Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network

Dai, Jufeng; Song, Hui; Sheng, Gehao; Jiang, Xiuchen

doi:10.1109/tdei.2017.006727

Cited by 197 publications

(88 citation statements)

References 22 publications

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“…According to the IEC60599 standard, the types of potential transformer faults can be classified into thermal fault of partial discharge (PD), low-energy discharge (LD), and high-energy discharge (HD), low temperature (LT), thermal fault of medium temperature (MT), thermal fault of high temperature (HT) [21]. Thus, the predicted running state of the transformer is divided into 7 (6 + 1) types.…”

Section: Transformer Running Status Analysismentioning

confidence: 99%

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Lin

Yan

et al. 2018

Energies

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It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

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Section: Transformer Running Status Analysismentioning

confidence: 99%

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Lin

Yan

et al. 2018

Energies

Self Cite

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“…Yang et al [15] proposed a time series analysis model based on a long short-term memory neural network to make fault predictions for electro-mechanical actuators. Dai et al [16] proposed a multisource information fusion model based on a deep belief network to perform fault detection analyses on a power transformer. Jiang et al [17] proposed a multifeature fusion method for stacked multilevel denoising autoencoders, which can effectively improve the fault diagnosis accuracy of wind turbines by using a deep network architecture formed by stacking.…”

Section: Introductionmentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

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“…In this way by using transformer occupies huge volume, and weight highest nature contamination as there is utilization of oil for transformer protection where it is absent in high frequency transformer (HFT). By using regular transformer it has drawbacks like protection and cooling of power transformer [14,15]. To reduce cost and size line frequency transformer can be removed and dual active bridge conversion devices with high frequency transformers can be replaced.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Power Quality in Multi-Terminal Hybrid Ac/Dc Microgrid by using Fuzzy Logic Controller

Gowd¹,

Gowd²,

Lakshmi³

2019

IJRTE

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In this paper analysis of 3-Terminal and 5- Terminal hybrid(cross breed) micro grid using Fuzzy logic controller is proposed. The cascaded H-bridge interfaces between supply and DAB converter. The DAB(Dual Active Bridge) converter connects between cascaded H-bridge and load. By using Zero sequence voltage (ZSV) and inner current controller improvement in grid current and voltage balance are achieved. To boost the grid current and unbalance voltage, 49 ruled fuzzy controller based scheme is used. The analysis of the concept mainly deals with maintenance of power quality and uninterruption in power supply by tuning and Fuzzy logic controlling. The cascaded H-bridge hybrid micro grid with 3- terminal or 5 terminal would have better performance only if there is balanced voltage as well uninterrupted grid current. Fuzzy logic based controller performances are analyzed through simulation results.

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Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network

Cited by 197 publications

References 22 publications

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Improvement of Power Quality in Multi-Terminal Hybrid Ac/Dc Microgrid by using Fuzzy Logic Controller

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