Dynamic Incipient Fault Forecasting for Power Transformers Using an LSTM Model

Wang, Lin; Littler, Timothy; Liu, Xueqin

doi:10.1109/tdei.2023.3253463

IEEE Trans. Dielect. Electr. Insul.

2023

DOI: 10.1109/tdei.2023.3253463

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Dynamic Incipient Fault Forecasting for Power Transformers Using an LSTM Model

Lin Wang

Timothy Littler

Xueqin Liu

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2024

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Cited by 6 publications

References 29 publications

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Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.

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Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

Shang,

Zhao,

Zhang

et al. 2024

IET Electric Power Appl

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Insulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model.

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A Data-Driven Digital Twin Architecture for Failure Prediction of Customized Automatic Transverse Robot

Ye,

Liu,

Zhao

et al. 2024

IEEE Access

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The customized automatic transverse robots play important roles in transporting heavy printing material rolls in automatic and intelligent flexible packaging printing workshops. A three-dimensional (3D) real-time visualization monitoring and feedback system of the customized automatic transverse robot is developed by adopting digital twin technology. This system is based on the theoretical framework of the datadriven digital twin model, and the main functions work through the physical layer, digital twin layer, and functional layer. The twin model is established and connected through the parent-child matching relationship of the components and it is driven by the real-time data which are acquired, transmitted and stored through communication protocols, such as OPC UA and RS-485. The physical automatic transverse robot and the virtual world of digital twins are synchronously mapped. The data are transferred from the virtual world twin model to the real world by establishing the application programming interface (API), implementing a feedback regulation mechanism that controls reality with virtuality. Combined with the long short-term memory (LSTM) model, the prediction of the failure trend of automatic transverse robot during the motion process has been achieved. A prototype digital twin system is developed for validation, which completes the closed-loop system of digital twin theory and proves the feasibility and effectiveness of the proposed system.

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Dynamic Incipient Fault Forecasting for Power Transformers Using an LSTM Model

Cited by 6 publications

References 29 publications

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

A Data-Driven Digital Twin Architecture for Failure Prediction of Customized Automatic Transverse Robot

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