Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine

Asman, Saidatul Habsah; Abidin, Ahmad Farid; Yusoh, Mohd Abdul Talib Mat; Subiyanto, Subiyanto

doi:10.1016/j.rineng.2021.100311

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…Using db1, which is similar to a Haar wavelet, the reconstruction signal and original signal have zero shifting. However, the order of Daubechies was determined by calculating the shifting amount of the signal, even though, in the literature, some researchers suggested that the Daubechies wavelet function types db8 and db4 are good for signal de-noising and fault detection problems [3,29,45]. Hence, it is observed from Figure 8 that for each sub-figure, a high frequency is shown at approximately 0.1 sec, which was the time disconnection of the short-circuit block simulation.…”

Section: Simulation Results On Predictive Fault Detectionmentioning

confidence: 99%

“…For the redundancy ratio as defined in Equation (7), RR is approximately determined as 0.36.Using db1, which is similar to a Haar wavelet, the reconstruction signal and original signal have zero shifting. However, the order of Daubechies was determined by calculating the shifting amount of the signal, even though, in the literature, some researchers suggested that the Daubechies wavelet function types db8 and db4 are good for signal de-noising and fault detection problems[3,29,45].…”

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confidence: 99%

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Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

Nasser Mohamed,

Seker,

Akinci

2023

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The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

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Section: Simulation Results On Predictive Fault Detectionmentioning

confidence: 99%

mentioning

confidence: 99%

Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

Nasser Mohamed,

Seker,

Akinci

2023

Information

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“…In [32], a technique based on SVM was introduced to detect system disturbances and categorize the disturbance waveforms, comparing this approach with the ANN technique. A more recent investigation employs SVMs to detect and categorize transient overvoltages while mitigating the distortion occurring at signal start and end, which can hinder data processing [33]. Estimation of peak transient overvoltages is explored in [34] using the ANN method.…”

Section: Characteristics Of Equipment and Components For Trvmentioning

confidence: 99%

A Comprehensive Review on Transient Recovery Voltage in Power Systems: Models, Standardizations and Analysis

Lazzari,

de Morais,

Ramos

et al. 2023

Energies

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Electrical power systems are exposed to transient disturbances that change the voltage and current signals of the network, which can interrupt power and damage equipment. In high-frequency phenomena, it is essential to study the transient recovery voltage (TRV) to ensure the electrical insulation limits of circuit breakers are not violated, thus leading to a safe and reliable operation. Adequate models are crucial to achieving satisfactory results in the studies, according to the range of frequency of the transient being evaluated. This paper presents a comprehensive literature review of methods and models for studying electromagnetic transients, focusing on TRV requests imposed on circuit breakers, in addition to fault-clearing simulations on real system modeling. The analyses are fundamental both for the evaluation of the amplitude of the voltage signal and for its rate of rise. We also compare the reviewed models and techniques to provide a handy resource for researchers.

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“…Signal processing analysis without considering border distortion can produce inaccurate detection of PQ disturbance. However, the researchers focused only on single PQ disturbance [16], [18]. So, the major aim of the study is to minimize border distortion occurred in multiple PQ disturbances.…”

Section: Introductionmentioning

confidence: 99%

Border Distortion Minimization for Multiple PQ Disturbances - Stockwell Transformation Modification

Marzuki,

Bin-Abidin,

Bin-Jusoh

et al. 2023

IJCDS

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Signal processing is a method used in electrical engineering to analyse and modify signals hence improving the quality. In power quality classification, signal processing is employed as an alternative for better detection of disturbances. This is done with the application of some extraction features to amplify the differences of each disturbance. However, due to border distortion effect, signal processing results usually encounter problem. Border distortion is the aberration in results due to tempering, appearing at the starting and the ending of the waveform when signals are processed and modified using signal processing techniques. This leads to misclassification of PQ disturbances, causing problems in determining the correct mitigation technique to be taken to reduce power quality disturbances. In this paper, minimization of border distortion of features extraction resulting from Stockwell transform will be discussed via the implementation of Hanning Taper and polynomial fitting. The discussion involves the three types of multiple power quality disturbances which are voltage sag with harmonics, voltage sag with transient, and harmonics with transient. The modification technique in Stockwell transform will be analysed to prove the accuracy and the consistency in features extraction. In this study, the border distortion effect was successfully reduced by an average of 88.704%.

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Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine

Cited by 8 publications

References 29 publications

Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

A Comprehensive Review on Transient Recovery Voltage in Power Systems: Models, Standardizations and Analysis

Border Distortion Minimization for Multiple PQ Disturbances - Stockwell Transformation Modification

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