Distinguishing Micro-Scale Voltage Disturbances Using Wavelet Decomposition Techniques
Power quality (PQ) issues have raised the attention of all parties especially the power electronic community as the disturbances occurred during the power transmission and distribution downgrades the service quality of the power delivered and causes damage to the connected load. In this paper, three...
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| Format: | Final Year Project |
| Language: | English |
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Universiti Teknologi PETRONAS
2014
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| Online Access: | http://utpedia.utp.edu.my/14410/1/final%20report-wcy.pdf http://utpedia.utp.edu.my/14410/ |
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| Institution: | Universiti Teknologi Petronas |
| Language: | English |
| Summary: | Power quality (PQ) issues have raised the attention of all parties especially the power electronic community as the disturbances occurred during the power transmission and distribution downgrades the service quality of the power delivered and causes damage to the connected load. In this paper, three types of PQ disturbances: voltage sag, voltage swell and voltage notch are discussed and a novel approach to distinguish various PQ signal using wavelet multi-resolution decomposition technique is proposed. Today, wavelet transform is increasingly being employed in signal processing in place of Fourier-based technique. The main reason for advocating wavelet transform is that it not only traces signal change across time plane but it also decompose the signal across the frequency plane. In this paper, Haar wavelet and 4-levels of signal decomposition are adequate to detect and distinguish the disturbances from their background. All the modelling and classification processes are performed in MATLAB where wavelet-1D toolbox and MATLAB algorithm are developed and employed. Based on the wavelet decomposition technique, voltage sag and voltage swell disturbances are identified at low frequency bands such as detail coefficients d4 and approximation coefficients a4. Conversely, voltage notch disturbances are clearly captured at high frequency bands particularly in the detail coefficients d1 and d2. 3 types of PQ disturbances are well detected and distinguished by employing this method. This approach is effective in tracking various PQ disturbances as compared to the conventional point-to-point comparison method which is principally based on visual inspection. |
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