Existing power grid fault diagnosis methods rely on manual experience to design diagnosis models

lack the ability to extract fault knowledge

and are difficult to adapt to complex and changeable engineering sites. Considering this situation

this paper proposes a power grid fault diagnosis method based on a deep pyramid convolutional neural network for the alarm information set. This approach uses the deep feature extraction ability of the network to extract fault feature knowledge from alarm information texts and achieve end-to-end fault classification and fault device identification. First

a deep pyramid convolutional neural network model for extracting the overall characteristics of fault events is constructed to identify fault types. Second

a deep pyramidal convolutional neural network model for alarm information text is constructed

the text description characteristics associated with alarm information texts are extracted

the key information corresponding to faults in the alarm information set is identified

and suspicious faulty devices are selected. Then

a fault device identification strategy that integrates fault-type and time sequence priorities is proposed to identify faulty devices. Finally

the actual fault cases and the fault cases generated by the simulation are studied

and the results verify the effectiveness and practicability of the method presented in this paper.