A Machine Learning Approach for Coordinated Voltage and Reactive Power Control

Increasing penetration of renewable-based distributed generators (DGs) has transformed passive distribution networks to active distribution networks (ADNs). Therefore, traditional practices for voltage and reactive power (V/Q) control should be revised and improved. All control resources should be coordinated based on real-time information and in closed loop. To achieve this, machine learning (ML) is used to assist in making decisions by mapping the relationship between the selected network information and the desired control output. In this paper, setting of the shunt compensator operating in capacitive or inductive modes is coordinated with the tap position of the substation transformer such that all security measures are within the limits. Dataset emulating network behaviour during a year of operation is constructed for training a ML algorithm. A multi-class classification problem is formulated. Simulation results show satisfactory accuracy for some classes.