Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection  
[PERSON]  
1 College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (Q.Z.) [PERSON]  
33 College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China 334  
electronic converter might lead to harmonics and notches. Voltage flickers are usually caused by arc furnaces. Transients and spikes may be caused by lightning or a sudden switching-off for large loads. Power quality (PQ) is the ability of power systems to deliver undistorted voltage, current, and frequency signals with a set of limits of electrical properties, which permit power systems to function under expected operational conditions without a significant loss of performance or life [7]. According to the IEEE standard 1159-1995 [8], the PQD include a wide range of PQ phenomena, namely transient (impulsive and oscillatory), short duration variations (interruption, sag, and swell), frequency variations, long duration variations (sustained under voltages and sustained over voltages), and steady state variations (harmonics, notch, and flicker), with a time scale that ranges from tens of nanoseconds to steady state. The IEEE standard 1459-2010 [9] includes definitions for the measurement of electric power quantities under sinusoidal, non-sinusoidal, balanced, and unbalanced conditions. In [10], the authors presented an IEEE Std.1459 power magnitude measurement system that works as a part of a PQ improvement structure. It is essential to analyze and identify power disturbances through power quality event detection to mitigate power quality events. The effective strategy of mitigation action can be implemented to improve the quality of power systems by classifying power quality disturbances. Various studies have focused on signal processing methods, such as Fourier Transform (FT), short time Fourier Transform (STFT), S-transform, and wavelet transform (WT), when considering that feature extraction plays an important role in power quality analysis [11; 12; 13]. However, Fourier Transform is effective for stationary signals, but it is not suitable for PQD detection. Meanwhile, Short Time Fourier Transform also has a limitation, as it has a fixed resolution when the time-frequency window is selected [14]. The wavelet transform has better performance under ideal conditions, but its ability to detect PQDs is reduced in a noisy background [15]. S-transform (ST) is a time-frequency analysis method that combines the WT and STFT. ST is utilized to recognize PQDs by using a flexible localizing Gaussian window. However, in [16], it shows that ST degrades when dealing with some nonstationary events, especially for transient PQDs. Recently, empirical mode decomposition (EMD) has been widely used in PQD feature extraction due to its excellent local adaptive performance [17; 18]. In [19], the authors developed a method for evaluating PQDs by utilizing EMD and the Hilbert transform. Nevertheless, EMD is sensitive to noise and sampling and it suffers from the problems of recursive calculation and mode mixing [20]. [PERSON] and [PERSON] proposed a more robust decomposition technique, named variational mode decomposition (VMD), to overcome the drawbacks of EMD [21]. VMD decomposes a signal into several band-limited intrinsic modes by updating with Wiener filtering. Each mode has a central frequency that is gradually demodulated to the corresponding baseband. Different from EMD, the bandwidth of the VMD intrinsic modes is narrow, which can not only prevent the mode mixing problem in EMD but also accurately extract time-frequency features. In highlighting VMD, many studies have tried to enhance its application in signal feature extraction. [PERSON] et al. [22] proposed a VMD-based method to assess the health status of wind turbines in condition monitoring fields. [PERSON] et al.