A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Zhang, Li; Zhang, Wenfang; Liu, Jinxin; Zhao, Tong; Zou, Liang; Wang, Xinghua

doi:10.3390/en10121998

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…On this basis, the error backpropagation process can be realized as written in Equation (10) Collectively, it can be observed that the initial positions of particles and the velocity update algorithm are of great significance to the convergence of the WNN model. Given the good randomness and exploratory characteristics of the chaos method [29], chaos chaotic properties are applied in the PSO to enhance the optimization effects of the WNN model.…”

Section: Parameter Optimization Methods For Wnn Based On Cpsomentioning

confidence: 99%

A Novel Prediction Model for Seawall Deformation Based on CPSO-WNN-LSTM

Zheng,

Gu,

Shao

et al. 2023

Mathematics

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Admittedly, deformation prediction plays a vital role in ensuring the safety of seawall during its operation period. However, there still is a lack of systematic study of the seawall deformation prediction model currently. Moreover, the absence of the major influencing factor selection is generally widespread in the existing model. To overcome this problem, the Chaotic Particle Swarm Optimization (CPSO) algorithm is introduced to optimize the wavelet neural network (WNN) model, and the CPSO-WNN model is utilized to determine the major influencing factors of seawall deformation. Afterward, on the basis of major influencing factor determination results, the CPSO algorithm is applied to optimize the parameters of Long Short-Term Memory (LSTM). Subsequently, the monitoring datasets are divided into training samples and test samples to construct the prediction model and validate the effectiveness, respectively. Ultimately, the CPSO-WNN-LSTM model is employed to fit and predict the long-term settlement monitoring data series of an actual seawall located in China. The prediction performances of LSTM and BPNN prediction models were introduced to be comparisons to verify the merits of the proposed model. The analysis results indicate that the proposed model takes advantage of practicality, high efficiency, stable capability, and high precision in seawall deformation prediction.

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Section: Parameter Optimization Methods For Wnn Based On Cpsomentioning

confidence: 99%

A Novel Prediction Model for Seawall Deformation Based on CPSO-WNN-LSTM

Zheng,

Gu,

Shao

et al. 2023

Mathematics

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“…Combining Equations (2)-(5), the following relationship in terms of the reluctance and flux density can be drawn: (6) where B i is the flux density of the i-th magnetic segment.…”

Section: Steinmetz Equation (Mse) General Steinmetz Equation (Gse)mentioning

confidence: 99%

A Coupled, Semi-Numerical Model for Thermal Analysis of Medium Frequency Transformer

et al. 2019

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Medium-frequency (MF) transformer has gained much popularity in power conversion systems. Temperature control is a paramount concern, as the unexpected high temperature declines the safety and life expectancy of transformer. The scrutiny of losses and thermal-fluid behavior are thereby critical for the design of MF transformers. This paper proposes a coupled, semi-numerical model for electromagnetic and thermal-fluid analysis of MF oil natural air natural (ONAN) transformer. An analytical model that is based on spatial distribution of flux density and AC factor is exploited to calculate the system losses, while the thermal-hydraulic behavior is modelled numerically leveraging the computational fluid dynamics (CFD) method. A close-loop iterative framework is formulated by coupling the analytical model-based electromagnetic analysis and CFD-based thermal-fluid analysis to address the temperature dependence. Experiments are performed on two transformer prototypes with different conductor types and physical geometries for validation purpose. Results suggest that the proposed model can accurately model the AC effects, losses, and the temperature rises at different system components. The proposed model is computationally more efficient than the full numerical method but it reserves accurate thermal-hydraulic characterization, thus it is promising for engineering utilization.

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“…The parts with strong vibration signal are more likely to have fault accidents occur [27]. High signal-to-noise ratio can also be obtained by selecting a strong vibration area of the transformer winding [28]. According to the model studied in this paper, the vibration signal near the winding is strong.…”

Section: Transformer Short-circuit Experimentsmentioning

confidence: 99%

Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method

et al. 2018

Self Cite

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The winding is the core component of a transformer, and the technology used to diagnose its current state directly affects the operation and maintenance of the transformer. The mechanical vibration characteristics of a dry-type transformer winding are studied in this paper. A short-circuit test was performed on an SCB10-1000/10 dry-type transformer, and the vibration signal at the surface was measured. Based on actual experimental conditions, a vibration-simulation model of the transformer was established using COMSOL Multiphysics software. A multiphysics coupling simulation of the circuit, magnetic field, and solid mechanics of the transformer was performed on this model. The simulation results were compared with measured data to verify the validity of the simulation model. The simulation model for a transformer operating under normal conditions was then used to develop simulation models of transformer-winding looseness, winding deformation, and winding-insulation failure, and the winding fault vibration characteristics were analyzed. The results provide a basis for detecting and analyzing the mechanical state of transformer windings.

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A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Cited by 9 publications

References 17 publications

A Novel Prediction Model for Seawall Deformation Based on CPSO-WNN-LSTM

A Novel Prediction Model for Seawall Deformation Based on CPSO-WNN-LSTM

A Coupled, Semi-Numerical Model for Thermal Analysis of Medium Frequency Transformer

Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method

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