Power transformer disturbance classification based on the wavelet transform and artificial neural networks

Fernandes, Jessika Fonseca; Medeiros, Rodrigo Prado de

doi:10.1109/ijcnn.2016.7727260

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…Otherwise, the relay issues trip/no trip action based on restrain and differential membership function. In [17], maximal overlap discrete wavelet transform is used with two ANN networks. A spectral energy of the sliding window is calculated first and compared then to some threshold as a disturbance detector.…”

Section: Introductionmentioning

confidence: 99%

Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques

Khatib

Arar

2020

Mathematical Problems in Engineering

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In this research, a differential protection technique for a power transformer is proposed by using random forest and boosting learning machines. The proposed learning machines aim to provide a protection expert system that distinguishes between different transformer status which are normal, inrush, overexcitation, CT saturation, or internal fault. Data for 20 different transformers with 5 operating cases are used in this research. The utilized random forest and boosting techniques are trained using these data. Meanwhile, the proposed models are validated by other measures such as out-of-bag error and confusion matrix. In addition, variable importance analysis that shows signal’s component importance inside a transformer at different instances is provided. According to the result, the proposed random forest model successfully identifies all of the current cases (100% accuracy for the conducted experiment). Meanwhile, it is found that it is less accurate as a conditional monitoring element with accuracy in the range of 97%–98%. On the other hand, the proposed boosting model identifies all of the currents for both cases (100% accuracy for the conducted experiment). In addition to that, a comparison is conducted between the proposed models and other AI-based models. Based on this comparison, the proposed boosting model is the simplest and the most accurate model as compared to other models.

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Section: Introductionmentioning

confidence: 99%

Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques

Khatib

Arar

2020

Mathematical Problems in Engineering

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Power System Fault Detection and Classification Using Wavelet Transform and Artificial Neural Networks

Malla

Coburn

Keegan

et al. 2019

Advances in Neural Networks – ISNN 2019

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Winding-to-ground fault location in power transformer windings using combination of discrete wavelet transform and back-propagation neural network

Chiradeja

Ngaopitakkul

2022

Sci Rep

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Power transformers are important equipment in power systems and require a responsive and accurate protection system to ensure system reliability. In this paper, a fault location algorithm for power transformers based on the discrete wavelet transform and back-propagation neural network is presented. The system is modelled on part of Thailand’s transmission and distribution system. The ATP/EMTP software is used to simulate fault signals to validate the proposed algorithm, and the performance is evaluated under various conditions. In addition, various activation functions in the hidden and output layers are compared to select suitable functions for the algorithm. Test results show that the proposed algorithm can correctly locate faults on the transformer winding under different conditions with an average error of less than 0.1%. This result demonstrates the feasibility of implementing the proposed algorithm in actual protection systems for power transformers.

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Power transformer disturbance classification based on the wavelet transform and artificial neural networks

Cited by 8 publications

References 12 publications

Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques

Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques

Power System Fault Detection and Classification Using Wavelet Transform and Artificial Neural Networks

Winding-to-ground fault location in power transformer windings using combination of discrete wavelet transform and back-propagation neural network

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