A Modified Simulation Model for Predicting the FDS of Transformer Oil-Paper Insulation Under Nonuniform Aging

Liu, Jiefeng; Sun, Tengyue; Fan, Xianhao; Zhang, Yiyi; Lai, Benhui

doi:10.1109/tim.2021.3056742

Cited by 18 publications

(10 citation statements)

References 21 publications

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“…When the moisture content of paper is 5.5%, tanδoil/tanδpaper<1 at all frequencies, which indicates that there is no characteristic frequency within [10 -3 , 10 4 ] Hz. According to (25), χ is defined as the slope of the tanδ curves of oil-immersed paper to the moisture content, which indicates the sensitiveness of the assessed moisture to the calculated tanδ. The bigger χ is, the less sensitivity of the assessed moisture to the error of tanδ.…”

Section: B Analysis Of Moisture Assessment Resultsmentioning

confidence: 99%

“…For most oil-immersed transformers, the major insulation consists of insulating oil, oil-immersed paper, and spacers used for propping the barriers and forming oil ducts, as shown in Fig. 1(a) [25]. By concentrating the oil, oilimmersed paper, and spacers, which are in different locations, the major insulation of transformers can be further simplified as the XY model [26,27], which is shown in Fig.…”

Section: Derivationof Oil-immersed Paper's Frequency Spectroscopy From Oil-paper Insulationmentioning

confidence: 99%

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Moisture Assessment of Oil-Immersed Paper Based on Dynamic Characteristics of Frequency Domain Spectroscopy

Wang

Lin

et al. 2022

IEEE Access

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In on-site measurement, as the transformer's structure is often unknown, the application of frequency domain spectroscopy (FDS) is greatly limited in moisture assessment of transformer. This paper proposes a moisture assessment method for oil-immersed paper based on the dynamic characteristic frequency even for an unknown transformer structure. FDS tests are performed on the oil-paper insulation, corresponding oil-immersed paper, and mineral oil under different moisture conditions. The moisture criterion of oil-immersed paper and the influence of the structural parameters on the calculated frequency spectroscopy at different frequencies are discussed. It is concluded that the moisture criterion's effectiveness, which indicates the sensitivity of assessed moisture content to the error of the tanδ, changes at different frequencies. Moreover, there are some frequencies at which tanδ is not influenced by structural parameters and is close to the measured value, which are regarded as "characteristic frequencies". Based on the characteristic frequency, the moisture content of oil-immersed paper is assessed according to the frequency spectroscopy of oil-paper insulation without the structural parameters. The result shows that the assessing frequency changes under different moisture conditions, and the error of the assessed moisture content is less than 0.1%.INDEX TERMS frequency domain spectroscopy (FDS), oil-paper insulation, oil-immersed paper, moisture assessment, dynamic characteristic

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Section: B Analysis Of Moisture Assessment Resultsmentioning

confidence: 99%

Section: Derivationof Oil-immersed Paper's Frequency Spectroscopy From Oil-paper Insulationmentioning

confidence: 99%

Moisture Assessment of Oil-Immersed Paper Based on Dynamic Characteristics of Frequency Domain Spectroscopy

Wang

Lin

et al. 2022

IEEE Access

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“…Suppose the input is a two-dimensional (2D) matrix I; in that case, there is a 2D kernel K satisfying Eq. (11).…”

Section: Cnnmentioning

confidence: 99%

“…Due to a series of quantum leaps made by DL, a series of end-to-end learning algorithms have emerged one after another. In particular, Sequence to Sequence based on Recurrent Neural Network (RNN) [9], Convolution Sequence to Sequence [10], and Transformer algorithm based on the self-attention mechanism [11] have raised the level of machine translation to a new height. Therefore, applying DL and other AI technologies to machine translation of natural language has become an inevitable trend of language information processing in social life services.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Computational Linguistics for Chinese Language Translation

Feng

Xie

Wei

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Applying Artificial Intelligence to Chinese language translation in CL (Computational Linguistics) is of practical significance for economic boosts and cultural exchanges. In the present work, the Bi-directional Long Short-Term Memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. Besides, the AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. At last, the forecasting algorithm is simulated to validate its performance. Some state-of-art algorithms are selected for a comparative experiment, including Long Short-Term Memory, Regions with Convolutional Neural Networks features, AlexNet, and Support Vector Machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. Besides, it provides an Area Under the Curve of above 90%, a training duration of about 54.21s, and a test duration of about 19.07s. Regarding the performance of Chinese language translation, the algorithm proposed here provides a Bilingual Evaluation Understudy (BLEU) value of 28.21 on the training set, with a Performance Gain Ratio (PGR) reaching 111.55%; on the test set, its BLEU reaches 40.45, with a PGR of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in CL.

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“…However, only the FDS curve of the main insulation could be obtained in the actual transformer test process. How to obtain the non‐uniform ageing information of the main insulation through this curve has not been deeply analysed in the research [24, 25]. Therefore, the modified dielectric relaxation model could not be used to assess the ageing of the transformer.…”

Section: Introductionmentioning

confidence: 99%

Method for quantitative assessment of transformer oil‐paper insulation non‐uniform ageing parameters based on frequency domain dielectric response

Zhang

Liu³

et al. 2021

IET Science Measure & Tech

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The stable operation of transformers is an important guarantee of power system security. The temperature distribution of main insulation is different in long‐term operation. The temperature difference between the local hottest spot temperature and the average oil temperature in the transformer exceeds 30 K. Therefore, the oil‐paper insulation inside the transformer will exhibit non‐uniform ageing after long‐term operation. At present, frequency domain dielectric response (FDS) technology is widely used in transformer ageing assessment. However, the existing assessment methods are only used for overall insulation ageing assessment. The average moisture content and polymerization degree of the overall insulation between each phase winding are obtained. But it still cannot be used to assess the non‐uniform ageing of the transformer's main insulation. The radial, axial, and two directions non‐uniform ageing models have been made in the laboratory in this study. The FDS and physicochemical properties of non‐uniform ageing models were tested. The variation law of FDS for non‐uniform ageing models was analysed. Meanwhile, the dielectric relaxation model of non‐uniform ageing for main insulation was established. The method of frequency division iterative calculation was proposed. Combined with the Fessler moisture balance formula of ageing oil‐paper insulation, the quantitative assessment method for ageing parameters of the non‐uniform ageing model can be realized. Compared the calculated results with the chemical analysis results, the relative error of the polymerization degree calculation is less than 4%, and the error of the moisture content calculation is less than 0.3%. The calculation results can meet the needs of the engineering apply. Therefore, this method can realize the quantitative assessment of transformer main insulation ageing, which is of great significance to assess the transformer insulation state.

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A Modified Simulation Model for Predicting the FDS of Transformer Oil-Paper Insulation Under Nonuniform Aging

Cited by 18 publications

References 21 publications

Moisture Assessment of Oil-Immersed Paper Based on Dynamic Characteristics of Frequency Domain Spectroscopy

Moisture Assessment of Oil-Immersed Paper Based on Dynamic Characteristics of Frequency Domain Spectroscopy

Deep Learning in Computational Linguistics for Chinese Language Translation

Method for quantitative assessment of transformer oil‐paper insulation non‐uniform ageing parameters based on frequency domain dielectric response

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