Transformer differential protection using wavelet transform

Özgönenel, Okan; Karagöl, Serap

doi:10.1016/j.epsr.2014.04.008

Cited by 42 publications

(26 citation statements)

References 15 publications

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“…A survey of pertinent literature reveals that most publications on this topic can be classified in two main groups. Ozgonenel et al [2], Subramanian et al [3] and Ozgonenel and Onbilgin [4] presented a method to distinguish the inrush current from internal fault currents in order to differentiate protection of power transformer. Some of the related studies [5 -8], employing the transmission line method, have presented a transformer model for identification of incipient and internal faults [such as turn-toturn short circuit (SC) faults].…”

Section: Introductionmentioning

confidence: 99%

Transformer winding faults classification based on transfer function analysis by support vector machine

Bigdeli

Vakilian

Rahimpour³

2012

IET Electr. Power Appl.

125

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This study presents an intelligent fault classification method for identification of transformer winding fault through transfer function (TF) analysis. For this analysis support vector machine (SVM) is used. The required data for training and testing of SVM are obtained by measurement on two groups of transformers (one is a classic 20 kV transformer and the other is a model transformer) under intact condition and under different fault conditions (axial displacement, radial deformation, disc space variation and short circuit of winding). Two different features extracted from the measured TFs are then used as the inputs to SVM classifier for fault classification. The accuracy of proposed method is compared with the accuracy of past well-known works. This comparison indicates that the proposed method can be used as a reliable method for transformer winding fault recognition.

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Section: Introductionmentioning

confidence: 99%

Transformer winding faults classification based on transfer function analysis by support vector machine

Bigdeli

Vakilian

Rahimpour³

2012

IET Electr. Power Appl.

125

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“…Discrete wavelet transform (DWT) and wavelet coefficients: DWT is a spectral analysis technique used in the analysis of nonstationary signals [7,29]. It provides time-frequency representations of the signals.…”

Section: Feature Name Activitymentioning

confidence: 99%

“…Generally, EEG comprises 4 main wave types. These are alpha waves (8)(9)(10)(11)(12)(13), beta waves (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), delta waves (0-4 Hz), and theta waves (4-7 Hz) [2]. In EEG signals, these wave types show changes in pathological situations.…”

Section: Introductionmentioning

confidence: 99%

Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

Şen¹,

Peker²

2013

Turk J Elec Eng & Comp Sci

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This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms.In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

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“…A new algorithm based on Clarke's transform and Discrete Wavelet Transform (DWT) [4] was applied on power transformer to differentiate between external and internal faults. Entropy approach integrated with Artificial Neural Network [5] resulted in the efficient discrimination between inrush and internal faults in an intelligent based network monitoring system.…”

Section: Introductionmentioning

confidence: 99%

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Gopila¹,

Gnanambal²

2016

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Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequencybased Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.

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Transformer differential protection using wavelet transform

Cited by 42 publications

References 15 publications

Transformer winding faults classification based on transfer function analysis by support vector machine

Transformer winding faults classification based on transfer function analysis by support vector machine

Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

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