Islanding Detection for Active Distribution Networks Using WaveNet+UNet Classifier

TL;DR

This paper introduces a WaveNet+UNet AI-based classifier for reliable, fast, and noise-robust islanding detection in active distribution networks, addressing key limitations of existing methods.

Contribution

It proposes a novel WaveNet classifier reinforced by a denoising U-Net, improving accuracy, stability, and applicability across various system conditions with a simple, efficient structure.

Findings

High detection accuracy across 3,000 test cases

Robust performance under noisy conditions

Fast detection within 10 milliseconds

Abstract

This paper proposes an AI-based scheme for islanding detection in active distribution networks. By reviewing existing studies, it is clear that there are several gaps in the field to ensure reliable islanding detection, including (i) model complexity and stability concerns, (ii) limited accuracy under noisy conditions, and (iii) limited applicability to systems with different types of resources. Accordingly, this paper proposes a WaveNet classifier reinforced by a denoising U-Net model to address these shortcomings. The proposed scheme has a simple structure due to the use of 1D convolutional layers and incorporates residual connections that significantly enhance the model's generalization. Additionally, the proposed scheme is robust against noisy conditions by incorporating a denoising U-Net model. Furthermore, the model is sufficiently fast using a sliding window time series of 10 milliseconds for detection. Utilizing positive/negative/zero sequence components of voltages, superimposed waveforms, and the rate of change of frequency provides the necessary features to precisely detect the islanding condition. In order to assess the effectiveness of the suggested scheme, over 3k islanding/non-islanding cases were tested, considering different load active/reactive powers values, load switching transients, capacitor bank switching, fault conditions in the main grid, different load quality factors, signal-to-noise ratio levels, and both types of conventional and inverter-based sources.