Analysis of Multi-Disturbance Power Signals on Compression: A Wavelet Based Approach

Jeevitha, S.; Mabel, M. Carolin

doi:10.1166/jctn.2018.7095

Cited by 2 publications

(2 citation statements)

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“…In this way down sampling is applied for further levels of wave decomposition [19,21]. This process is largely termed as multi-level decomposition [22]. The filter output relations are mathematically expressed in equation 4 and 5, where k represents number of samples.…”

Section: ) Multi-resolution Analysismentioning

confidence: 99%

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

Jandan¹,

Khokhar²,

Shaha³

et al. 2019

ijacsa

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Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable.

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Section: ) Multi-resolution Analysismentioning

confidence: 99%

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

Jandan¹,

Khokhar²,

Shaha³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…feature extraction, feature selection, classification) have been listed and compared amongst each other. There are two types of approaches found in the literature; one is time-domain based techniques like sliding window, dV/dt, root mean square value (Axelberg et al, 2007; Styvaktakis et al, 2002), higher order statistics (Perez et al, 2011), envelope analysis, Kalman filter (Khoa and Tung, 2018; Xi et al, 2018) and so forth; another type is frequency domain based techniques like fast Fourier transform, wavelet transform (WT) (Jeevitha et al, 2018), Hilbert transform (Biswal et al, 2018; Sahani and Dash, 2018), Goertzel algorithm (Najafi et al, 2018) and so forth.…”

Section: Introductionmentioning

confidence: 99%

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Saini

Kapoor

Beniwal

et al. 2019

Transactions of the Institute of Measurement and Control

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In this work, a new fractionally delayed biorthogonal wavelet is designed for recognition of voltage sag causes. This work addresses the classification of voltage sag causes into three categories, i.e., fault events (namely line-to-ground fault, line-to-line fault, double-line-to-ground fault and symmetrical fault), induction motor starting and transformer energization. Fractionally delayed biorthogonal Coiflet wavelet of order 2 (named as Coiflet fraclet) is designed using Lagrange interpolation and employed for the multiresolution analysis of voltage sag signals. To achieve higher classification accuracy, event information is derived from both time-frequency domain and frequency domain. Frequency information is obtained by applying multiple signal classification (MUSIC) algorithm on voltage sag signals. Both the time-frequency domain and frequency domain features are concatenated to implement the feature level fusion for strengthening the feature set. The feature set obtained after feature level fusion is used for training the recursive reduced kernel based extreme learning machine which addresses the problem of highly complex structure due to huge dataset of large dimensions. The classifier has shown robustness to the presence of noise also and classified the voltage sag causes with high accuracy.

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Analysis of Multi-Disturbance Power Signals on Compression: A Wavelet Based Approach

Cited by 2 publications

References 0 publications

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

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