Wavelet packet and support vector machine analysis of series DC ARC fault detection in photovoltaic system

Xia, Kun; He, Sheng; Tan, Yuanqiang; Jiang, Quan; Xu, Jingjun; Wei, Yu

doi:10.1002/tee.22797

Cited by 47 publications

(23 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Daubechies (DbN) are orthogonal and asymmetrical compact support functions [ 30 ]. Symlet (SymN) is an improvement of DbN; it can reduce phase distortion in signal analysis or reconstruction to a certain extent [ 31 ]. Sym8 possesses nearly similar attributes that match well with those of biosignals.…”

Section: Methodsmentioning

confidence: 99%

Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition

Liu

Hua³

et al. 2020

Journal of Healthcare Engineering

View full text Add to dashboard Cite

The feature extraction of surface electromyography (sEMG) signals has been an important aspect of myoelectric prosthesis control. To improve the practicability of myoelectric prosthetic hands, we proposed a feature extraction method for sEMG signals that uses wavelet weighted permutation entropy (WWPE). First, wavelet transform was used to decompose and preprocess sEMG signals collected from the relevant muscles of the upper limbs to obtain the wavelet sub-bands in each frequency segment. Then, the weighted permutation entropies (WPEs) of the wavelet sub-bands were extracted to construct WWPE feature set. Lastly, the WWPE feature set was used as input to a support vector machine (SVM) classifier and a backpropagation neural network (BPNN) classifier to recognize seven hand movements. Experimental results show that the proposed method exhibits remarkable recognition accuracy that is superior to those of single sub-band feature set and commonly used time-domain feature set. The maximum recognition accuracy rate is 100% for hand movements, and the average recognition accuracy rates of SVM and BPNN are 100% and 98%, respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition

Liu

Hua³

et al. 2020

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Since the wavelet transform provides better approximation capacity than the RBF, the WSVM classifier provides higher accuracy than the SVM with RBF kernel. Since then, the WSVM have been employed in many real applications, such as in the medical field [ 21 ], and machine fault diagnosis [ 22 ]. Due to the merits of the LSSVM classifier and the approximation capability of the wavelet kernel, a new least squares wavelet support vector machine (LSWSVM) is proposed first time in this paper to improve both computational efficiency and classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

Van

Hoang

2020

Sensors

View full text Add to dashboard Cite

Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.

show abstract

“…In [19], a methodology based on probabilistic neural network and wavelet packet transform was presented to predict and classify grid faults in a PV system. Various intelligent fault detection methods in PV systems have been proposed based on computational intelligent methods and modern signal processing techniques to detect grid faults correctly and to improve the performance of PV systems [20][21][22][23][24][25][26]. The cumulative sum algorithm (CUSUM) [27] and permanent magnet synchronous motor [28] have been used to improve the performances of fault detection and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

et al. 2019

View full text Add to dashboard Cite

A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.

show abstract

Wavelet packet and support vector machine analysis of series DC ARC fault detection in photovoltaic system

Cited by 47 publications

References 21 publications

Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition

Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

Contact Info

Product

Resources

About