A measurement method based on the wavelet transform for power quality analysis

Angrisani, L.; Daponte, P.; D’Apuzzo, Massimo; Testa, A.

doi:10.1109/61.714415

Cited by 264 publications

(89 citation statements)

References 14 publications

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“…The concept of wavelet is proposed by a French engineer J. Morlet in 1974 [30]. By comparing the Fourier transform, wavelet transform, partial transformation of space (time) and frequency, the dilation and translation operation functions are used to analyze the functions or signals to solve these difficult problems that cannot be solved by Fourier.…”

Section: Wavelet Transformmentioning

confidence: 99%

Study on a New Remote Sensing Image Classification Method and Its Application

Deng¹,

Wang²,

Zhao³

2018

PIER C

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Abstract-For slower computation speed and lower classification accuracy of the traditional image classification methods, wavelet transform, multi-strategy, particle swarm optimization (PSO) algorithm and support vector machine (SVM) are introduced into image classification in order to propose a new remote sensing image classification (RIWMPS) method. First of all, wavelet transform method with multi-resolution characteristics is used to extract the features of remote sensing image. Then the steepest descent strategy, corrective decline strategy, random movement, aggregation strategy and diffusion strategy are used to improve the PSO algorithm to obtain an improved PSO (MSPSO) algorithm, which is used to optimize the parameters of the SVM model in order to construct an optimized SVM classifier for realizing remote sensing classification. Finally, the remote sensing image of Chongming Island is select to test the effectiveness of the RIWMPS method. The experiment results show that the RIWMPS method has higher classification efficiency and accuracy, and takes on better superiority and effectiveness. This study provides a new classification method for processing the remote sensing image.

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Section: Wavelet Transformmentioning

confidence: 99%

Study on a New Remote Sensing Image Classification Method and Its Application

Deng¹,

Wang²,

Zhao³

2018

PIER C

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“…It was demonstrated by (Mo et al, 1997) how to extract features from wavelet transform coefficients at different scales that can be applied as input to neural networks for classification of non stationary signal type. Angrisani et al (1998) proposed to employ disturbances time duration estimated by continuous wavelet transform (CWT) and disturbances amplitude estimated by DWT to classify transient disturbance type. To extract the squared wavelet transform coefficients at each scale as inputs to neural networks for classification of the disturbances have been proposed in (Santoso et al, 1996;Santoso et al, 2000c;Gaouda et al, 1999).…”

Section: Artificial Neural Network Based Classifiersmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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“…Wavelet exhibits its notable capabilities for detection and localization the power disturbances [1][2]. In order to identify the type of PQ disturbance more effectively, several authors have presented different methodologies based on the combination of wavelet transform (WT) and artificial neural network (ANN) [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An integrated approach of Wavelet-Rough Set technique for classification of power quality disturbances

Bhende

Mishra

2008

2008 13th International Conference on Harmonics and Quality of Power

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This paper presents an integrated approach of wavelet and rough set theory for the classification of power quality (PQ) disturbances. Further, the number of features and the rules required for proper classification are decided through rough set approach. Moreover, as the proposed methodology can reduce the number of features extracted through wavelet to a great extent, it will indirectly reduce the memory requirement for the classification procedure. Eleven types of PQ disturbances are considered for classification. The simulation results show that the combination of wavelet and rough set theory can effectively classify different power quality disturbances. Since rule based approach is easy to understand and simple to implement, the rough set technique is a good candidate for the classification of PQ disturbances.

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A measurement method based on the wavelet transform for power quality analysis

Cited by 264 publications

References 14 publications

Study on a New Remote Sensing Image Classification Method and Its Application

Study on a New Remote Sensing Image Classification Method and Its Application

Power quality event classification: an overview and key issues

An integrated approach of Wavelet-Rough Set technique for classification of power quality disturbances

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