| 引用本文: | 黄蔓云,王天昊,卫志农,孙国强.基于长短期记忆网络的UKF动态谐波状态估计[J].电力系统保护与控制,2022,50(11):1-11.[点击复制] |
| HUANG Manyun,WANG Tianhao,WEI Zhinong,SUN Guoqiang.Dynamic harmonic state estimation of an unscented Kalman filter based on longshort-term memory neural networks[J].Power System Protection and Control,2022,50(11):1-11[点击复制] |
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| 摘要: |
| 传统动态谐波状态估计的卡尔曼滤波预测步通常以单位阵构建状态空间模型，同时将系统噪声协方差矩阵假设为常数阵，从而导致动态估计预测精度降低，影响动态状态估计模型的滤波性能。为了准确建立谐波状态的空间模型，提出一种基于长短期记忆网络(Long Short-Term Memory, LSTM)的时序预测方法。通过大量历史数据离线训练模拟复杂的状态转移过程，基于历史时刻的滤波估计值预测当前时刻的谐波状态量，有效提高无迹卡尔曼滤波(Unscented Kalman Filter, UKF)中预测模型精度。在改进IEEE34节点三相不平衡系统上进行了测试分析。与传统算法进行对比，结果证明所提出的方法在谐波状态估计精度和鲁棒性方面均表现更好。 |
| 关键词: 动态谐波状态估计 无迹卡尔曼滤波 长短期记忆网络 预测模型 鲁棒性 |
| DOI：DOI: 10.19783/j.cnki.pspc.211221 |
| 投稿时间：2021-09-03修订日期：2021-12-13 |
| 基金项目:国家自然科学基金项目资助(U1966205)；中央高校基本科研业务费专项资金项目资助(B200201067) |
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| Dynamic harmonic state estimation of an unscented Kalman filter based on longshort-term memory neural networks |
| HUANG Manyun,WANG Tianhao,WEI Zhinong,SUN Guoqiang |
| (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China) |
| Abstract: |
| The Kalman filter prediction step of traditional dynamic harmonic state estimation usually constructs the state space model with a unit matrix and assumes the system noise covariance matrix as a constant matrix. This reduces the accuracy of the estimation and affects the results of the dynamic state estimation model. In order to establish the spatial model of harmonic state accurately, this paper proposes a time series prediction method based on a long short-term memory network. The complex state transfer process is simulated by off-line training of a large number of historical data, and the harmonic state at the present moment is predicted based on the filtering estimation of historical moments. This effectively improves the accuracy of the prediction model in an unscented Kalman filter. The method in this paper is tested and analyzed on the improved IEEE 34-node three-phase unbalanced system. Compared with the traditional method, the results show that the proposed method performs better in both precision and robustness of harmonic state estimation.
This work is supported by the National Natural Science Foundation of China (No. U1966205). |
| Key words: dynamic harmonic state estimation unscented Kalman filter long short-term memory neural networks prediction model robustness |
