It is essential for monitoring and diagnosing defects of insulators to guarantee safe and stable operation of power systems. In view of the problem that the deterioration characteristic of the UV detection image of the power transmission and transformation insulator is unapparent and the diagnostic accuracy is low

we put forward a novel method for evaluating flashover grade of insulator ultraviolet image based on the sparse representation method. Firstly

a adaptive dictionary is constructed by a dictionary learning method. Then

the accelerated proximal gradient algorithm and the orthogonal matching pursuit algorithm were employed to solve the sparse solution

and the classification was diagnosed according to the nonzero term of the sparse matrix. The results show that the method has the advantages of high detection accuracy

up to 98%. The accuracy of the orthogonal matching pursuit algorithm depends on the completeness of the dictionary. When the training samples are sufficient

the algorithm recognition time is only 0.000 8 s. While the accelerated neighbor gradient algorithm selects multiple preferred parameters and is suitable for classification evaluation of smaller data sets. In addition

the sparse parameters are insensitive and robust. Compared with multi-classification support vector machine algorithm

the proposed method has a better performance and application prospect in UV layered pre-warning and fault detection of insulators.