Abstract In this paper, Hilbert-Huang Transform (HHT) and Discrete Wavelet Transform (DWT) based methods are employed on line to monitor the deviation in electrical quantities of the power system integrated with distributed generation. HHT employs an Empirical Mode Decomposition (EMD) method i.e. decomposition of the signal into Intrinsic Mode Functions (IMFs) in such a way that they are sorted from the highest frequency to the lowest frequency whereas, Wavelet Transform (WT) decomposes the signal into components that are indicative of signal details and trend. This paper presents a comparative analysis of HHT and DWT with the motive to provide the guidelines for deciding which one of these techniques is more suitable for Power Quality (PQ) event analysis in the case of Distributed Generation (DG) integrated system, in real-time domain. From the comparative analysis, it is observed that DWT is more precise in comparison with HHT in determining PQ events in real-time domain with high resolution. This work further explores the potential of WT for the real time application in measuring, monitoring, and analyzing the electrical signals during the occurrence of voltage sag, swell and harmonics. Real time implementation of WT necessitates signal sections to be serially processed. Towards the same, signal samples are collected in a buffer and then subjected to WT to extract the inherent details. The processing of the signal gives attributes that help in identifying the type of disturbance. Performance of the proposed method is tested through simulations on MATLAB/Simulink platform along with the experimental validation of the technique. Results reveal that the application of WT, in real time, can accurately detect the instant of disturbances with minimum delay to analyze the PQ issues.

中文翻译：

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基于小波的分布式发电（DG）集成系统电信号实时监测

摘要 本文采用基于希尔伯特-黄变换(HHT)和离散小波变换(DWT)的方法在线监测分布式发电一体化电力系统的电量偏差。HHT 采用经验模态分解 (EMD) 方法，即将信号分解为固有模态函数 (IMF)，从最高频率到最低频率排序，而小波变换 (WT) 将信号分解为分量指示信号细节和趋势。本文对 HHT 和 DWT 进行了比较分析，目的是为确定这些技术中的哪一种更适合分布式发电 (DG) 集成系统中的电能质量 (PQ) 事件分析提供指导。时域。从对比分析中可以看出，与HHT相比，DWT在高分辨率实时域确定PQ事件方面更精确。这项工作进一步探索了 WT 在电压骤降、骤升和谐波发生期间实时测量、监测和分析电信号的潜力。WT 的实时实现需要串行处理信号部分。同样，将信号样本收集在缓冲区中，然后进行 WT 以提取固有细节。信号的处理提供了有助于识别干扰类型的属性。通过在 MATLAB/Simulink 平台上进行仿真以及对该技术的实验验证来测试所提出方法的性能。结果表明，WT的应用，