Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

Min, Fan; Xia, Jialu; Meng, Xinyu; Zhang, Ke

doi:10.3390/s22093521

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…In this paper, the ResNet18 [ 35 ] network is used for model training. The basic architecture of ResNet18 is a ResNet and the depth of the network is 18 layers so it is called ResNet18.…”

Section: Introduction To the Fematl Methodsmentioning

confidence: 99%

Application of Fault Diagnosis Method Combining Finite Element Method and Transfer Learning for Insufficient Turbine Rotor Fault Samples

Zhang

Qin

et al. 2023

Entropy

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Deep learning has led to significant progress in the fault diagnosis of mechanical systems. These intelligent models often require large amounts of training data to ensure their generalization capabilities. However, the difficulty of obtaining turbine rotor fault data poses a new challenge for intelligent fault diagnosis. In this study, a turbine rotor fault diagnosis method based on the finite element method and transfer learning (FEMATL) is proposed, ensuring that the intelligent model can maintain high diagnostic accuracy in the case of insufficient samples. This method fully exploits the finite element method (FEM) and transfer learning (TL) for small-sample problems. First, FEM is used to generate data samples with fault information, and then the one-dimensional vibration displacement signal is transformed into a two-dimensional time-frequency diagram (TFD) by taking advantage of the deep learning model to recognize the image. Finally, a pre-trained ResNet18 network was used as the input to carry out transfer learning. The feature extraction layer of the network was trained on the ImageNet dataset and a fully connected layer was used to match the specific classification problems. The experimental results show that the method requires only a small amount of training data to achieve high diagnostic accuracy and significantly reduces the training time.

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“…In this paper, the ResNet18 [ 35 ] network is used for model training. The basic architecture of ResNet18 is a ResNet and the depth of the network is 18 layers so it is called ResNet18.…”

Section: Introduction To the Fematl Methodsmentioning

confidence: 99%

Application of Fault Diagnosis Method Combining Finite Element Method and Transfer Learning for Insufficient Turbine Rotor Fault Samples

Zhang

Qin

et al. 2023

Entropy

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show abstract

“…However, the zero-sequence current of non-fault short line changes little before and after fault, which makes the fault characteristics not obvious and easy to be submerged by noise signal. Some scholars have introduced time-frequency support vector machine classification detection methods [18] and deep learning and other artificial intelligence methods [19][20][21]. These methods often use traditional fault feature quantities to train the detection model, and the results depend on the completeness and accuracy of the training database.…”

Section: Introductionmentioning

confidence: 99%

Time Estimation Algorithm of Single-Phase-to-Ground Fault Based on Two-Step Dimensionality Reduction

Lin

Chen

et al. 2023

Energies

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The fault detection time identified by relying on the over-voltage criterion of zero-sequence voltage often lags behind the actual occurrence time of ground faults, which may cause fault protection methods based on transient quantity principles to miss fault characteristics and lose their protection capability. To accurately estimate the time of occurrence of a single-phase-to-ground fault, this paper proposes a two-step dimensionality reduction algorithm for estimating the time of occurrence of a single-phase-to-ground fault in a distribution network. This algorithm constructs a filter based on Empirical Mode Decomposition (EMD) to establish a high-dimensional feature dataset based on the zero-sequence current of all feeders. After Principal Component Analysis and Hilbert Mapping Algorithm, the high-dimensional data are reduced to two dimensions to construct a two-dimensional feature dataset. The density-based clustering method is used to adaptively divide the data into two categories, fault data and non-fault data, so as to estimate the time of occurrence of the fault. The paper designs 11 sets of experiments including 7 common high-resistance grounding mediums to verify the accuracy of the fault time recognition of this algorithm. The accuracy of this algorithm is within 7.3 ms and it exhibits better detection performance compared to the threshold detection method.

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“…Most of the 3~66 kV power supply systems in China's petrochemical enterprises adopt neutral point non-effective grounding. Among them, due to the compensation effect of the arc suppression coil, the resonant grounding system has the disadvantages of the fault characteristic quantity not being obvious, and the fault detection being difficult after the single-phase grounding fault occurs [1][2][3][4][5], resulting in a worse faulty feeder detection effect [6,7].…”

Section: Introductionmentioning

confidence: 99%

Faulty Feeder Detection Based on Grey Correlation Degree of Adaptive Frequency Band in Resonant Grounding Distribution System

et al. 2023

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Due to the particularity of their process, petrochemical enterprises have high requirements for the reliability of power supply. If a large-scale blackout occurs due to a grounding fault, it will pose a huge threat to safe production. When the resonant grounding system of petrochemical enterprises faults, due to the complex fault process and weak fault signal, it is difficult to accurately detect the faulty feeder by traditional methods. This paper presents a new method of grey correlation degree based on adaptive frequency band. Firstly, the transient zero-sequence current of each feeder is decomposed by coif5 wavelet, and the low frequency band a5 (power frequency component) and high frequency band d1, d2 (noise signal) are removed. By stacking all of the remaining frequency band signals to construct the wavelet area matrix, the faulty feeder detection characteristic scale and the first faulty feeder detection result are obtained. Secondly, based on the faulty feeder detection characteristic scale, the second faulty feeder detection result is obtained by the average grey correlation degree matrix, which detects the faulty feeder according to the waveform correlation degree. Finally, the final faulty feeder detection result is obtained by equal weight voting. In MATLAB/Simulink, the 10 kV resonant grounding system of petrochemical enterprises is modeled. A large number of simulation results show that the faulty feeder detection method is not affected by the initial phase angle (0°, 45° and 90°), transition resistance (10 Ω, 100 Ω and 1000 Ω), fault distance (1 km, 8 km and 15 km) and overcompensation degree (5%, 8% and 10%), and has good sensitivity.

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Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

Cited by 7 publications

References 36 publications

Application of Fault Diagnosis Method Combining Finite Element Method and Transfer Learning for Insufficient Turbine Rotor Fault Samples

Application of Fault Diagnosis Method Combining Finite Element Method and Transfer Learning for Insufficient Turbine Rotor Fault Samples

Time Estimation Algorithm of Single-Phase-to-Ground Fault Based on Two-Step Dimensionality Reduction

Faulty Feeder Detection Based on Grey Correlation Degree of Adaptive Frequency Band in Resonant Grounding Distribution System

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