Klaar Anne Carolina Rodrigues, Seman Laio Oriel, Mariani Viviana Cocco, Coelho Leandro Dos Santos
Graduate Program in Education, University of Planalto Catarinense, Lages 88509-900, Brazil.
Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis 88040-535, Brazil.
Sensors (Basel). 2024 Feb 8;24(4):1113. doi: 10.3390/s24041113.
The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.
电能供应依赖于绝缘子的正常运行。从处于不同状态的绝缘子记录的超声具有时间序列输出，可用于对故障绝缘子进行分类。随机卷积核变换（Rocket）算法使用卷积滤波器从时间序列数据中提取各种特征。本文提出了Rocket算法、机器学习分类器和经验模态分解（EMD）方法的组合，如自适应噪声完备总体经验模态分解（CEEMDAN）、经验小波变换（EWT）和变分模态分解（VMD）。结果表明，EMD方法与MiniRocket相结合，显著提高了绝缘子故障诊断中逻辑回归的准确率。所提出的策略使用CEEMDAN时准确率达到0.992，使用EWT时为0.995，使用VMD时为0.980。这些结果突出了将EMD方法纳入绝缘子故障检测模型以提高电力系统安全性和可靠性的潜力。
