This article examines the potential ability of a chosen Hilbert–Huang transform (HHT) in detecting and identifying a multi-complex electric power quality events (PQE) signal in smart grid power systems under various noise situations. HHT is an active signal processing technique, comprising the units of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA) to detect the non-stationary electric signals. The function of EMD is to detect and decompose the non-stationary electric signal in different scales of frequency modules ranging from maximum to minimum values, and thereby the attained signals are characterized as intrinsic mode functions (IMFs). HSA details the IMF signals individually to produce a unique Hilbert spectrum which carries the information of original signal. By observing the instant time-varying deviations of frequency and amplitude of the resultant signals, it is possible to categorize the disturbing signals from the original signal. The discussed slots of work is simulated under MATLAB environment, and the results report that the HHT successfully detects the single PQE, complex PQE and multi-complex PQE signals under 25 dB, 50 dB and without noise situations. The outcomes of HHT technique are compared with other transforms such as S-transform and wavelet transform to highlight superior qualities of HHT. The identified PQE signals from HHT are classified using support vector machine to improve its classification accuracy. It is wiser to disclose that the proposed system with inbuilt monitoring and identification of PQE signals will suit present smart grid system.

中文翻译：

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使用希尔伯特-黄变换和支持向量机检测和分类智能电网中的多复杂电能质量事件

本文研究了所选的希尔伯特-黄变换 (HHT) 在检测和识别智能电网电力系统中各种噪声情况下的多复杂电能质量事件 (PQE) 信号方面的潜在能力。HHT是一种有源信号处理技术，包括经验模式分解（EMD）和希尔伯特谱分析（HSA）单元来检测非平稳电信号。EMD 的功能是对非平稳电信号进行从最大值到最小值的不同尺度频率模块的检测和分解，从而将得到的信号表征为本征模式函数（IMFs）。HSA 对 IMF 信号分别进行详细说明，以产生独特的希尔伯特谱，该谱携带原始信号的信息。通过观察合成信号的频率和幅度的瞬时时变偏差，可以从原始信号中对干扰信号进行分类。讨论的工作槽在 MATLAB 环境下进行仿真，结果表明 HHT 在 25 dB、50 dB 和无噪声情况下成功检测了单 PQE、复 PQE 和多复 PQE 信号。将 HHT 技术的结果与 S 变换和小波变换等其他变换进行比较，以突出 HHT 的优越品质。从 HHT 识别出的 PQE 信号使用支持向量机进行分类，以提高其分类精度。披露具有内置监控和识别 PQE 信号的拟议系统将适合当前的智能电网系统是更明智的做法。