Application of Wavelet Technique for Fault Classification in Transmission Systems

Prasad, Avagaddi; Edward, J. Belwin

doi:10.1016/j.procs.2016.07.326

Cited by 31 publications

(9 citation statements)

References 15 publications

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“…In this section, we will discuss some of the approaches used to protect transmission line which includes: i) distance relay approach [12], [13]; ii) wavelet approach [14], [15]; iii) the artificial neural network (ANN) approach [16]- [22], iv) fuzzy logic approach [23], [24], and v) mobile robot approach [25]- [28]. Other techniques are the hybrid method, machine learning and deep learning technique: i) neurofuzzy technique [24], [29]- [33], ii) wavelet and ANN technique [34]- [36], iii) wavelet and fuzzy-logic technique [37], [38], iv) wavelet and neuro-fuzzy technique [39], [40], v) machine learning approach [15], [41], [42], vi) support vector machine (SVM) [43], vii) k-nearest neighbours (KNN) and decision tree (DT) [44], and viii) principal component analysis (PCA) [45].…”

Section: Relevant Research On Protection Of Transmission Linementioning

confidence: 99%

Protection for 330 kV transmission line and recommendation for Nigerian transmission system: a review

Ogar

Gamage

Hussain

2022

IJECE

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The transmission line is an integral part of the electrical power system; however, a fault has a negative impact on the system, like blackout, power loss, financial losses, and socio-economic impact. This fault occurs due to ageing conductors, lightning stroke, switching surge and human interference. We reviewed the protection scheme implemented in the Nigerian transmission network, which has challenges relating to the environment's terrain and a long-distance transmission line of about 20,000 km. The different approach of fault classification, detection and location was analysed and critically summarised. This review paper proposes a hybrid Artificial Neural Network and distance protection scheme that can automatically identify, locate, isolate, predict, correct faults, and real-time monitor and control the entire network. It can also detect the shortest possible trip time of 0.02 s and 0.03 s of line current and fault losses, respectively, during fault to avert damage on the line. However, this method has its challenges, such as the volume of data generated from load flow analysis, training time, and the total distance covered by the network. However, these can be averted by segmenting the entire network for easy evaluation and monitoring to achieve set goals.

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Section: Relevant Research On Protection Of Transmission Linementioning

confidence: 99%

Protection for 330 kV transmission line and recommendation for Nigerian transmission system: a review

Ogar

Gamage

Hussain

2022

IJECE

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“…It can highlight mutative components of the processed signals by flexibly changing the window of the time-frequency domain, and can then extract the power cable fault information effectively [18][19][20][21]. After comparing the wavelet modulus difference of the target traveling wave from the two ends, the fault type can be recognized [22]. However, this method fails to select the faulty phases of two-phase grounded faults.…”

Section: Description Of Short-circuit Fault Components In Online Powementioning

confidence: 99%

Fault Sensing Using Fractal Dimension and Wavelet

2016

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Abstract:A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method.

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“…Also, various methods have been presented for transient stability improvement of the recent power systems [16]. A wavelet transform approach has been used to discriminate the stability of power system enhanced in both uncompensated and compensated systems [17]. Also wavelet based on Clarke's transformation is used to obtain the fault current as a new algorithm for fault location and classification [18].…”

Section: Introductionmentioning

confidence: 99%

Improvement of protection relay with a single phase autore-closing mechanism based on artificial neural network

Hussain¹,

Ali²,

Allu³

et al. 2020

IJPEDS

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This paper presents a developed logical tripping scheme to improve conventional protection performance. Adaptive single pole auto reclosure (ASPAR) system is proposed that considers, automatically tripping and reclosing of a multi-shot independent pole technique of a circuit breaker at a predetermined sequence, which can be used to boost the synchronization of the power grid under the transient fault conditions. Moreover, the ASPAR can be utilized to enhance the electrical system stability and reliability at the same operating conditions. Based on the three-phase system, the Artificial neural network (ANN) in this work has been done in order to diagnose and detect healthy and faulted phases. The proposed ANN fault classifier method consists of the logic gates, router circuits, timers, and positive and negative sequence analyses circuit. In addition, it is used to give the ability to recognize a fault type, which by training on the sequence angle values and coordination of the transmission line. Three-phase overhead transmission line including the proposed ASPAR is built in MATLA \SIMULINK environment. Thus the performance ANN-fault classified is tested under different fault conditions. Simulation results show that the proposed ASPAR based on ANN is accurate and well performance. Whereas resultant tripping and reclosing signals of ASPAR are successfully provided that enhances the circuit breaker mechanism under these operating condition.

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Application of Wavelet Technique for Fault Classification in Transmission Systems

Cited by 31 publications

References 15 publications

Protection for 330 kV transmission line and recommendation for Nigerian transmission system: a review

Protection for 330 kV transmission line and recommendation for Nigerian transmission system: a review

Fault Sensing Using Fractal Dimension and Wavelet

Improvement of protection relay with a single phase autore-closing mechanism based on artificial neural network

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