Transformer Fault Diagnosis Method Based on Self-Powered RFID Sensor Tag, DBN, and MKSVM

Zhang, Chaolong; He, Yigang; Jiang, Shanhe; Wang, Tao; Liu, Yuan; Li, Bing

doi:10.1109/jsen.2019.2919868

Cited by 39 publications

(19 citation statements)

References 45 publications

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“…Based on dissolved gas analysis (DGA), more scholars adopted the artificial neural network (ANN), the polynomial neural network (PNN), etc., [ 19 , 20 , 21 ]. Moreover, some studies have optimized the algorithms to achieve better results [ 22 , 23 , 24 ]. Compared to the three-ratio method, the adoption of machine learning significantly improved the accuracy of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

Tan

Zhang

et al. 2022

Sensors

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Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO2, and O3 under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

Tan

Zhang

et al. 2022

Sensors

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“…In the field of single-source artificial intelligence algorithms, data such as non-electrical and electrical signals, such as dissolved gas (DA) in oil, vibration, etc., are usually used as the training set of fault diagnosis models. Reference [2] uses the wavelet packet to extract the energy characteristics of the gearbox, and uses the hybrid cuckoo to improve the gray wolf optimization algorithm to optimize the parameters of the multiclass support vector machine, and obtains the composite fault diagnosis of the gearbox of the wind turbine; Reference [3] makes use of the DA fluctuation coefficient and the sliding window outlier identification method to establish the fault gas feature quantity, and the Canopy cluster merging algorithm is used to classify the fault state in the variable weight high-dimensional space; the references [4] and [5] respectively use Deep Belief Neural Networks (BDN) After training the fault data to form a diagnosis model and using BDN to extract the fault set features, a multi-kernel vector machine is used to construct a fault diagnosis model. Reference [6] uses gray wolf optimization and differential evolution algorithm for the kernel extreme learning machine to obtain the kernel parameters and weight 2 optimization results.…”

Section: Introductionmentioning

confidence: 99%

Transformer Fault Diagnosis Method Based on Neural Network and D-S Evidence Theory

Zhan

Yang

Wang

et al. 2022

J. Phys.: Conf. Ser.

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The BP neural network is used to calculate the basic fault probability distribution of the dissolved gas in the transformer oil and the core grounding online monitoring data, and the Dempster-Shafer evidence theory is used to fuse the multi-source information of the basic probability of various types of faults to obtain a transformer fault diagnosis model. Transformer samples are used to verify the model, and the support vector machine and convolutional neural network fault diagnosis models are compared, and it is concluded that the proposed method is better in terms of fault diagnosis accuracy and stability.

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“…More recently, deep learning [39] has become an emerging technology in large data analysis and learning. Typically, it contains deep neural networks (DNN) [40], deep belief network (DBN) [41] and convolutional neural network (CNN) [42], etc. In [42]- [44], deep learning for WiFi-based indoor localization has been investigated to obtain high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network for Passive RFID Tag Localization Via Joint RSSI and PDOA Fingerprint Features

Peng

Jiang

2021

IEEE Access

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Radio-frequency identification (RFID) localization has drawn much attention with the emergence of the Internet of Things (IoT). Deep learning with applications to RFID localization owns a lot of advantages. In this paper, we present a deep convolutional neural network (CNN)-based approach for passive RFID tag localization exploiting joint fingerprint features of the received signal strength indication (RSSI) and phase difference of arrival (PDOA). First, the RSSI and PDOA data are extracted from the received signals of RFID readers. Then, a CNN with three convolution layers and pooling layers is design, in which the normalized RSSI and PDOA data are formed into images as its input to train the weights in the offline stage. In the online stage, the RSSI and PDOA data of test tags are collect and then the positions of unknown tags is predected based on the designed CNN. In the simulations, the accuracy of the proposed approach is compared with several fingerprinting-based schemes such as LANDMARC, weighted K-nearest neighbor (WKNN) and deep neural network (DNN), and the impact of different fingerprint data sets and noise variances on the positioning accuracy is analyzed. Experiments show that the proposed approach can locate multiple tags with high accuracy and stability in complex indoor environment, and outperforms other existing schemes.

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Transformer Fault Diagnosis Method Based on Self-Powered RFID Sensor Tag, DBN, and MKSVM

Cited by 39 publications

References 45 publications

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

Transformer Fault Diagnosis Method Based on Neural Network and D-S Evidence Theory

Deep Convolutional Neural Network for Passive RFID Tag Localization Via Joint RSSI and PDOA Fingerprint Features

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