Hybrid islanding detection method based on decision tree and positive feedback for distributed generations

Islanding detection plays an important role in operation and control of distributed generations (DGs). In this study, a novel hybrid method is proposed, which combines decision tree (DT) with voltage and frequency positive feedbacks for islanding detection in DGs. In the proposed method, the feature indices of prescribed events are captured and recorded in a target location to train a DT classifier, and the classifier can then be used for categorising islanding mode or grid-connected mode. The voltage and frequency positive feedback schemes are employed to regulate the DG power outputs based on voltage and frequency deviation between DG outputs and rated values. Furthermore, the disturbances are introduced to change the feature indices significantly when DG is switched to islanding mode, which can increase the sensitivities of feature indices and improve detection accuracy. The proposed hybrid method has been fully evaluated and tested on a representative DG system with two DGs. Simulation studies demonstrate that the proposed method can detect the islanding more accurately in situations where the power is balanced between DG output and load consumption, and can also improve detection accuracy in situations where DG system operates in minimum or maximum loading states.