Patent ID: 11906957
Assignee: HUNAN UNIVERSITY
Field: Electrical machinery, apparatus, energy (Electrical engineering)
Classification: CPC G  H  Y | IPC G  H

Claim 1:
2. The method for assessing the short-term voltage stability of the power grid based on the synchronous spatio-temporal information learning according to claim 1, wherein step S3 comprises:
step S31: performing, by using the graph convolutional neural network algorithm, the synchronous spatio-temporal information learning on the transient sample set obtained in step S1, to match with the spatio-temporal adjacency matrix AST obtained in step S23, dividing the temporal response trajectories of the voltage V, the current I, the active power P, and the reactive power Q within L time instants contained in each sample in the transient sample set obtained in step S1 into L−2 trajectory slices consisting of three consecutive time instants, concisely denoting the temporal response trajectories of the voltage V, the current I, the active power P, and the reactive power Q within the three consecutive time instants in a trajectory slice as x, mapping the x to a hidden layer state h0 using a fully connected layer, and performing a spatio-temporal graph convolution operation on the hidden layer state h0 by using a graph convolution learning module to obtain h1:

h1=(ASTh0W1+b1)eσ(ASTh0W2+b2)  (9)

wherein h1 represents a hidden layer state obtained by the spatio-temporal graph convolution operation, W1, W2, b1, and b2 represent to-be-learned parameters during the spatio-temporal graph convolution operation, σ(*) represents a sigmoid activation function, and e represents a matrix dot product; and
performing the spatio-temporal graph convolution operation for L−2 times during a graph convolution learning, learning L−2 trajectory slices separately, associating the hidden layer state h1 with the fully connected layer and a flattening layer sequentially after completing the graph convolution learning, denoting an output of the flattening layer as f, and associating the output f of the flattening layer with a discretized output of the power grid's predicted status Y′ by using the sigmoid activation function, to construct the short-term voltage stability assessment model driven by the spatio-temporal information synchronization; and
step S32: implementing, by using the transient sample set obtained in step S1, synchronous spatio-temporal information learning and performance test on the short-term voltage stability assessment model driven by spatio-temporal information synchronization in step S31, until a stability assessment accuracy of the short-term voltage stability assessment model meets a requirement.