Cross-wavelet transform as a new paradigm for feature extraction from noisy partial discharge pulses

Dey, Debangshu; Chatterjee, Biswendu; Chakravorti, S.; Munshi, Sugata

doi:10.1109/tdei.2010.5412014

Cited by 72 publications

(38 citation statements)

References 17 publications

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“…The reason behind the choice of features, F 1 -F 12 , is that they represent salient features of the cross-wavelet spectrum and gave acceptable classification performance in [28,29]. However, one may choose some other features such as, location of local peaks of |W xy | and φ(s, τ) surfaces, if any, or any other features from |W xy | and φ(s, τ) depending upon the nature of the problem.…”

Section: Analysis Of Recorded Data 41 Xwt Based Feature Extractionmentioning

confidence: 99%

“…A new approach using Cross-wavelet transform (XWT) based analysis for condition monitoring of different engineering systems has been recently developed and applied to health monitoring of high voltage systems [28,29]. Cross-wavelet transform method, which may be considered as an extension of wavelet analysis, gives a measure of correlation between two waveforms in time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

“…In the aforesaid works, some features are extracted from the cross-wavelet spectrum of two waveforms to make a judgment on the conditions of electrical insulation by employing a classification algorithm. Moreover, it has also been established in these works that XWT has an ability to nullify the effect of small random noises present in the data which are often introduced in measured time series during data acquisition [28,29].…”

Section: Introductionmentioning

confidence: 99%

“…As the problem statements in [28,29] are topologically similar to that in condition monitoring of flame in combustion system, the objective of the present work is to explore an XWT aided rule based strategy on for early prediction of lean blowout. The method has been tested using the time series data obtained from a laboratory-scale combustor that can be used as an idealized representation of gas turbine combustors.…”

Section: Introductionmentioning

confidence: 99%

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A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

Dey

Chaudhari

Mukhopadhyay

et al. 2015

International Journal of Spray and Combustion Dynamics

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Lean or ultralean combustion is one of the popular strategies to achieve very low emission levels. However, it is extremely susceptible to lean blow-out (LBO). The present work explores a Crosswavelet transform (XWT) aided rule based scheme for early prediction of lean blowout. XWT can be considered as an advancement of wavelet analysis which gives correlation between two waveforms in time-frequency space. In the present scheme a swirl-stabilized dump combustor is used as a laboratory-scale model of a generic gas turbine combustor with LPG as fuel. Various time series data of CH chemiluminescence signal are recorded for different flame conditions by varying equivalence ratio, flow rate and level of air-fuel premixing. Some features are extracted from the cross-wavelet spectrum of the recorded waveforms and a reference wave. The extracted features are observed to classify the flame condition into three major classes: near LBO, moderate and healthy. Moreover, a Rough Set based technique is also applied on the extracted features to generate a rule base so that it can be fed to a real time controller or expert system to take necessary control action to prevent LBO. Results show that the proposed methodology performs with an acceptable degree of accuracy.

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Section: Analysis Of Recorded Data 41 Xwt Based Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

Dey

Chaudhari

Mukhopadhyay

et al. 2015

International Journal of Spray and Combustion Dynamics

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“…At present, there are neural network, support vector machine and fuzzy logic for identifying partial discharge pattern [1][2][3]. The application of neural network and fuzzy logic is more extensive.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Cable Insulation Defects Based on ANFIS–PCA Method

Cao¹,

Li²,

Gan³

et al. 2017

dtetr

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Abstract. Partial discharge (PD) detection is an effective method to detect the condition of power cable insulation. Since different modes of cable defect have different discharge characteristics, the PD detection method is required to be able to collect PD pulse and identify the type of the defect. In this paper, the statistical parameters of partial discharge are extracted and an ANFIS-PCA-based method for diagnosing cable insulation defects is proposed. The ANFIS system is applied to pattern classification of partial discharge. The experimental results show that the method proposed in this paper can classify and identify the various discharge patterns effectively.

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Advanced Signal Processing Techniques for Partial Discharge Measurement

Saha¹,

Purkait²

2017

Transformer Ageing

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Cross-wavelet transform as a new paradigm for feature extraction from noisy partial discharge pulses

Cited by 72 publications

References 17 publications

A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

Diagnosis of Cable Insulation Defects Based on ANFIS–PCA Method

Advanced Signal Processing Techniques for Partial Discharge Measurement

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