Voltage sag source location based on multi-layer perceptron and transfer learning

Abstract

The existing voltage sag source localization only utilizes one simulation type or measurement data based on sampled waveform data. Moreover, it requires more storage space and transmission channels and cannot combine the advantages of simulated and measurement data, resulting in poor applicability of the model. Hence, this paper proposes a voltage sag source locating method based on transfer learning from the sag information in the sag event list combined with the grid structure data. Firstly, the location features of the sag source are extracted from the degree of sag impact, network structure, and sag-type information based on the characteristics of simulated and measurement data that characterize the position of the sag source, and they are collectively used as inputs to the model. Then, the simulated data is used to build a multi-classification model based on the multi-layer perceptron with the line number as the classification number, and the measurement data is employed to fine-tune the model parameters to achieve transfer learning. Finally, voltage sag source localization is achieved based on the trained multi-classification model. The correctness of the proposed method in this paper is verified through simulation and actual measurement in a specific area of East China.