ABSTRACT

In this article, a robust random under-sampling boosting (RUSB) islanding detection technique evolved from the machine learning (ML) approaches is proposed. Unlike the conventional passive islanding techniques, the ML algorithms are superior in performance owing to their better dynamic behaviour. However, a major challenge arises when non-islanding events are more as compared to islanding events. This leads to skewness in dataset resulting in improper classification, and poor accuracy are often observable. To address these issues, the proposed algorithm which relies on the under-sampling approach can easily identify the dominating cases in the available dataset such that overall detection accuracy improves with a better dynamic response. Also, the modal transformation employed at the point to measurement is a rescuer for reducing the dataset by decomposing the three-phase current signal into single-phase current signal (also known as modal current (MC) component). Therefore, the feature extraction is carried out from MC’s information by employing wavelet transformation to detect islanding conditions quickly. The extensive numerical simulations are carried out for a standard IEEE 15 bus distribution network to assess the improvement achieved in the accuracy of classification and the ability to accurately detect the islanding condition in the event of large number of non-islanding test cases with a fast dynamic response.

摘要

在本文中，提出了一种从机器学习 (ML) 方法演变而来的鲁棒随机欠采样增强 (RUSB) 孤岛检测技术。与传统的被动孤岛技术不同，ML 算法由于其更好的动态行为而在性能上更胜一筹。然而，当非孤岛事件比孤岛事件更多时，就会出现一个重大挑战。这会导致数据集出现偏斜，从而导致分类不当，并且经常会出现精度不佳的情况。为了解决这些问题，所提出的依赖于欠采样方法的算法可以轻松识别可用数据集中的主要情况，从而提高整体检测精度并提供更好的动态响应。还，在测量点采用的模态变换是通过将三相电流信号分解为单相电流信号（也称为模态电流 (MC) 分量）来减少数据集的救星。因此，利用小波变换对MC信息进行特征提取，快速检测孤岛情况。对标准 IEEE 15 总线配电网络进行了广泛的数值模拟，以评估分类准确性的提高以及在具有快速动态的大量非孤岛测试用例的情况下准确检测孤岛情况的能力回复。利用小波变换从MC信息中提取特征，快速检测孤岛情况。对标准 IEEE 15 总线配电网络进行了广泛的数值模拟，以评估分类准确性的提高以及在具有快速动态的大量非孤岛测试用例的情况下准确检测孤岛情况的能力回复。利用小波变换从MC信息中提取特征，快速检测孤岛情况。对标准 IEEE 15 总线配电网络进行了广泛的数值模拟，以评估分类准确性的提高以及在具有快速动态的大量非孤岛测试用例的情况下准确检测孤岛情况的能力回复。