  The significant imbalance between power generation and load caused by severe
disturbance may make the power system unable to maintain a steady frequency. If
the post-disturbance dynamic frequency features can be predicted and emergency
controls are appropriately taken, the risk of frequency instability will be
greatly reduced. In this paper, a predictive algorithm for post-disturbance
dynamic frequency features is proposed based on convolutional neural network
(CNN) . The operation data before and immediately after disturbance is used to
construct the input tensor data of CNN, with the dynamic frequency features of
the power system after the disturbance as the output. The operation data of the
power system such as generators unbalanced power has spatial distribution
characteristics. The electrical distance is presented to describe the spatial
correlation of power system nodes, and the t-SNE dimensionality reduction
algorithm is used to map the high-dimensional distance information of nodes to
the 2-D plane, thereby constructing the CNN input tensor to reflect spatial
distribution of nodes operation data on 2-D plane. The CNN with deep network
structure and local connectivity characteristics is adopted and the network
parameters are trained by utilizing the backpropagation-gradient descent
algorithm. The case study results on an improved IEEE 39-node system and an
actual power grid in USA shows that the proposed method can predict the lowest
frequency of power system after the disturbance accurately and quickly.
