In order to evaluate the health state of switchgear with the live detection data more objectively and accurately

we propose an evaluation method for health states based on the multi-dimensional features and optimal rank. Firstly

based on the fluctuating characteristics of partial discharge data

the index of fluctuating degree is introduced. Meanwhile

a comprehensive partial discharge multi-dimensional feature database is constructed to fully exploit the characteristics of partial discharge data. Then

the number of optimal rank K in the cluster algorithm is determined by the sum of squared errors

and the K is the optimal rank. This can solve the problem of setting the number of rank subjectively in the cluster algorithm. Also

the cluster algorithm is applied to classify the health state of switchgear. Finally

the t-distributed stochastic neighbor embedding algorithm is used to reduce dimension to visualize the cluster results two-dimensionally to realize the visualization of high-dimensional data. The feasibility of the algorithm is verified by the live detection data

which can effectively improve the classification accuracy of switchgear health states by 10.9%. Thus it can provide a certain theoretical basis for the evaluation of switchgear.