A deep learning framework using convolution neural network for classification of impulse fault patterns in transformers with increased accuracy

Dey, Debangshu; Chatterjee, Biswendu; Dalai, Sovan; Munshi, Sugata; Chakravorti, S.

doi:10.1109/tdei.2017.006793

Cited by 62 publications

(22 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nesta mesma linha, alguns trabalhos utilizam redes LSTM combinadas a outras técnicas como, Support Vector Machine (SVM) (Zhang et al, 2017) e Convolutional Neural Networks (CNN) (He et al, 2019;Dey et al, 2017). Em Li et al (2018), uma rede neural LSTM foi utilizada em conjunto com a técnica de processamento de sinais Wavelet Transform (WT) (Li et al, 2018).…”

Section: Trabalhos Baseados Em Rede Neural Para Detecção De Falhasunclassified

Identificação de Falhas de uma Planta de Nível Piloto Baseada em Rede Neural LSTM

Oliveira

Santos

Nunes

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

A classificação das falhas industriais utilizando abordagens orientadas a dados é um campo crescente e amplamente discutido na literatura. As Redes Neurais Artificiais, em especial as Redes Neurais Recorrentes se demonstraram fortes aliadas a esse tipo de abordagem, por sua capacidade de transportar informações contidas no histórico das séries temporais ao longo dos instantes de tempo. Aqui será avaliada a capacidade de um modelo Long Short Term Memory previamente proposto em classificar corretamente as falhas em dados gerados obtidos de um processo não simulado. Posteriormente, será testada uma variação dos hiperparâmetros com o objetivo de encontrar um conjunto que apresente resultados otimizados. Através dos testes foi obtido uma acurácia final de 91,49% na classificação de falhas de uma planta de nível piloto.

show abstract

Section: Trabalhos Baseados Em Rede Neural Para Detecção De Falhasunclassified

Identificação de Falhas de uma Planta de Nível Piloto Baseada em Rede Neural LSTM

Oliveira

Santos

Nunes

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

show abstract

“…The cross entropy cost function is used for training. Dey et al [25] also used a CNN structure to classify impulse-fault patterns in transformers, but it could also be applied to PD pattern classification. The input data for this CNN structure was time-series data instead of an image and it was tested for single and multiple faults (i.e., simultaneously occurring at two different winding locations).…”

Section: Time-domain Waveform Datamentioning

confidence: 99%

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Barrios¹,

Buldain

Comech

et al. 2019

Energies

View full text Add to dashboard Cite

This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure.

show abstract

“…Hence, it is necessary to establish complex nonlinear relationships between the dissolved gas concentration and transformer faults with some artificial intelligent technologies. AI (Artificial Intelligence) technologies have been widely applied in recent years due to the advantages of continuous learning and timely updating [30][31][32][33][34][35][36][37][38][39][40]. The AI technologies such as clustering analysis [22], fuzzy logic approach [31], neural network algorithm [32][33][34], and SVM [35,36] have shed lights on transformer fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier

et al. 2018

View full text Add to dashboard Cite

Dissolved gas analysis (DGA) is widely used to detect the incipient fault of power transformers. However, the accuracy is greatly limited by selection of DGA features and performance of fault diagnostic model. This paper proposed a fault diagnostic method integrating feature selection and diagnostic model optimization. Firstly, this paper set up three feature sets with eight basic DGA gases, 28 DGA gas ratios and 36 hybrid DGA features, respectively. Then, to eliminate the interference of weak-relevant and irrelevant features, the genetic-algorithm-SVM-feature-screen (GA-SVM-FS) model was built to screen out the optimal hybrid DGA features subset (OHFS) from three feature sets. Next, using the OHFS as the input, the support vector machine (SVM) multi-classifier optimized by ISGOSVM (SVM classifier optimized by improved social group optimization) was built to diagnose fault types of transformers. Finally, the performance of OHFS and ISGOSVM diagnostic model was tested and compared with traditional DGA features and diagnostic models, respectively. The results show that the OHFS screened out is comprised of 14 features, including 12 gas ratios and two gases. The accuracy of OHFS is 3–30% higher than traditional DGA features, and the accuracy of ISGOSVM can increase by 3% to 14% compared with the SGOSVM (SVM classifier optimized by social group optimization), GASVM (SVM classifier optimized by genetic algorithm optimization), PSOSVM (SVM classifier optimized by particle swarm optimization), and SVM diagnostic models. The proposed approach integrating the OHFS with ISGOSVM achieves the highest accuracy of fault diagnose (92.86%).

show abstract

A deep learning framework using convolution neural network for classification of impulse fault patterns in transformers with increased accuracy

Cited by 62 publications

References 12 publications

Identificação de Falhas de uma Planta de Nível Piloto Baseada em Rede Neural LSTM

Identificação de Falhas de uma Planta de Nível Piloto Baseada em Rede Neural LSTM

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier

Contact Info

Product

Resources

About