Aiming at the problems of complex signal types and heterogeneous data of power quality disturbance in novel power system

a power quality disturbance classification method using Federated learning and prototype learning is proposed. This method includes two types of work nodes: server and client. The server collects the local prototype output from the local model of clients. The local prototype cannot be reverse reconstructed to get the original data. Instead

the server aggregates the local prototype to get the global prototype and sends it back to the client to regularize the local model training. Compared with the convolutional neural network model

this method does not require a lot of training data

and the model is not vulnerable to slight heterogeneous data disturbance

and has strong robustness to unknown disturbance signals. The simulation experimental results show that

compared with existing methods

the proposed method is suitable for small-scale power quality disturbance samples

with a classification accuracy of 0.998 3

which has high application value in the new distributed power grid environment.