The application relates to a power distribution facility
anomaly detection method based on
deep learning, and relates to the technical field of power distribution facility
anomaly detection. The application collects electric characteristic signals of a power distribution facility at a set sampling rate, selects electric characteristic signals of a non-anomaly time period located on a slow spectrum submanifold as training data, minimizes a
loss function, uses a full connection network to learn a mapping matrix from a nonlinear
feature vector of the electric characteristic signals to a
latent variable on a low-dimensional slow spectrum submanifold of the electric characteristic signals and a linear dynamics matrix in a latent space, in application, collects electric characteristic signals, uses the last corresponding
latent variable of the electric characteristic signals and a predicted future
latent variable obtained by iteration using the linear dynamics matrix, reconstructs a predicted non-anomaly electric characteristic
signal through inverse transformation between the latent variable and the electric characteristic
signal, and performs anomaly analysis by using the difference between the predicted non-anomaly electric characteristic
signal and an actual electric characteristic signal.