Power grid fault diagnosis method based on time constraints and improved time-convnext network

A power grid fault diagnosis method based on time constraints and an improved Time-ConvNeXt network, comprising the following steps:

• S1:
• extracting and screening key features from a power grid fault alarm text, generating an alarm event encoding matrix, and incorporating a time-dependent exponentially decaying weight, thereby obtaining a temporal encoding matrix;
• S2:
• incorporating a recurrent sequential attention into a ConvNeXt network, thereby obtaining the improved Time-ConvNeXt network, and inputting the temporal encoding matrix for end-to-end training of a power grid fault diagnosis model, thereby extracting spatiotemporal features;
• and
• S3:
• inputting a test set into a trained power grid fault diagnosis model, and outputting a probability value of a power grid fault type via a fully connected layer;
• wherein in the step S1, the time-dependent exponentially decaying weight is as follows:
• w
• decay
• (
• t
• )
• =
• e
• -
• α
• ⁢
• t
• ;
• t
• =
• ❘
• "\[LeftBracketingBar]"
• T
• c
• ⁢
• urrent
• -
• T
• past
• ❘
• "\[RightBracketingBar]"
• ;
• wherein Tpast denotes a timestamp of a historical alarm event;
• Tcurrent denotes a timestamp of a current alarm event;
• t denotes a time difference between the historical alarm event and the current alarm event;
• wdecay(t) denotes a weight value assigned to the historical alarm event;
• and a denotes an attenuation factor configured to control an influence degree of the time difference on the weight value;
• and
• wherein in the step S2, the incorporating the recurrent sequential attention into the ConvNeXt network comprises:
• assigning channel attention weights by global average pooling and the fully connected layer, comprising:
• performing the global average pooling on input features to obtain a feature representation with reduced spatial dimensions, wherein the feature representation is configured to further extract channel-wise features, and then transforming through the fully connected layer, thereby obtaining attention weights for each channel, as follows:
• W
• d
• ⁢
• s
• =
• F
• ⁢
• C
• ⁢
• (
• GAP
• (
• F
• )
• )
• ;
• wherein Wds denotes the attention weights for each channel;
• F denotes an input feature tensor;
• GAP denotes a global average pooling operation, configured to obtain global average values for each channel;
• and FC denotes the fully connected layer configured to transform the global average values into the attention weights for each channel;
• and
• employing a residual mechanism to iteratively update features, comprising:
• multiplying the attention weights with the input features, adding a resulting product to original input features to form a residual connection, and repeating an updating process R times, as follows:
• F
• ds
• =
• F
• +
• F
• ⊙
• W
• ds
• ;
• F
• ds
• =
• update
• (
• F
• )
• i
• ,
• for
• ⁢
• i
• ∈
• R
• ;
• wherein Fds denotes an updated feature tensor;
• ⊙ denotes element-wise multiplication;
• and update denotes performing a cyclic updating operation.