scholarly journals Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7967
Author(s):  
Shaorui Qin ◽  
Siyuan Zhou ◽  
Taiyun Zhu ◽  
Shenglong Zhu ◽  
Jianlin Li ◽  
...  
Keyword(s):  
White Noise ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Noise Removal ◽  
Time Frequency ◽  
Singular Spectrum ◽  
Frequency Representation ◽  
Synchrosqueezing Transform ◽  
Analytic Wavelet ◽  
The Difference

In electrical engineering, partial discharge (PD) measurement has been widely used for inspecting and judging insulation conditions of high voltage (HV) apparatus. However, on-site PD measurement easily becomes contaminated by noises. Particularly, sinusoidal noise makes it difficult to recognize real PD signal, thus leading to the misjudgment of insulation conditions. Therefore, sinusoidal noise removal is necessary. In this paper, instantaneous frequency (IF) is introduced, and the synchrosqueezing transform (SST) as well as singular spectrum analysis (SSA) is proposed for sinusoidal noise removal. A continuous analytic wavelet transform is firstly applied to the noisy PD signal and then the time frequency representation (TFR) is reassigned by SST. Narrow-band sinusoidal noise has fixed IF, while PD signal has much larger frequency range and time-varying IF. Due to the difference, the reassigned TFR enables the sinusoidal noise to be distinguished from PD signal. After synthesizing the signal with the recognized IF, SSA is further applied to signal refinement. At last, a numerical simulation is carried out to verify the effectiveness of the proposed method, and its robustness to white noise is also validated. After the implementation of the proposed method, wavelet thresholding can be further applied for white noise reduction.

scholarly journals Perbandingan Keakuratan Hasil Peramalan Produksi Bawang Merah Metode Holt-Winters dengan Singular Spectrum Analysis (SSA)

2017 ◽  
Vol 3 (1) ◽  
pp. 13-22
Author(s):  
Yogo Aryo Jatmiko ◽  
Rini Luciani Rahayu ◽  
Gumgum Darmawan
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Percentage Error ◽  
Seasonal Patterns ◽  
Model Data ◽  
Vector Method ◽  
Singular Spectrum ◽  
The Difference ◽  
Method R ◽  
Tracking Signal

The Holt-Winters method is used to model data with seasonal patterns, whether trends or not. There are two methods of forecasting in Singular Spectrum Analysis (SSA), namely recurrent method (R-forecasting) and vector method (V-forecasting). The recurrent method performs continuous continuation (with the help of LRF), whereas the vector method corresponds to the L-continuation. Different methods of course make a difference in the accuracy of forecast results. To see the difference between the three methods is done by looking at the comparison of accuracy and reliability of forecast results. To measure the accuracy of forecasting used Mean Absolute Percentage Error (MAPE) and to measure the reliability of forecasting results is done by tracking signal. Applications are done on Indonesian red onion production from January 2006 to December 2015. Forecasting of both methods in SSA uses window length L = 39 and grouping r = 8. With α = 0.1, β = 0.001 and γ = 0.5, Holt-Winters additive method gives better result with MAPE 13,469% than SSA method.   Keywords: 

scholarly journals Improving time–frequency domain sleep EEG classification via singular spectrum analysis

2016 ◽  
Vol 273 ◽  
pp. 96-106 ◽  
Author(s):  
Sara Mahvash Mohammadi ◽  
Samaneh Kouchaki ◽  
Mohammad Ghavami ◽  
Saeid Sanei
Keyword(s):  
Frequency Domain ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Sleep Eeg ◽  
Eeg Classification ◽  
Time Frequency ◽  
Singular Spectrum
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