This article presents an automatic recognition of power quality events (PQEs) by integrating variational mode decomposition (VMD) with Hilbert transform (HT) and the proposed online P-norm adaptive extreme learning machine (OPAELM). The robust parameters estimation capability from the highly nonstationary PQE patterns is presented using VMDHT method and a novel mode selection scheme is introduced based on the correlation coefficient. Three most efficient power quality indices are extracted and fed as an input to train and test the OPAELM classifier with a few existing advanced classifiers. The distinctive modes extraction, low computational burden, robust antinoise performance, short event recognition time, and outstanding recognition capability are the prime superiority expediencies of the VMDHT-OPAELM method. Finally, the proposed method is developed in Xilinx integrated synthesis environment (ISE) Design Suite 14.5 configured with MATLAB/Simulink software environment and implemented in a high-speed field-programmable gate array digital circuitry hardware platform to validate the cogency in real time.

中文翻译：

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使用基于模式分解的在线 P 范数自适应极限学习机自动电能质量事件识别


本文通过将变分模式分解 (VMD) 与希尔伯特变换 (HT) 和所提出的在线 P 范数自适应极限学习机 (OPAELM) 相结合，提出了一种电能质量事件 (PQE) 的自动识别方法。使用 VMDHT 方法提出了高度非平稳 PQE 模式的鲁棒参数估计能力，并引入了一种基于相关系数的新颖模式选择方案。提取三个最有效的电能质量指数并将其作为输入，使用一些现有的高级分类器来训练和测试 OPAELM 分类器。独特的模式提取、低计算负担、强大的抗噪声性能、短的事件识别时间和出色的识别能力是VMDHT-OPAELM方法的主要优势。最后，该方法在配置有MATLAB/Simulink软件环境的Xilinx集成综合环境（ISE）设计套件14.5中开发，并在高速现场可编程门阵列数字电路硬件平台上实现，以实时验证有效性。