A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

Lima, Marcelo A. A.; Cerqueira, A. S.; Coury, Denis Vinicius; Duque, Carlos A.

doi:10.1016/j.ijepes.2012.05.004

Cited by 28 publications

(7 citation statements)

References 28 publications

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“…A hybrid model for identification of the complex PQ events by the use of wavelet multi-class support vector machine (SVM) is detailed by the authors in [25]. Lima et al [26], proposed a technique based on independent component analysis (ICA) for analysis of the complex PQ signals. Dalai et al [27], introduced a cross wavelet aided Fischer linear discriminate processing technique to sense simultaneous incidence of the complex PQ events.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

Mahela¹,

Shaik

Khan

et al. 2020

IEEE Access

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Deteriorated quality of power leads to problems, such as equipment failure, automatic device resets, data errors, failure of circuit boards, loss of memory, power supply issues, uninterrupted power supply (UPS) systems generate alarm, corruption of software, and heating of wires in distribution network. These problems become more severe when complex (multiple) power quality (PQ) disturbances appear. Hence, this manuscript introduces an algorithm for identification of the complex nature PQ events in which it is supported by Stockwell's transform (ST) and decision tree (DT) using rules. PQ events with complex nature are generated in view of IEEE-1159 standard. Eighteen different types of complex PQ issues are considered and studied which include second, third, and fourth order disturbances. These are obtained by combining the single stage PQ events such as sag & swell in voltage, momentary interruption (MI), spike, flicker, harmonics, notch, impulsive transient (IT), and oscillatory transient (OT). The ST supported frequency contour and proposed plots such as amplitude, summing absolute values, phase and frequencyamplitude obtained by multi-resolution analysis (MRA) of signals are used to identify the complex PQ events. The statistical features such as sum factor, Skewness, amplitude factor, and Kurtosis extracted from these plots are utilized to classify the complex PQ events using rule-based DT. This is established that proposed approach effectively identifies a number of complex nature PQ events with accuracy above 98%. Performance of the proposed method is tested successfully even with noise level of 20 dB signal to noise ratio (SNR). Effectiveness of the proposed algorithm is established by comparing it with the methods reported in literature such as fuzzy c-means clustering (FCM) & adaptive particle swarm optimization (APSO), Wavelet transform (WT) & neural network (NN), spline WT & ST, ST & NN, and ST & fuzzy expert system (FES). Results of simulations are validated by comparing them with real time results computed by Real Time Digital Simulator (RTDS). Different stages for design of complex PQ monitoring device using the proposed approach are also described. It is verified that the proposed approach can effectively be employed for design of the online complex PQ monitoring devices. INDEX TERMS Complex nature PQ event, power quality, ruled decision tree, Stockwell's transform, statistical feature. I. INTRODUCTION N OWADAYS, power quality is becoming a serious issue to service providers and consumers. This includes fluctuations in frequency, amplitude and phase of the signals of voltage and current due to wide spread application of loads with non-linear natures. These include solid-state devices, drives with adjustable speed, energy efficient lamps, power electronic controller operated devices, computers, loads used for processing of data, rectifiers and inverters used in industrial applications as well as power system faults [1]. Further,

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Section: Related Workmentioning

confidence: 99%

Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

Mahela¹,

Shaik

Khan

et al. 2020

IEEE Access

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“…These power converters provide fast response capability and effective filtering against power disturbances [6][7][8]. However, compared to the study on power quality (PQ) factors of AC power systems, many PQ issues have not been explicitly resolved or studied [9][10][11], including harmonics, interharmonics, sag, swell, interruptions, transients, and notch, which are mainly caused by load changes, switching phenomena, power electronic equipment, transformer charging, non-linear loads and environmental factors [12]. In order to ensure reliable, secure and quality supply of power, it has it has become an urgent task for distribution system operator to continuously monitor these disturbances.…”

Section: Introductionmentioning

confidence: 99%

Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Luo

et al. 2020

Energies

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Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.

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“…In [19], the authors developed a method for evaluating PQDs by utilizing EMD and the Hilbert transform. Nevertheless, EMD is sensitive to noise and sampling and it suffers from the problems of recursive calculation and mode mixing [20]. Dragomiretskiy and Zosso proposed a more robust decomposition technique, named variational mode decomposition (VMD), to overcome the drawbacks of EMD [21].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Zhu

Pan

et al. 2019

Applied Sciences

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Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

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A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

Cited by 28 publications

References 28 publications

Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

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