Inversion Detection Method for Resistivity of Oil-Immersed Paper in Transformer

Xie, Yiming; Ruan, Jiangjun; Shi, Yu; Jin, Shuo; Tian, Yuan; Zhu, Lin

doi:10.1109/tpwrd.2019.2921799

Cited by 19 publications

(12 citation statements)

References 27 publications

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“…ɸ is the activation function of hidden layer, and the sigmoid activation function is used. Ψ is the linear activation function of output layer, and y1 is the final output value [29], [30].…”

Section: ) Hst Inversion Results Compared With Ga-bpnn Methodsmentioning

confidence: 99%

“…In the case of knowing the transformer load and external heat dissipation conditions, the transformer temperature field can be calculated based on the numerical calculation of transformer thermal fluid field, which can directly reflect the transformer HST and the temperature distribution of transformer iron shell. Contrary to forward model, namely direct calculation, the inverse model is the process of inferring the cause from the result, which is defined as the estimation of parameters inside physical model using measured data [21].…”

Section: A Transformer Transient Hst Inversion Methodsmentioning

confidence: 99%

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Inversion Detection of Transformer Transient Hot Spot Temperature

Ruan

Deng

Quan³

et al. 2021

IEEE Access

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Section: ) Hst Inversion Results Compared With Ga-bpnn Methodsmentioning

confidence: 99%

Section: A Transformer Transient Hst Inversion Methodsmentioning

confidence: 99%

Inversion Detection of Transformer Transient Hot Spot Temperature

Ruan

Deng

Quan³

et al. 2021

IEEE Access

Self Cite

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“…Moreover, the tanδ curves of the lab samples can be seen in Figure 7. For the feasibility verification of the reported GA-SVM model, the dielectric fingerprints of the lab samples are extracted by Equation (1). Combining the tested σoil, the dielectric fingerprints of lab samples can be collected, which are shown in Table 7.…”

Section: Verification Under Laboratory Conditionsmentioning

confidence: 99%

“…Power transformers perform a vital task of transforming electrical energy in power systems, and the condition evaluation of its internal oil/paper system has attracted wide attention [ 1 , 2 , 3 , 4 , 5 , 6 ]. In recent years, a prevailing method of moisture determination is the so-called frequency domain spectroscopy (FDS) [ 7 , 8 , 9 , 10 ] technique, which is also called dielectric frequency response (DFR) technique.…”

Section: Introductionmentioning

confidence: 99%

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

Zhang

Fan

et al. 2020

Polymers

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The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer oil-immersed insulation. In the present work, a method of constructing a GA-SVM multi-classifier for moisture diagnosis based on the fitting analysis model is firstly reported. Then, the feasibility and reliability of the reported method are proved by employing the laboratory and field test experiments. The experimental results indicate that the reported prediction model might be serviced as a potential tool for the moisture prediction of transformer oil-immersed polymer insulation.

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“…During identification, the partial discharge signal detected by HFCT was filtered by discrete wavelet and input into the Lorenz chaotic system to draw the dynamic error scatter diagram, and the image features were extracted and identified by CNN. Meanwhile, the proposed A method was compared with the traditional HOG image feature extraction method with SVM [13], [14], BPNN [15], and K-Nearest Neighbor (KNN) [16], [17] classifiers for identification accuracy. The results show that the proposed method can effectively identify the partial discharge type of power cables and has the highest identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

Wang

Liao

2022

IEEE Trans. Power Delivery

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In this paper, the discrete wavelet transform (DWT) and a chaotic system were combined with a convolutional neural network (CNN) and applied to the diagnosis of insulation faults in XLPE (cross-linked polyacetylene) power cables. First, four different types of insulation faults in power cables were constructed, including the normal state of the cable, the short outer semi-conducting layer, impurities in the insulation layer, and insulation layer damage, and a high-speed capture card (NI PXI-5105) was adopted to measure the partial discharge (PD) signal, which was then filtered through discrete wavelet transform. Then, based on the Lorenz chaotic system, a dynamic error scatter diagram was established as the feature of each fault state. Finally, the dynamic error scatter diagram was processed by CNN to recognize four different types of faults in the power cable. The test results show that the method proposed in this paper can quickly recognize the fault state of power cables and has excellent performance in terms of recognition accuracy, which reaches 97.5%. Therefore, the proposed method can effectively detect the fault signal changes of power cables and identify the fault state of power cables in real time.

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Inversion Detection Method for Resistivity of Oil-Immersed Paper in Transformer

Cited by 19 publications

References 27 publications

Inversion Detection of Transformer Transient Hot Spot Temperature

Inversion Detection of Transformer Transient Hot Spot Temperature

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm

Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

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