Fault location in transmission lines based on stationary wavelet transform, determinant function feature and support vector regression

2014 ◽  
Vol 110 ◽  
pp. 73-83 ◽  
Author(s):  
A.A. Yusuff ◽  
A.A. Jimoh ◽  
J.L. Munda
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Transmission Lines ◽  
Fault Location ◽  
Support Vector ◽  
Stationary Wavelet Transform ◽  
Determinant Function

scholarly journals Fault location for MMC–MTDC transmission lines based on least squares-support vector regression

2019 ◽  
Vol 2019 (16) ◽  
pp. 2125-2130 ◽  
Author(s):  
Mingguang Zhang ◽  
Heng Wang
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Transmission Lines ◽  
Fault Location ◽  
Support Vector

Wavelet transform-based weighted $$\nu$$-twin support vector regression

2019 ◽  
Vol 11 (1) ◽  
pp. 95-110 ◽  
Author(s):  
Lidong Wang ◽  
Chuang Gao ◽  
Nannan Zhao ◽  
Xuebo Chen
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Support Vector ◽  
Twin Support Vector Regression

scholarly journals Study on Fault Location in the T-Connection Transmission Lines Based on Wavelet Transform

2015 ◽  
Vol 03 (03) ◽  
pp. 106-115
Author(s):  
Penggao Wen ◽  
Hong Song ◽  
Zhiting Guo ◽  
Quan Pan
Keyword(s):  
Wavelet Transform ◽  
Transmission Lines ◽  
Fault Location

scholarly journals Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 586 ◽  
Author(s):  
Longkang Chang ◽  
Huiliang Cao ◽  
Chong Shen
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Temperature Compensation ◽  
Linear Prediction ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Temperature Error ◽  
Support Vector ◽  
Mixed Features

For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

Robust and sparse Dual Tree Complex Wavelet Transform-based Twin Support Vector regression for dense 5G InH  Communications

2021 ◽  
Author(s):  
anis charrada ◽  
Abdelaziz Samet
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Support Vector Regression ◽  
Path Loss ◽  
Series Data ◽  
Support Vector ◽  
Sensitivity Threshold ◽  
Discrete Wavelet ◽  
Twin Support Vector Regression ◽  
Receiver Sensitivity

Abstract A robust and sparse Twin Support Vector Regression based on Dual Tree Discrete Wavelet Transform algorithm is conceived in this paper and applied to 28, 38, 60 and 73-GHz LOS (Line-of-Sight) wireless multipath transmission system in 5G Indoor Hotspot (InH) settings (simple, semi-complex and complex conference rooms) under small receiver sensitivity threshold. The algorithm establishes a denoising process in the learning phase based on Dual Tree Discrete Wavelet Transform (DT-CWT) which is suitable for time-series data. Additionally, the Close-In (CI) free space reference distance path loss model is analyzed and the large-scale propagation and probability distribution functions are investigated by determining the PLE (Path Loss Exponent) and the standard deviation of Shadow Factor (SF) for each InH scenario under consideration. Performance are evaluated under twelve (12) configuration scenarios, according to three criteria: mobility (0/3mps), receiver sensitivity threshold (-80/-120 dBm) and type of the InH area (simple, semi-complex and complex conference room). Experimental results confirm the effectiveness of the proposed approach compared to other standard techniques.

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