Name
Title | ||
|---|---|---|
Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries |
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 elementary 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIM.2014.2330493 | Instrumentation and Measurement, IEEE Transactions |
Keywords | DocType | Volume |
compressed sensing,decision trees,distributed power generation,fault diagnosis,feature extraction,harmonic distortion,matrix decomposition,power supply quality,power system faults,power system measurement,power system transients,signal classification,sparse matrices,Gabor-Wigner transform,Hilbert-Haung transform,OHD matrix,PQ disturbance classification,PQ disturbance detection,PQ feature extraction,PQ monitoring network,S transform,SSD,compressed sensing,dc-offset,decision tree algorithm,long-duration variation,microgrid simulated PQ disturbance,overcomplete hybrid dictionary matrix,power quality disturbance classification,power quality disturbance detection,power system monitoring,short-duration variation,sparse signal decomposition,waveform distortion,wavelet transform,Compressed sensing,disturbance classification,overcomplete dictionary,power quality (PQ) signal analysis,power system monitoring,sparse representation,sparse representation. | Journal | 64 |
Issue | ISSN | Citations |
1 | 0018-9456 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Sabarimalai Manikandan | 1 | 184 | 20.80 |
| Samantaray, S.R. | 2 | 14 | 2.07 |
| Innocent Kamwa | 3 | 25 | 12.52 |