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

Abstract

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 MAT-LAB/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. INTRODUCTIONDistributed generation (DG) integration has gained a lot of interest because of electricity market deregulation, customer requirements for reliable and high-quality power, and environmental concerns. Although DG integration has some vital benefits such as cost reduction, low transmission losses, fewer emissions, flexibility, and reliability, it also introduces challenges and hitches in the power distribution network, one of which is the islanding condition. The term "islanding" means that the operating system has all the load and generation isolated from the main utility. In a power grid, the process of islanding is partitioned into two major classes: intentional islanding and unintentional islanding. Intentional islanding is designed primarily for system repairs and operating problems, whereas unintentional islanding incidents are triggered by unex-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Comprehensive Review of Intelligent Islanding Schemes and Feature Selection Techniques for Distributed Generation System

“…Table IX compares intelligent classifiers with traditional IDS based on different criteria such as reliability, complexity accuracy, detection speed, impact on PQ, and implementation cost. In the research on islanding detection conducted by our team, various intelligent classifiers were compared based on accuracy, precision, recall, and F_1 score in [3]. Adaboost performs very accurately with the highest accuracy, precision, recall, and F_1 score, while DT performance is worst among all models.…”

“…In the conventional electrical power system (EPS), the production of power is centrally operated, and power is delivered to customers through transmission and distribution networks. The primary disadvantages of conventional networks are their high cost and transmission losses, environmental issues, and the unidirectional flow in the network [2], [3]. However, DG interconnection also poses challenges such as elongated payback times, the intermittent nature of renewables, and glitches in the power system [4].…”

Security Assessment for the Islanding Transition of Microgrids

“…In [34], an intelligent islanding detection method is proposed based on intrinsic mode function feature-based grey wolf optimized artificial neural network. Both ensemble learning and canonical methods are considered for the islanding detection technique of synchronous machine-based distributed generation in [35]. Kalman filter is used to extract and filter the harmonic contents of the voltage signal at DG terminals to identify the islanding operation in [36].…”

A Wavelet Transform-Based Real-Time Filtering Algorithm for Fusion Magnet Power Signals and Its Implementation

“…Nevertheless, its fixed resolution restricts its usefulness. In recent studies, researchers have proposed combining STFT with convolutional neural networks [5], normative methods [6], and integrated learning for noise-containing signal processing for intelligent island detection. Despite their effectiveness in extracting signal features, machine learning-based methods have the drawbacks of having many network parameters and requiring large amounts of data for training.…”