To address the issue of inaccurate sediment thickness identification in the existing ultrasonic echo detection method for grading electrodes sediments, an intelligent identification method of grading electrodes sediments based on SVMD-CPO-RF-AdaBoost combining multiscale entropies is proposed. The successive variational mode decomposition method is used to decompose the ultrasonic echo signals, and effective signal components are selected and reconstructed based on the modal selection method to achieve signal denoising. Multiscale permutation entropy and multiscale dispersion entropy are introduced as feature extraction methods to extract the multiscale features of ultrasonic echo signals from grading electrodes with different sediments thicknesses. A dual ensemble learning model of RF-AdaBoost, with random forest as the weak classifier of AdaBoost, is constructed and optimized by the crested porcupine optimizer to achieve intelligent identification of sediment thicknesses. A comparative study was conducted on different multiscale entropies, as well as six models including CPO-RF, CPO-AdaBoost, and CPO-SVM. Experimental results show that the recognition accuracy of sediment thicknesses using a combination of MPE and MDE features is superior to that using a single feature. The proposed method can accurately identify the sediment thickness of the grading electrodes sediments in the range of 0 to 0.8 mm, with a recognition accuracy of 94.50%. Moreover, its precision, recall, and F1 score outperform those of other methods, providing an intelligent identification solution for grading electrodes sediments in ultra-high voltage converter stations.