| 曹睿康,李岩松,耿聪,刘逸伦,刘君.基于径向基Koopman-Kalman的光学电流传感器误差预测[J].电子测量与仪器学报,2025,39(5):84-94 |
| 基于径向基Koopman-Kalman的光学电流传感器误差预测 |
| Optical current sensor error prediction based on radial basis Koopman-Kalman |
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| DOI： |
| 中文关键词: 光学电流传感器 径向基函数 Koopman算子 卡尔曼滤波 实时预测 |
| 英文关键词:optical current sensor radial basis function Koopman operator Kalman filter real-time forecasting |
| 基金项目:国家自然科学基金（51277066）项目资助 |
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| Author | Institution |
| Cao Ruikang | School of Electrical and Engineering, North China Electric Power University, Beijing 102206, China |
| Li Yansong | School of Electrical and Engineering, North China Electric Power University, Beijing 102206, China |
| Geng Cong | School of Electrical and Engineering, North China Electric Power University, Beijing 102206, China |
| Liu Yilun | School of Electrical and Engineering, North China Electric Power University, Beijing 102206, China |
| Liu Jun | School of Electrical and Engineering, North China Electric Power University, Beijing 102206, China |
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| 中文摘要: |
| 光学电流传感器(OCS)对温度的变化非常敏感，温度的变化导致其测量产生误差，难以达到电力系统计量的要求，因此准确预测由温度变化引起的OCS测量误差对监测其运行稳定性和保证电力系统的安全运行具有重要意义。由于OCS输出电流受温度的影响具有强非线性，提出了一种适用于非线性动力系统的径向基Koopman-Kalman预测算法，解决了温度影响下OCS输出电流因强非线性而难以预测的问题。首先通过径向基函数将非线性的OCS输出电流状态量映射至高维空间形成扩展状态，采用扩展动态模态分解算法分解扩展状态计算高维空间中Koopman算子的近似矩阵。其次，采用近似的Koopman算子在高维线性空间中进行批量预测。最后，采用Kalman滤波对批量预测的最后一个预测值更新校正，以跟随系统的状态变化。以实验测量得到的OCS温度-电流数据进行实验，结果表明在不同温度变化情况下，相较于标准Koopman预测和长短期记忆（LSTM）预测，所提出预测算法的均方误差（MSE）均减小90%以上，证明了所提算法的有效性。 |
| 英文摘要: |
| Optical current sensor (OCS) is very sensitive to temperature changes, and temperature changes lead to errors in its measurement, which makes it difficult to meet the requirements of power system metering. Therefore, accurate prediction of OCS measurement errors caused by temperature changes is of great significance for monitoring its operational stability and ensuring the safe operation of the power system. Since the OCS output current is strongly nonlinear due to the influence of temperature, this paper proposes a radial basis Koopman-Kalman prediction algorithm for nonlinear power systems, which solves the problem that the OCS output current is difficult to predict under the influence of temperature due to strong nonlinearity. Firstly, the nonlinear OCS output current state quantities are mapped into the high-dimensional space to form an extended state by the radial basis function (RBF), and the extended state is decomposed by the extended dynamic mode decomposition (EDMD) algorithm to calculate the approximate Koopman-Kalman algorithm in the high-dimensional space. Koopman operator approximation matrix. Secondly, the approximated Koopman operator is used for batch prediction in the high-dimensional linear space. Finally, Kalman filtering is used to update the correction to the last prediction of the batch prediction to follow the state change of the system. The OCS temperature-current data obtained from experimental measurements are used for experiments, and the results show that the mean square error （MSE） of the prediction algorithm proposed in this paper is reduced by more than 90% in comparison with both the standard Koopman prediction and the LSTM prediction for different temperature variations, which proves the effectiveness of the proposed algorithm. |
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