[Objective] The traditional methods for extracting fault characteristics of disconnector are not reliable enough

and the fault identification accuracy is not high. To address this issue

a fault diagnosis method for disconnector based on multi-characteristic map and convolutional neural network (CNN)-black-winged kite algorithm (BKA)-least squares support vector machine (LSSVM) is proposed. [Methods] Firstly

three characteristic maps were constructed in the frequency domain

time domain

and time-frequency domain using the Markov transition field (MTF)

Gramian angular field (GAF)

and short-time Fourier transform (STFT) respectively. Then

three CNN models were established separately

three characteristic maps were input

effective fault characteristics were extracted through convolution

pooling

and other steps. And the t-distributed stochastic neighbor embedding algorithm was used to reduce the dimension of the characteristic data in the fully connected layer of the CNN model. Finally

the extracted characteristic vectors were fused and spliced

the BKA-optimized LSSVM was used instead of the Softmax layer

and the fused characteristic vectors were input into the BKA-LSSVM for fault identification. [Results] Through on-site fault simulation tests

vibration signal data of the disconnector in four different states were collected

and comparative analysis was carried out. The results showed that the model proposed in this paper has higher accuracy

stronger reliability and generalization ability compared with other fault diagnosis models

and the average diagnostic accuracy of the proposed model for 8 runs reached 97.08%. The experimental results verified the feasibility of the proposed model. [Conclusion] The proposed method in this paper extracts the characteristic maps from multiple dimensions

which overcomes the limitations of the single dimension method. By introducing the CNN-BKA-LSSVM model

it can better extract the key characteristics and improve the precision and accuracy of fault identification. The proposed method provides a reliable theoretical basis and technical reference for the fault diagnosis of disconnectors

and also provides new ideas for the maintenance of disconnector equipment

which has important application value.