An intelligent islanding detection of distribution networks with synchronous machine DG using ensemble learning and canonical methods

One of the crucial challenges of the distribution network is the unintentionally isolated section of electricity from the power network, called unintentional islanding. Unintentional islanding detection is severed when the local generation is equal to or closely matches the load requirement. In this paper, both ensemble learning and canonical methods are implemented for the islanding detection technique of synchronous machine-based distributed generation. The ensemble learning models for this study are random forest (RF) and Ada boost, while the canonical methods are multi-layer perceptron (MLP), decision tree (DT), and support vector machine (SVM). The training and testing parameters for this technique are the total harmonic distortion (THD) of both current and voltage signals. THD is the most important parameter of power quality monitoring under islanding scenarios. The parameter and data extraction from the test system is executed in a MATLAB/Simulink environment, whereas the training and testing of the presented techniques are implemented in Python. Performance indices such as accuracy, precision, recall, and F 1 score are used for evaluation, and both ensemble learning models and canonical models demonstrate good performance. Ada-boost shows the highest accuracy among all the five models with original data, while RF is robust and gives the best results with noisy data (20 and 30 dB) because of its ensemble nature.

Automated summary

This paper investigates the islanding detection technique of synchronous machine-based distributed generation using ensemble learning and canonical methods. Total harmonic distortion (THD) is identified as the most crucial parameter for monitoring power quality in islanding scenarios. Ada-boost demonstrates the highest accuracy among all models, while RF is robust and achieves the best results with noisy data.