Disturbance Classification Utilizing Dynamic Time Warping Classifier

Youssef, Abou-Bakr M.; Abdel-Galil, T.K.; El-Saadany, E.F.; Salama, M.M.A.

doi:10.1109/tpwrd.2003.820178

Cited by 77 publications

(33 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [45], WT has been utilized to produce representative feature vectors for each disturbance. The approach is based on inductive learning by using decision trees.…”

Section: Wavelet Transform Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Classification of power quality events – A review

Saini

Kapoor

2012

International Journal of Electrical Power & Energy Systems

184

View full text Add to dashboard Cite

“…In [45], WT has been utilized to produce representative feature vectors for each disturbance. The approach is based on inductive learning by using decision trees.…”

Section: Wavelet Transform Based Methodsmentioning

confidence: 99%

“…This method is having large computation time. In [45], authors suggested a framework based on Walsh transform and Fast Fourier transform (FFT) as features and the dynamic time warping algorithm as classifier. Complexity of [45] is quite high.…”

Section: Miscellaneous Techniquesmentioning

confidence: 99%

Classification of power quality events – A review

Saini

Kapoor

2012

International Journal of Electrical Power & Energy Systems

184

View full text Add to dashboard Cite

“…The main drawback of ANN technique is the need of training cycle and requirement of retraining the entire ANN for every new PQ event (Chung et al, 2002;Youssef et al, 2004). In (Chung et al, 2002), first, a rule-based method has been used to classify time-characterized disturbances, and then, a wavelet method has been utilized to obtain a more flexible time frequency information.…”

Section: Expert System Based Classifiermentioning

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

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.

show abstract

“…The advantage of this technique is its capability to handle easily the noisy data that is present in real-time measurements. However, the main drawback of ANN technique is the need of a large numbers of training cycles and the requirement of retraining the entire ANN for every new PQ event, as demonstrated in [11] and [12].…”

Section: Literature Review On Monitoring and Classification Of Pqmentioning

confidence: 99%