Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM

Yan, Pengcheng; Chen, Fengxiang; Zhao, Tian‐Jian; Zhang, Heng; Kan, Xuyue; Yang, Licong

doi:10.1039/d2ay01745h

Cited by 3 publications

(5 citation statements)

References 38 publications

(38 reference statements)

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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%

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%

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

Yan,

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…The leaf-wise decision tree growth strategy is illustrated in Figure 2. LightGBM utilizes the leaf-wise decision tree growth strategy [23]. This strategy involves finding the leaf with the highest splitting gain among all the current leaves for splitting, repeating the process, and continuously iterating.…”

Section: Lightgbmmentioning

confidence: 99%

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

Chen,

Cui,

Zhang

2023

Electronics

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The type of power fiber optic cable fault event obtained by analyzing the optical time domain reflectometer (OTDR) detection curve is an important basis for ensuring the operation quality of communication lines. To address the issue of low accuracy in recognizing fault event patterns, this research proposes the AVOA-LightGBM method for optical cable fault event pattern recognition based on wavelet packet decomposition. Initially, a three-layer wavelet packet decomposition is performed on different fault events, resulting in eight characteristic signals. These signals are then normalized and used as input for each recognition model. The Light Gradient Boosting Machine (LightGBM) is optimized using the African vulture optimization algorithm (AVOA) for pattern recognition. The experimental results demonstrate that this method achieves a recognition accuracy of 98.24%. It outperforms LightGBM, support vector machine (SVM), and extreme learning machine (ELM) by 3.7%, 19.15%, and 5.67%, respectively, in terms of accuracy. Moreover, it shows a 1.8% improvement compared with the combined model PSO-LightGBM.

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“…This isolation aids in safeguarding personnel and equipment. (4). Load matching: power transformers have the capability to regulate output voltage to meet the demands of diverse loads, ensuring the provision of appropriate voltage to end-users, including households and industrial equipment.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, power transformers are indispensable components within electrical power systems, serving the purposes of voltage adjustment, power transmission, isolation, and load matching. They significantly contribute to achieving efficient and reliable electricity transmission and distribution [2][3][4]. Consequently, diagnosing transformer faults holds significant importance.…”

Section: Introductionmentioning

confidence: 99%

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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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Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM

Abstract: Transformer fault diagnosis is a necessary operation to ensure the stable operation of a power system.

Cited by 3 publications

References 38 publications

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

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

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

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

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