Classification of Power Quality Disturbances Using Wavelets and Support Vector Machine

Taskovski, Dimitar; Milchevski, Aleksandar; Kostadinov, Dimce

doi:10.5755/j01.eee.19.2.1213

Cited by 4 publications

(5 citation statements)

References 17 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The author have used discrete wavelet transform and performed the classification using support vector machine. Similar line of research has also been carried out by Milchevski et al [23]. Using the scheme of treebased support vector machine (Figure 5 Usage of decision tree and support vector machine was also seen in the study of Ray et al [24].…”

Section: Support Vector Machinesupporting

confidence: 59%

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

Vighneshwari¹,

Neela²

2016

IJECE

View full text Add to dashboard Cite

The growing demands of global consumer market in green energy system have opened the doors for many technologies as well as various sophisticated electrical devices for both commercial and domestic usage. However, with the increasing demands of energy and better quality of services, there is a significant increase in non-linearity in load distribution causing potential effect on the Power Quality (PQ). The harmful effects on PQ are various events e.g. sag, swell, harmonics etc that causes significant amount of system degradation. Therefore, this paper discusses various significant research techniques pertaining to the PQ disturbance classification system introduced by the authors in the past and analyzes its effectiveness scale in terms of research gap. The paper discusses some of the frequently exercised PQ classification techniques from the most relevant literatures in order to have more insights of the techniques.

show abstract

Section: Support Vector Machinesupporting

confidence: 59%

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

Vighneshwari¹,

Neela²

2016

IJECE

View full text Add to dashboard Cite

show abstract

Section: Support Vector Machinesupporting

confidence: 59%

“…Similarly, study conducted towards usage of support vector machine has similar work e.g. Kocaman et al [22] and Milchevski et al [23]. Likewise, Study conducted by Parizi et al [44], Majhi et al [36] have originally used by Nayak and Dash [33].…”

Section: Less Novelty In Approachesmentioning

confidence: 92%

“…Our investigation shows that 95% of the studies conducted till date in PQ classification are not benchmarked. Few to name are study of Monedero et al [10], Dastfan and Zadeh [11], Mishra et al [12], Manjula and Sarma [14], Lin et al [21], Kocaman et al [22], Milchevski et al [23], Naderian and Salemnia [25], Suja and Jerome [27], Nath and Mishra [30], Nayak and Dash [33], Biswal et al [34] etc.…”

Section: Fewer Standard Outcomesmentioning

confidence: 99%

See 1 more Smart Citation

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

Vighneshwari¹,

Neela²

2016

IJECE

View full text Add to dashboard Cite

<p>The growing demands of global consumer market in green energy system have opened the doors for many technologies as well as various sophisticated electrical devices for both commercial and domestic usage. However, with the increasing demands of energy and better quality of services, there is a significant increase in non-linearity in load distribution causing potential effect on the Power Quality (PQ). The harmful effects on PQ are various events e.g. sag, swell, harmonics etc that causes significant amount of system degradation. Therefore, this paper discusses various significant research techniques pertaining to the PQ disturbance classification system introduced by the authors in the past and analyzes its effectiveness scale in terms of research gap. The paper discusses some of the frequently exercised PQ classification techniques from the most relevant literatures in order to have more insights of the techniques.</p>

show abstract

“…[84] combines wavelet packet energy and multiclass SVM (MSVM) outperforming ANN classifiers. [85] employs two discrete WT with different wavelet filters for feature extraction and SVM for classification of PQDs. [86] suggests a method for PQD identification and classification using Independent Component Analysis (ICA) and SVM.…”

Section: Classification Of Pqds Using Svm Related Workmentioning

confidence: 99%

Review on Power Quality Disturbances Detection, Classification, Optimization and Mitigation Techniques

Khaldi,

Dekhandji,

Recioui

2024

Preprint

View full text Add to dashboard Cite

Power quality (PQ) plays an important role in ensuring the smooth operation of power systems. PQ disturbances may cause significant consequences, impacting the reliability and efficiency of power networks. This paper presents a comprehensive review of PQ within smart grids and the integration of renewable energy sources. Methods of detecting, classifying, optimizing, and mitigating PQ events are reviewed with providing a brief theoretical explanation for each technique. Related work done in the past two decades, was conducted, presenting a clear view of the development of research in a chronological order serving as a guide for future investigations in this field of study.

show abstract

Classification of Power Quality Disturbances Using Wavelets and Support Vector Machine

Cited by 4 publications

References 17 publications

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

Review on Power Quality Disturbances Detection, Classification, Optimization and Mitigation Techniques

Contact Info

Product

Resources

About