A novel analytic method of power quality using extension genetic algorithm and wavelet transform

Wang, Menghui; Tseng, Yi-Feng

doi:10.1016/j.eswa.2011.04.032

Cited by 34 publications

(14 citation statements)

References 11 publications

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“…The two critical issues namely the selection of the most suitable features and the estimation of the best SVM kernel parameters are addressed through a classification system by using GA and simulated annealing (SA) optimization techniques. A combination of the extension theory and genetic algorithm known as Extension GA (EGA) was proposed in [161]. The extension theory provides a means for distance measurement in the classification process whereas the GA has the ability to search for an optimal solution within a wide space.…”

Section: Feature Selection and Parameter Optimization Techniquesmentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

228

111

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Section: Feature Selection and Parameter Optimization Techniquesmentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

228

111

View full text Add to dashboard Cite

“…Beside time domain analysis, flicker analysis in fre quency domain has been developed. Fast Fourier Transform (FFT) [13], S transform [14] and wavelet transform [15], which are time frequency domain methods, have been applied to evaluate the voltage flicker. Application of these approaches is time cost ing and includes some practical problems.…”

Section: Introductionmentioning

confidence: 99%

K-means clustering and correlation coefficient based methods for detection of flicker sources in non-radial power system

Dejamkhooy

Dastfan

Ahmadifard

2014

Russ. Electr. Engin.

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Nowadays electric power quality problems such as flicker (voltage fluctuation) are major concern of electric companies and industrial consumers. Identification of flicker sources is an important stage in cus tomers convincing and in flicker reduction process. For equal fluctuation frequency of flicker sources, detec tion of coupling points of these sources is not an easy task. In this paper, voltage envelope is extracted by Enhanced Phase Locked Loop (EPLL), which acts as a nonlinear adaptive filter, and then the amplitude of flicker tone is used as a flicker index. The power system with arbitrary flicker sources is assumed and simulated to generate a set of training data. Then two methods based on K means clustering algorithm and correlation coefficient are introduced to identify the location of flicker sources in a non radial power system. Using pro posed methods location of flicker sources with similar fluctuation frequency in non radial power system is determined. In a typical test system, several simulations have been conducted. The simulation results show the performance of the proposed method is very promising and flicker sources can be detected tolerably.

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“…To address the limitations of the STFT method, Yilmaz et al [10] proposed a lifting-based wavelet transform method for power quality analysis, in which the window width was optimized in such a way as to provide a higher time-domain resolution for the high frequency part of the signal and a higher frequency-domain resolution for the lower frequency part of the signal. Many researchers have used the wavelet transform method to extract the fundamental characteristics of the power signal for subsequent power disturbance event classification by means of artificial intelligence (AI) algorithms such as Neural Networks (NNs) [11], Fuzzy Theoretic (FT) schemes [12], and Genetic Algorithms (GAs) [13]. However, the wavelet transformation process not only yields a large number of characteristic features, but also has poor robustness to noise.…”

Section: Introductionmentioning

confidence: 99%

New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network

Wang

Yau

2014

Energies

Self Cite

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Abstract:A hybrid method comprising a chaos synchronization (CS)-based detection scheme and an Extension Neural Network (ENN) classification algorithm is proposed for power quality monitoring and analysis. The new method can detect minor changes in signals of the power systems. Likewise, prominent characteristics of system signal disturbance can be extracted by this technique. In the proposed approach, the CS-based detection method is used to extract three fundamental characteristics of the power system signal and an ENN-based clustering scheme is then applied to detect the state of the signal, i.e., normal, voltage sag, voltage swell, interruption or harmonics. The validity of the proposed method is demonstrated by means of simulations given the use of three different chaotic systems, namely Lorenz, New Lorenz and Sprott. The simulation results show that the proposed method achieves a high detection accuracy irrespective of the chaotic system used or the presence of noise. The proposed method not only achieves higher detection accuracy than existing methods, but also has low computational cost, an improved robustness toward noise, and improved scalability. As a result, it provides an ideal solution for the future development of hand-held power quality analyzers and real-time detection devices.

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A novel analytic method of power quality using extension genetic algorithm and wavelet transform

Cited by 34 publications

References 11 publications

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

K-means clustering and correlation coefficient based methods for detection of flicker sources in non-radial power system

New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network

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