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In this work, ZnLaAl layered double hydroxides (LDHs) were prepared by the co-precipitation method, and the ZnLaAl-LDHs nanosheets were embedded in sol-gel coating for the corrosion protection of 6061 aluminum alloys. The structure, morphology, and long-term anti-corrosion performance of sol-gel coating modified with ZnLaAl-LDHs were investigated. The structure and morphology analysis showed that nanosheets of ZnLaAl-LDHs are finer than those of ZnAl-LDHs, with the results suggesting that the La can refine the size of LDHs’ nanosheets and improve their nucleation rate. The results of long-term corrosion tests showed that the sol-gel coating with ZnLaAl-LDHs exhibits higher corrosion resistance and better stability compared with the sol-gel coating with ZnAl-LDHs, which indicates that the addition of La enhances the anti-corrosion performance of the LDHs and improves the stability of sol-gel coating with LDHs. Finally, the formation mechanism of ZnLaAl-LDHs and the corrosion mechanism of sol-gel coating with ZnLaAl-LDHs on 6061 aluminum alloys are both discussed in detail.

Partial discharge phenomenon of overhead lines in distribution network is usually caused by the concentration of local electric field inside or on the surface of electrical equipment. According to partial discharge problem, based on the characteristics of the project the use of a machine learning is proposed for distribution network overhead line partial discharge detection model, first using the characteristics of engineering to extract the signal characteristics of different sides characterization, then respectively using K neighbor algorithm and back propagation algorithm, support vector machine (SVM) classification algorithm test. Experimental results show that when machine learning algorithm is used to classify time domain characteristic signals based on the feature engineering selected in this paper, k-nearest Neighbor algorithm has better classification and recognition effect than back propagation algorithm and support vector machine algorithm, with accuracy rate of 97.20%, recall rate of 96.30% and F value of 96.73%. In the frequency domain feature recognition and classification, the k-nearest Neighbor algorithm has 98.95% accuracy, 99.42% recall rate and 97.61% F value. Compared with the back propagation algorithm and support vector machine algorithm, the K-nearest Neighbor algorithm has the highest detection accuracy in the frequency domain feature detection of partial discharge on overhead lines of distribution network.