人：“基于人工神经网络的调速器-涡轮机系统建模”IEEE 电力工程学会 1999 年冬季会议论文集。