Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition Based Distribution Network State Estimation

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.