A Transfer Learning Method for Intelligent Load Shedding using Graph Convolutional Network considering Unknown Faults

Pourmoradi, Nazanin; Ameli, Mohammad Taghi

doi:10.48308/ijrtei.2024.237789.1066

A Transfer Learning Method for Intelligent Load Shedding using Graph Convolutional Network considering Unknown Faults

Document Type : Research Article

Authors

Nazanin Pourmoradi

Mohammad Taghi Ameli

Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

10.48308/ijrtei.2024.237789.1066

Abstract

Event-based load shedding (ELS) is a vital emergency countermeasure against transient voltage instability in power systems. Deep learning(DL)--based ELS has recently achieved promising results. However, in power systems, faults may occur that are not in the training database, reducing the model's effective performance. In this situation, it is necessary to update the model. On the other hand, updating the model for new faults requires a large database. To address the problem of unknown faults, this paper proposes a transfer learning-based graph convolutional network (GCN) model that allows updating the model with a small database. In the first step, an ELS model is trained with a large database. Then, if a new fault occurs, the model is transferred to the new fault and updated using transfer learning and with a small database. To evaluate the performance of the proposed model, it was implemented and tested on the IEEE 39 bus system. The results show that the proposed model has high-performance accuracy and can be updated with a small database when encountering an unknown fault. According to the results, the proposed model has reduced the database size by 78.91% for optimal updating.

Keywords

Graph Convolutional Network

Unknown Fault

Transfer Learning

Event-based Load Shedding

Small Database

Subjects