Addressing the significant interference in the filtering accuracy of deep learning-based state estimation models caused by strong non-Gaussian noise in distribution network measurement information,an enhanced kernel ridge regression state estimation method based on ensemble empirical mode decomposition is proposed.Firstly,the ensemble empirical mode decomposition is utilized to filter out most of the noise data in the measurement information,ensuring the reliability of data for subsequent filtering.Subsequently,an enhanced kernel ridge regression state estimation model is constructed to establish a mapping relationship between measurement information and estimation residuals.By inputting the measurement information,the estimation results and estimation residuals can be obtained.Finally,numerical simulations are conducted on the standard IEEE 33-node system and a city-level 78-node system,demonstrating that the proposed method exhibits high accuracy and robustness under the interference of strong non-Gaussian noise.
关键词
配电系统/状态估计/核岭回归/非高斯噪声/集合经验模态分解
Key words
distribution system/state estimation/kernel ridge regression/non-gaussian noise/ensemble empirical mode decomposition
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