Wavelet-Based Fuzzy Reasoning Approach to Power-Quality Disturbance Recognition

Zhu, Tianhua; Tso, S.K.; Lo, Kueiming

doi:10.1109/tpwrd.2004.832382

Cited by 102 publications

(53 citation statements)

References 14 publications

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“…Expert System Based Classifier Santoso et al, 2000b;Styvaktakis et al, 2001;Styvaktakis et al, 2002;Chung et al, 2002;Reaz et al, 2007. Fuzzy Expert System Based Classifiers Dash et al, 2000Thapar et al, 2003;Zhu et al, 2004;Chilukuri et al, 2004;Ortiz et al, 2006;Bizjak et al, 2006. Adaptive Neuro-Fuzzy System Based Classifier Ibrahim et al, 2001.…”

Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

“…To resolve the fixed resolution problem of STFT, many researchers have proposed the use of the WT approach to analyzing the power system disturbances (Santoso et al, 1996;Perunicic et al,1998;Santoso et al, 2000a;Ibrahim et al, 2002;Zhu et al, 2004;Abdel-Galil et al, 2004). The WT approach prepares a window that automatically adjusts to give proper resolutions of both the time and the frequency.…”

Section: Features Extractionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

Section: Features Extractionmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

show abstract

“…However, in this application, as in (Zhu et al, 2004;Chen & Zhu, 2007), the Daubechie "db4" wavelet was adopted to perform the DWT. As can be seen in (Daubechies, 1992), the "db4" is a wavelet of support four, i.e., each filter has four coefficients, 0123 hhhhh =   .…”

Section: Wwwintechopencommentioning

confidence: 99%

“…Based on this idea, basic rules are generated for the extended fuzzy reasoning. Then, the disturbance waveforms are classified (Zhu et al, 2004). Further on, Chen and Zhu presented a review of the wavelet transform approach used in power quality processing.…”

mentioning

confidence: 99%

Wavelet and PCA to Power Quality Disturbance Classification Applying a RBF Network

Pozzebon¹,

Machado²,

Gomes³

et al. 2011

Power Quality Â€“Â Monitoring, Analysis and Enhancement

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“…Common methods include short-time Fourier transform (STFT) [1], wavelet transform (WT) [2], S-transform (ST) [3], and sparse signal decomposition (SSD) [4]. Nevertheless, STFT has a single time-frequency resolution and does not satisfy the requirement that various disturbance patterns have different time-frequency resolutions in different frequency domains.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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-This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

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Wavelet-Based Fuzzy Reasoning Approach to Power-Quality Disturbance Recognition

Cited by 102 publications

References 14 publications

Power quality event classification: an overview and key issues

Power quality event classification: an overview and key issues

Wavelet and PCA to Power Quality Disturbance Classification Applying a RBF Network

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

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