Deep Learning Enabled Scheduling System Data Anomaly Analysis in Robotic Process Automation

Robotic Process Automation (RPA) can automate various tasks in power grid scheduling, e.g., load forecasting, power flow calculation, equipment monitoring, etc. In emergency command scenarios, RPA can quickly respond and process various alarm information, assisting dispatchers in making correct decisions. This paper develops a deep learning-based solution to enable scheduling system data anomaly analysis in the presence of RPA. The proposed method is described and also assessed through experiments and implementation. The numerical results have confirmed that the proposed solution can well cope with the challenges of data analysis and anomaly detection. The application of RPA in power systems has broad prospects and enormous potential. With the continuous development and improvement of technology, RPA can be one of the key technologies during the process of digital transformation and intelligent upgrading of the power system in the future.