Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters

Abstract

With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive measurements and models, such as admittance at the PCC. This classification poses a challenge as the TSO lacks precise knowledge of the actual control structures and algorithms. This paper introduces a novel classification algorithm based on Convolutional Neural Networks (CNN), capable of detecting patterns in sequential data. The proposed CNN utilizes a new architecture to separate admittances along the d and q axes, and a decision layer allows to determine the correct converter mode. The performance of the proposed CNN model was assessed through two tests and compared to the traditional feedforward model. The proposed CNN architecture demonstrates significant classification capabilities, as it is able to identify the control mode of the converter even when its control structure is not part of the training dataset.