基于深度神经网络-近似线性网络混合模型的电力系统状态估计方法
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TM71

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Power System State Estimation Method Based on DNN-PN Hybrid Model
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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    为提高电力系统实时状态估计的精度和计算效率,解决电网电压波动频发、潮流分布的不确定性剧增等问题,通过提出一种基于深度神经网络和近似线性网络模型的电力系统状态估计方法,研究了其在电网的应用。该方法将混合系统量测数据通过粒子滤波算法得到样本集,利用训练样本训练所提出的混合神经网络模型,最后将测试样本输入已建立的模型中获得系统状态的估计结果。通过IEEE118节点系统进行的负载数据仿真实验表明:基于混合神经网络模型的电力系统状态估计方法不仅能快速进行海量数据训练,还能有效避免过拟合;在实时状态估计的精度和计算效率方面相较于高斯-牛顿法均有提高。可见所提方法在电力系统实时状态估计方面具有一定的应用价值。

    Abstract:

    In order to improve the accuracy and computational efficiency of power system real-time state estimation, solve the problems of frequent, voltage fluctuations in grids and sharp increase in the uncertainty of power flow distribution in grids, a power system state estimation method based on Deep Neural Networks and approximately linear Proline Networks models were proposed, and its application in the power grids is researched. In this method, the mixed system measurement data was obtained through a particle filter algorithm to acquire a sample set, the training sample was used to train the proposed hybrid model, and finally the test samples were input into the established model to obtain the estimation result of the system state. Simulation result on the load data in IEEE118 bus system show that the power system state estimation method based on the proposed hybrid model not only allows rapid training for massive data, but also effectively avoids overfitting. The accuracy and computational efficiency of real-time state estimation compared with the Gauss-Newton method, are both improved. It can be seen that the proposed method has the application value in real-time state estimation of power systems.

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宋雨露,樊艳芳,刘雨佳,等. 基于深度神经网络-近似线性网络混合模型的电力系统状态估计方法[J]. 科学技术与工程, 2022, 22(25): 11041-11048.
Song Yulu, Fan Yanfang, Liu Yujia, et al. Power System State Estimation Method Based on DNN-PN Hybrid Model[J]. Science Technology and Engineering,2022,22(25):11041-11048.

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  • 收稿日期:2021-10-26
  • 最后修改日期:2022-09-01
  • 录用日期:2022-05-07
  • 在线发布日期: 2022-09-29
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