Transformer Fault Diagnosis based on Deep Brief Sparse Autoencoder

Zhong, Xu; Mo, Wenxiong; Wang, Yong; Luo, Simin; Liu, Tian

doi:10.23919/chicc.2019.8866347

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…The results revealed better performance in comparison with KNN, SVM, BPNN. Similarly, the transformer fault is predicted using SAE and DBN algorithms to increase reliability and the stability of power systems [94]- [95]. The SAE algorithm is proposed in [96] for the same task.…”

Section: Tp Precision = Tp +Fpmentioning

confidence: 99%

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

et al. 2022

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This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.

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Section: Tp Precision = Tp +Fpmentioning

confidence: 99%

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

et al. 2022

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“…Lee et al [10] proposed a feature extraction method based on deep unsupervised sparse autoencoder (SAE) for data classification, which improved the classification performance and detection speed, but the performance of sparse classes was worse than that of other classes. Xu et al [11] proposed a deep belief sparse autoencoder (DBSAE), which captured features of label-free dissolved gas analysis (DGA) raw data, and a supervised trained back propagation network is used to implement transformer fault diagnosis. Marir et al [12] proposed a new stack denoising SAE method, which was implemented by using spark-based iterative simpli cation paradigm to improve detection performance and algorithm e ciency.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction Method Based on Sparse Autoencoder for Air Traffic Management System Security Situation Awareness

Bai

Zhang

et al. 2022

Security and Communication Networks

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In wide-area distributed scenarios, it is particularly important to carry out information security situational awareness for the air traffic management (ATM) system with integrated air-ground structure. The operation data of the communication, navigation and surveillance (CNS) equipment of ATM system have the characteristics of multi-dimension, complexity, and strong correlation. In the process of situation awareness feature extraction, there are problems such as poor model accuracy, weak feature expression ability, and low classification performance. A feature association algorithm is designed to solve the above problems. Based on this algorithm, a deep-related sparse autoencoder (DRSAE) model based on improved sparse autoencoder is established. In DRSAE model, L1 regularization and Kullback–Leibler divergence (KLD) sparsity terms are used to penalize the parameters of the encoder network, and the quantity of hidden layers is increased to allow the model to optimize the global encoder network by iteratively training a single encoder. Moreover, the proposed DRSAE model and other feature extraction models such as principal component analysis (PCA), autoencoder (AE), and sparse autoencoder (SAE) are compared and evaluated by using the support vector machine (SVM) classifier. Compared with other feature extraction models, it is found that the proposed DRSAE model has good robustness in feature extraction of ATM system, and the obtained features have strong expression ability, which enhances the classification performance of the model and is convenient for situation awareness.

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“…A. DEVICE IDENTIFICATION USING DEEP LEARNING Autoencoders (AE) are usually utilized to learn efficient data coding in an unsupervised learning manner [7][8][9][10][11]. Its application includes both feature extraction and sample variation diagnosis.…”

Section: Device Identification and Model Optimizationmentioning

confidence: 99%

“…Thus, the purpose of detecting damaged tools was achieved, producing experimental results with an accuracy of 95%. In [7][8][9][10], AE was also used for fault diagnosis, and it was difficult to collect all the samples because of variable fault conditions. Therefore, the training method that only fits the positive sample can achieve either normal or abnormal results.…”

Section: Introductionmentioning

confidence: 99%

Device Light Fingerprints Identification Using MCU-Based Deep Learning Approach

Hung

Lee³

2021

IEEE Access

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We introduce device identification using the light fingerprint by a MCU-based deep learning approach. At first, we observe that minor differences exist for individual components of lighting equipment. The corresponding difference produces a unique phenomenon in the frequency spectrum. Therefore, we adopt deep learning approaches for developing a mobile phone light fingerprint identification system and implementing it on a low-cost microcontroller platform. The screen light of the mobile phone is analyzed to obtain the features of unique light fingerprints. We utilize the convolutional neural network, the improved multi-class greedy autoencoder and variational autoencoder with domain adaptation techniques to develop the identification algorithm. Finally, the Bayesian optimization technique is used to optimize the hyperparameters of models for implementing in the microprocessor. The corresponding comparisons are introduced to demonstrate the performance. The multi-class greedy autoencoder algorithm produces results with an overall accuracy rate and abnormal sample detection rate of 99.67% and 99.85%, respectively. Only a single model needs to be added or deleted for updating new authentication data and this does not affect the identification ability of all models. This results in greater flexibility in real-life applications and potential for expansion to other fields, such as smart buildings and automated robots.

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Transformer Fault Diagnosis based on Deep Brief Sparse Autoencoder

Cited by 10 publications

References 10 publications

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

Feature Extraction Method Based on Sparse Autoencoder for Air Traffic Management System Security Situation Awareness

Device Light Fingerprints Identification Using MCU-Based Deep Learning Approach

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