Faults detection and diagnosis of transmission lines using wavelet transformed based technique

Masood, Bilal; Saleem, Umar; Anjum, Maria; Arshad, Usman

doi:10.1109/aeect.2017.8257776

Cited by 15 publications

(9 citation statements)

References 19 publications

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“…In addition, the capacity of wavelets, which is to separate minor characteristics in a signal, is a key advantage. Wavelet transforms are decimated and undecimated operations depending on the type of WT, and WTs can extract local spectral and temporal information simultaneously [26]. Based on operation types, a WT can be considered redundant, which is a property that is not well approved due to its functions, but from another point of view, it is a popular and good method for small defects [14].…”

Section: Mathematic Backgroundmentioning

confidence: 99%

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: Mathematic Backgroundmentioning

confidence: 99%

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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“…Moreover, it produces better spatial and spectral localization of signal. In recent decades, such advanced powerful tool has been used for designing the protective relays [17,[18][19][20][21][22][23][24]. In DWT, the fault current signal x (t) is decomposed into low and high frequency components such as approximation (A) and detailed coefficients (D) which is mathematically expressed in (1) and (2) for decomposed signal [25][26].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Transmission line fault identification and classification with integrated FACTS device using multiresolution analysis and naïve bayes classifier

Aker

Othman

Aris

et al. 2020

IJPEDS

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This paper is present a novel approach for solving the pending under-reach problem encountered by distance relay protection scheme in the 3rd zones protection coverage for a midpoint STATCOM compensated transmission lines. The propose transmission line model is develop in Matlab for analyzed feature extraction using Discrete Wavelet multiresolution analysis approach. Extracted feature from standard deviation and entropy energy contents of SLG transient faults current at location beyond the integrated STATCOM used for machine learning algorithm model building using WEKA software. The Naïve Bayes classifier model perform best with robustness prediction and detection of faults with quick convergence even with less training data. The outperformance of the proposed classifier has been 100 % for the relay algorithm modification for under-reach problem elimination in 3rd zones protection coverage.

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“…They are possibly accessible free of cost and are effective if the power-transformation mechanism is improved [1]. The demand for electrical energy is constantly growing because of population growth and the Industrial Revolution [2]. The power-production industry suffers various problems like the continuous supply of energy at lower costs.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Evaluation of SMC-Based DC–DC Converters for Microgrid Applications

et al. 2023

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In recent times, DC microgrids (MGs) have received significant attention due to environmental concerns and the demand for clean energies. Energy storage systems (ESSs) and photovoltaic (PV) systems are parts of DC MGs. This paper expands on the modeling and control of non-isolated, non-inverting four-switch buck-boost (FSBB) synchronous converters, which interface with a wide range of low-power electronic appliances. The proposed power converter can work efficiently both independently and in DC MGs. The charging and discharging of the battery are analyzed using the FSBB converter at a steady state in continuous conduction mode (CCM). A boost converter is connected to a PV system, which is then connected in parallel to the battery to provide voltages at the DC bus. Finally, another FSBB converter is connected to a resistive load that successfully performs the boost-and-buck operation with smooth transitions. Since these power converters possess uncertainties and non-linearities, it is not suitable to design linear controllers for these systems. Therefore, the controlling mechanism for these converters’ operation is based on the sliding mode control (SMC). In this study, various macro-level interests were achieved using SMC. The MATLAB Simulink results successfully prove the precise reference tracking and robust stability in different operating modes of DC–DC converters in a MG structure.

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Faults detection and diagnosis of transmission lines using wavelet transformed based technique

Cited by 15 publications

References 19 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

Transmission line fault identification and classification with integrated FACTS device using multiresolution analysis and naïve bayes classifier

Design and Performance Evaluation of SMC-Based DC–DC Converters for Microgrid Applications

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