Artificial Neural Network Based Virtual Sensor for Distributed Controlled DC Microgrids

The measured output current data of distributed energy resources is crucial in realizing cyber-physical DC microgrids (DCMG) and achieving distributed control objectives. This paper proposes an artificial neural network-based output current estimation at the secondary control level. The estimated output current value is used to implement distributed control successfully for the DCMG. The proposed artificial neural network (ANN) acts as a virtual sensor to alleviate problems associated with physical sensors, such as faults and biasing effects. The ANN’s input features and training performance are detailed for the considered DCMG. The performance of the proposed virtual sensor-based distributed controlled DCMG is validated on an experimental hardware setup under multiple load changes and communication delays.