Research on fault diagnosis of transformer based on laser induced fluorescence technology

Yan, Pengcheng; Zhang, Chaoyin; Mei, Kaifeng; Chen, Fengxiang; Wang, Yihan

doi:10.1016/j.molstruc.2022.132645

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Power transformers play an important role in daily life [1,2], its main roles include: (1) power transmission: Power transformers are used to raise or lower voltage to adapt to different power transmission and distribution needs. The * Author to whom any correspondence should be addressed.…”

Section: Research Backgroundmentioning

confidence: 99%

“…high-voltage electrical energy generated at a power plant needs to be boosted by a transformer for transmission over long distances over transmission lines, and then reduced by a transformer at a receiving station for domestic, industrial and commercial use. (2) Household electricity: In the home, power transformers are usually used to convert high-voltage electrical energy into low voltage suitable for household electricity to supply lights, appliances and equipment. Such transformers, commonly referred to as distribution transformers, ensure a safe and efficient power supply.…”

Section: Research Backgroundmentioning

confidence: 99%

“…In addition, it cannot detect faults in non-oil immersed transformers. (2). The limitation of fault diagnosis technology based on thermal image technology is that thermal image technology has good detection effect on surface faults, but limited diagnosis of internal faults.…”

Section: Research Backgroundmentioning

confidence: 99%

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Transformer fault diagnosis based on DBO-BiLSTM algorithm and LIF technology

Yan,

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

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In response to the deficiencies of traditional power transformer fault detection techniques, such as low sensitivity and the inability for online monitoring, a novel transformer fault diagnosis model combining Laser-Induced Fluorescence (LIF) technology with deep learning is proposed. Initially, the spectral data of transformer insulation oil is acquired using LIF technology, yielding spectral data for various fault types. Subsequently, MinMaxScaler (MMS) and Standard Normalized Variate (SNV) methods are employed for denoising and preprocessing the spectral data. The preprocessed data is then subjected to dimensionality reduction using Linear Discriminant Analysis (LDA) and T-distributed Stochastic Neighbor Embedding (T-SNE) to ensure that the spectral data retains maximal feature information while minimizing its dimensionality. Following this, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Dung Beetle Optimizer-Bi-directional Long Short Term Memory (DBO-BiLSTM), Convolutional Neural Network (CNN), and Support Vector Machine (SVM) models are constructed. The reduced-dimensional data is fed into each of the five models for training to facilitate transformer fault diagnosis. Through comparative analysis among the five models, the optimal model is selected. Experimental results indicate that the DBO-BiLSTM model is the most suitable for transformer fault diagnosis in this experiment, underscoring its significant implications for ensuring the safety of power systems.

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Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

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Transformer fault diagnosis based on DBO-BiLSTM algorithm and LIF technology

Yan,

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

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“…With the advancement of spectral analysis technology, LIF spectroscopy has found successful applications and advancements across various domains [10][11][12][13][14]. For example, Roger [15] used LIF technology to directly obtain the concentration distribution of nitrogen atoms in high enthalpy air.…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

Yan,

Wang,

Wang

et al. 2023

Meas. Sci. Technol.

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Power transformers are essential components in power systems used to regulate voltage, transmit electrical energy, provide isolation, and match loads. They contribute to efficient and reliable electricity transmission and distribution. However, traditional methods for diagnosing transformer faults are time-consuming, not suitable for online monitoring, and greatly affected by environmental conditions. In this experiment, we propose the use of laser-induced fluorescence (LIF) technology for transformer fault detection. LIF technology is a method for analyzing and detecting specific molecules or atoms in samples. It combines laser technology with fluorescence measurements, making it a powerful analytical tool. It achieves high sensitivity and selectivity in analyzing molecules and atoms by exciting and detecting fluorescence in the sample. This makes it an important technology in scientific research and practical applications. Furthermore, LIF technology has not been previously applied to power transformer fault diagnosis. Therefore, this experiment introduces a transformer fault diagnosis model based on the marine predators algorithm (MPA) optimized random forest (RF) algorithm and LIF spectroscopy technology. Four different oil samples were selected for experimentation: crude oil, thermally faulty oil, partially moist oil, and electrically faulty oil. First, LIF technology for collect spectral images and data from the different fault oil samples. The obtained spectral data was preprocessed using two methods, multivariate scatter correction (MSC) and standardization method (SNV). Then, principal component analysis (PCA) and kernel principal component analysis (KPCA) for reducing the dimensionality of the preprocessed spectral data. Finally, the RF model, MPA-RF model, and PSO-RF model were established; and the reduced data was input into the model for training. Through comparisons of the predictions on the test set, evaluation metrics of the algorithm (including fitting coefficient, MSE, RMSE, and RMSE), and iteration convergence curves, the best transformer fault diagnosis model was identified. The results show that the MSC-KPCA-MPA-RF model has the best matching resule, with a fitting coefficient of 0.9963 and a mean square error of 0.0047. The SNV-PCA-MPA-RF model has the worst fitting effect, with a fitting coefficient of 0.9840 and a mean square error of 0.0199. Through comparisons of the convergence of different models, the MSC-KPCA-MPA-RF model has the best convergence and is the most applicable model for transformer fault diagnosis in this experiment. This model has significant implications for ensuring the safety of the power system.

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