Wavelet Threshold De-noising of Power Quality Signals

Author(s):  
Jie Gu
Energies ◽  
2016 ◽  
Vol 9 (11) ◽  
pp. 933 ◽  
Author(s):  
Nabeel Khan ◽  
Faisal Baig ◽  
Syed Nawaz ◽  
Naveed Ur Rehman ◽  
Shree Sharma

Author(s):  
Richard Bini Almeida ◽  
Kenji Watanabe ◽  
Silvia Mara da Costa Campos Victer

This work presents a scientific study on Short-Time Frequency Transforms (STFT) with different windows, also called Windowed Fourier Transforms, applied to power quality signals.   Additionally, it deals with S transforms, with its frequency-dependent window.  The disturbances related to energy quality have non-stationary nature, in which the spectral content varies over time.   So, the Fourier Transform is not appropriate for such analysis, because it doesn’t show time locations, only information about existing frequencies in the signal.  Therefore, the spectral analysis by windowed transforms helps to identify and detect a series of defects associated to these power signals.  The motivation behind this document is to verify which window will provide a more precise identification of the characteristics of the disturbances in time-frequency domain.    For this work, synthetic signals were generated for some of these disturbances, and their spectra were compared considering Gaussian, Hann and Blackman windows, as well as the S transform. Based on the obtained results, it was verified that each transform presents different behaviours acording to the input signal,  except for the ones with Hann and Blackman windows, that showed similar spectra. For all of them, there is always a tradeoff between time and frequency resolutions. Therefore, the choice of the window must be done according to the desired outputs.  The Dev-C ++ ® IDE was used for C ++ programming, and the Gnuplot ® program for graphics generation.


2019 ◽  
Vol 17 ◽  
pp. 8-13
Author(s):  
L.C.M. Andrade ◽  
◽  
T. Nanjundaswamy ◽  
M. Oleskovicz ◽  
R.A.S. Fernandes ◽  
...  

2015 ◽  
Vol 733 ◽  
pp. 662-665
Author(s):  
Lin Lin ◽  
Xiao Huan Wu ◽  
Jia Jin Qi ◽  
Hong Xin Ci ◽  
Zhi Yong Yu

The noise component in power quality signals affects the accuracy of analysis result. This paper presents a new approach for power quality signals de-noising. Firstly, the original signals are transformed by S-transform method. Then, the matrix which is get from S-transform result is processed as a two-dimension image. The global thresholding de-noising method is used to filtering the noise component in the power quality signals. The simulation results showed the effectiveness of the new approach.


2014 ◽  
Vol 494-495 ◽  
pp. 1889-1894 ◽  
Author(s):  
A.R. Abdullah ◽  
N.A. Abidullah ◽  
N.H. Shamsudin ◽  
N.H.H. Ahmad ◽  
M.H. Jopri

Power quality signals are an important issue to electricity consumers. The signals will affect manufacturing process, malfunction of equipment and economic losses. Thus, an automated monitoring system is required to identify and classify the signals for diagnosis purposes. This paper presents the development of power quality signals classification system using time-frequency analysis technique which is spectrogram. From the time-frequency representation (TFR), parameters of the signal are estimated to identify the characteristics of the signals. The signal parameters are instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion, total harmonic distortion and total non harmonic distortion. In this paper, major power quality signals are focused based on IEEE Std. 1159-2009 such as swell, sag, interruption, harmonic, interharmonic, and transient. An automated signal classification system using spectrogram is developed to identify, classify as well as provide the information of the signal.


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