Obtaining patterns for identification of power quality disturbances using continuous wavelet transform

Gupta, Ritesh; Kumar, Rajesh; Gupta, Manoj

doi:10.1145/1980022.1980106

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Neural networks are good at recognizing patterns and they are extensively applied for the analysis of PQ disturbances [12]- [16]. One of the most basic and well-known architecture in neural networks is the Multiple Layer Preceptorn (MLP).…”

Section: Neural Network Modelmentioning

confidence: 99%

“…Based on the work presented in reference [16], signatures of various power quality disturbances namely sag, swell, transient, harmonics, and flicker were obtained using CWT. It was corroborated that these signatures are unique in shape for a particular type of PQ disturbance and their size is proportional to the amplitude of the PQ disturbance.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Based Indexing and Recognition of Power Quality Disturbances

2011

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AbstrakAnalisis kualitas daya atau power quality (PQ) Kata Kunci: jaringan syaraf tiruan umpan maju, kualitas daya, pengenalan, transformasi wavelet kontinyu Abstract Power quality (PQ) analysis has become imperative for utilities as well as for consumers due to huge cost burden of poor power quality. Accurate recognition of PQ disturbances is still a challenging task, whereas methods for its indexing are not much investigated yet. This paper expounds a system, which includes generation of unique patterns called signatures of various PQ disturbances using continuous wavelet transform (CWT) and recognition of these

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Section: Neural Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Based Indexing and Recognition of Power Quality Disturbances

2011

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Detection of power quality disturbances using symbolic dynamics

Gupta

Kumar

2014

International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014)

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Detection of Power quality (PQ) disturbances has become imperative for utilities as well as for consumers due to huge cost encumbrance of poor power quality and increased sensitivity of electronic equipments towards power quality. It is the first step towards monitoring and analysis of PQ disturbances. This paper proposes a simple and novel method based on symbolic dynamics for detection of PQ disturbances. The proposed method is applied to the synthetic data of PQ disturbances and 100% accurate results are obtained.

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Obtaining patterns for identification of power quality disturbances using continuous wavelet transform

Cited by 2 publications

References 19 publications

Neural Network Based Indexing and Recognition of Power Quality Disturbances

Neural Network Based Indexing and Recognition of Power Quality Disturbances

Detection of power quality disturbances using symbolic dynamics

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