scholarly journals Photovoltaic Power Quality Analysis Based on the Modulation Broadband Mode Decomposition Algorithm

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7948
Author(s):  
Zucheng Wang ◽  
Yanfeng Peng ◽  
Yanfei Liu ◽  
Yong Guo ◽  
Yi Liu ◽  
...  
Keyword(s):  
Back Propagation ◽  
Main Idea ◽  
Gibbs Phenomenon ◽  
Decomposition Algorithm ◽  
Quality Analysis ◽  
Single Frequency ◽  
Square Wave ◽  
Wideband Signal ◽  
Mode Decomposition ◽  
Relative Bandwidth

The Broadband Mode Decomposition (BMD) method was previously proposed to solve the Gibbs phenomenon that occurs during photovoltaic signal decomposition; its main idea is to build a dictionary which contains signal features, and to search in the dictionary to solve the problem. However, BMD has some shortcomings; especially if the relative bandwidth of the decomposed signal is not small enough, it may treat a square wave signal as several narrowband signals, resulting in a deviation in the decomposition effect. In order to solve the problem of relative bandwidth, the original signal is multiplied by a high-frequency, single-frequency signal, and the wideband signal is processed as an approximate wideband signal. This is the modulation broadband mode decomposition algorithm (MBMD) proposed in this article. In order to further identify and classify the disturbances in the photovoltaic direct current (DC) signal, the experiment uses composite multi-scale fuzzy entropy (CMFE) to calculate the components after MBMD decomposition, and then uses the calculated value in combination with the back propagation (BP) neural network algorithm. Simulation and experimental signals verify that the method can effectively extract the characteristics of the square wave component in the DC signal, and can successfully identify various disturbance signals in the photovoltaic DC signal.

scholarly journals Classification of sleep apnea using EMD-based features and PSO-trained neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sajjad Afrakhteh ◽  
Ahmad Ayatollahi ◽  
Fatemeh Soltani
Keyword(s):  
Neural Networks ◽  
Sleep Apnea ◽  
Back Propagation ◽  
Pso Algorithm ◽  
Bp Algorithm ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Obstructive Sleep

Abstract In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN’s performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN’s accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

An Empirical Mode Decomposition Algorithm Based on Cross Validation and Its Application to Lidar Return Signal De-Noising

2014 ◽  
Vol 41 (10) ◽  
pp. 1014001
Author(s):  
王欢雪 Wang Huanxue ◽  
刘建国 Liu Jianguo ◽  
张天舒 Zhang Tianshu ◽  
董云升 Dong Yunsheng
Keyword(s):  
Empirical Mode Decomposition ◽  
Cross Validation ◽  
Decomposition Algorithm ◽  
Mode Decomposition ◽  
Lidar Return

scholarly journals Support Vector Machine for Regional Ionospheric Delay Modeling

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2947 ◽  
Author(s):  
Zhengxie Zhang ◽  
Shuguo Pan ◽  
Chengfa Gao ◽  
Tao Zhao ◽  
Wang Gao
Keyword(s):  
Support Vector Machine ◽  
Regression Model ◽  
Dimensional Space ◽  
Back Propagation ◽  
Single Frequency ◽  
Support Vector ◽  
Electron Content ◽  
Total Electron ◽  
Ionosphere Modeling ◽  
Mean Square Errors

The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU).

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