IEEE TRANSACTIONS ON INSTRUMENTATIONAND MEASUREMENT, VOL. 64, NO. 1, JANUARY 2015 27 Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries M. Sabarimalai Manikandan, Member, IEEE, S. R. Samantaray, Senior Member, IEEE, and Innocent Kamwa, Fellow, IEEE Abstract— Several methods have been proposed for detection and classification of power quality (PQ) disturbances using wavelet, Hilbert transform, Gabor transform, Gabor–Wigner transform, S transform, and Hilbert–Haung transform. This paper presents a new method for detection and classification of single and combined PQ disturbances using a sparse signal decomposition (SSD) on overcomplete hybrid dictionary (OHD) matrix. The method first decomposes a PQ signal into detail and approximation signals using the proposed SSD technique with an OHD matrix containing impulse and sinusoidal elemen- tary waveforms. The output detail signal adequately captures morphological features of transients (impulsive and oscillatory) and waveform distortions (harmonics and notching). Whereas the approximation signal contains PQ features of fundamental, flicker, dc-offset, and short- and long-duration variations (sags, swells, and interruptions). Thus, the required PQ features are extracted from the detail and approximation signals. Then, a hierarchical decision-tree algorithm is used for classification of single and combined PQ disturbances. The proposed method is tested using both synthetic and microgrid simulated PQ disturbances. Results demonstrate the accuracy and robustness of the method in detection and classification of single and combined PQ disturbances under noiseless and noisy conditions. The method can be easily expanded for compressed sensing based PQ monitoring networks. Index Terms— Compressed sensing, disturbance classification, overcomplete dictionary, power quality (PQ) signal analysis, power system monitoring, sparse representation. I. I NTRODUCTION P OWER quality (PQ) monitoring has become an important part of power distribution networks to avoid equipment damage and to determine the cause of the disturbances [1]. Several approaches for detection and classification of PQ disturbances have been developed based on the digital filters, morphology operators, short-time Fourier transform (STFT), wavelet transform (WT), wavelet packet transform (WPT), Manuscript received February 24, 2014; revised May 16, 2014; accepted May 18, 2014. Date of publication June 30, 2014; date of current version December 5, 2014. The Associate Editor coordinating the review process was Dr. Kurt Barbe. M. S. Manikandan and S. R. Samantaray are with the School of Elec- trical Sciences, IIT Bhubaneswar, Bhubaneswar 641112, India (e-mail: msm@iitbbs.ac.in; sbh_samant@yahoo.co.in). I. Kamwa is with Hydro-Qubec/Hydro-Quebec Research Institute, Varennes, QC J3X 1S1, Canada (e-mail: kamwa.innocent@ireq.ca). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIM.2014.2330493 Hilbert transform (HT), Gabor transform (GT), Wigner distrib- ution function (WDF), S-transform (ST), Gabor-Wigner trans- form (GWT), Hilbert–Haung transform (HHT), and hybrid transform based methods [2]–[21]. Lieberman et al. [18] presented an excellent review on techniques and methodologies for PQ analysis and disturbances classification in power systems. The disturbance classification method commonly consists of three major steps: preprocessing, feature extraction, and classification. Tse et al. [5] summarized major limitations of different techniques, such as STFT, WT, WDF, GT, and GWT for analysis of PQ signals. The discriminative wavelet features extraction highly relies on the optimal wavelet, number of decomposition level and characteristics subbands. As shown in [5], the discrete WT and WPT exhibits varying spectra leakages in subbands. The ST uses a scalable Gaussian window for analyzing and detecting PQ disturbances [17]. The optimal parameters of Gaussian window are chosen to preserve temporal–spectral characteristics of PQ disturbances. The HHT by the empirical mode decomposition technique is very much influenced by the noise superimposed on the signal. In our previous work [21], it is observed that a set of elementary waveforms from a single basis matrix (or multi- wavelet bases) may not flexible enough to capture essential PQ features of different types of PQ disturbances. Although many decomposition techniques used in the aforementioned classification methods, it is not enough to capture meaningful, discriminative morphological features for classification of different PQ disturbances appeared simultaneously [2]. In this paper, we present a new method for detection and classification of PQ disturbances using sparse signal decompo- sition (SSD) technique with overcomplete hybrid dictionaries (OHDs). The PQ features extracted from both detail and approximation signals are used for detection and classification of single and combined of PQ disturbances. A hierarchical decision-tree (HDT) algorithm is presented to reduce compu- tational complexity. This paper mainly deals with the signal decomposition, feature extraction, event detection, and classi- fication. Finally, to demonstrate the effectiveness of the SSD technique, the proposed method is tested with different types of single and combined PQ signals. The results show that the proposed method is proven to be capable of achieving better accuracy in detection and classification of PQ disturbances, 0018-9456 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.