当前位置: X-MOL 学术J. Electr. Eng. Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Classification Method for Power-Quality Disturbances Using Hilbert–Huang Transform and LSTM Recurrent Neural Networks
Journal of Electrical Engineering & Technology ( IF 1.9 ) Pub Date : 2020-11-27 , DOI: 10.1007/s42835-020-00612-5
Miguel Angel Rodriguez , John Felipe Sotomonte , Jenny Cifuentes , Maximiliano Bueno-López

Power quality disturbances are one of the main problems in an electric power system, where deviations in the voltage and current signals can be evidenced. These sudden changes are potential causes of malfunctions and could affect equipment performance at different demand locations. For this reason, a classification strategy is essential to provide relevant information related to the occurrence of the disturbance. Nevertheless, traditional data extraction and detection methods have failed to carry out the classification process with the performance required, in terms of accuracy and efficiency, due to the presence of a non-stationary and non-linear dynamics, specific of these signals. This paper proposes a hybrid approach that involves the implementation of the Hilbert–Huang Transform (HHT) and long short-term memory (LSTM), recurrent neural networks (RNN) to detect and classify power quality disturbances. Nine types of synthetic signals were reproduced and pre-processed taking into account the mathematical models and their specifications established in the IEEE 1159 standard. In order to eliminate the presence of mode mixing, the ensemble empirical decomposition (EEMD) and masking signal methods were implemented. Additionally, based on the successful benefits of LSTM RNNs reported in the literature, associated to the high accuracy rates achieved at learning long short-term dependencies, this classification technique is implemented to analyze the sequences obtained from the HHT. Based on the experimental results, it is possible to show that the ensemble recognition approach using the EEMD yields a better classification accuracy rate (98.85%) compared with the masking signal and the traditional HHT approach.

中文翻译:

使用 Hilbert-Huang 变换和 LSTM 循环神经网络的电能质量扰动分类方法

电能质量扰动是电力系统中的主要问题之一,其中可以证明电压和电流信号的偏差。这些突然的变化是故障的潜在原因,可能会影响不同需求位置的设备性能。因此,分类策略对于提供与干扰发生相关的相关信息至关重要。然而,由于存在这些信号特有的非平稳和非线性动态特性,传统的数据提取和检测方法无法在精度和效率方面以所需的性能执行分类过程。本文提出了一种混合方法,涉及实施希尔伯特-黄变换 (HHT) 和长短期记忆 (LSTM),循环神经网络 (RNN) 来检测和分类电能质量扰动。考虑到数学模型及其在 IEEE 1159 标准中建立的规范,对九种类型的合成信号进行了再现和预处理。为了消除模式混合的存在,实现了集成经验分解(EEMD)和掩蔽信号方法。此外,基于文献中报道的 LSTM RNN 的成功优势,与在学习长期短期依赖关系时实现的高准确率相关,实施这种分类技术来分析从 HHT 获得的序列。基于实验结果,可以证明使用 EEMD 的集成识别方法可以产生更好的分类准确率 (98.
更新日期:2020-11-27
down
wechat
bug