Islanding detection in distributed energy resources based on gradient boosting algorithm

This paper proposes a novel passive-based intelligent method for anti-islanding. Passive methods generally suffer from the improper tuning of threshold values for measured variables and false detection when the active or reactive power mismatch is small. Conversely, intelligence-based methods highly depend on the choice of an appropriate model, the universality of data and selected features. In addition, the risk of overfitting and underfitting for a single model due to an improper selected feature or insufficient data is always possible. This condition makes a single classification model unreliable for practical purposes. The method used in this work is a sequential ensemble of intelligence-based models called gradient boosting. The sequential form of an ensemble is substantially designed to solve the underfitting problem. In addition, by selecting the best combination of features and ensembles, algorithm overfitting can be prevented and the attribute data space can be constructed with maximum discrimination. Furthermore, this condition yields a model with a zero non-detection zone. As a result, a multi-objective optimisation method called the non-dominated sorting genetic algorithm-III is used to tune the model. The reliability and economy of this method for distribution networks with various types of inverter-based and synchronous distributed energy resources are demonstrated.