Diagnosis for transformer winding Looseness based on LMD- Hilbert marginal spectrum energy entropy and SVM

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

Download Full-text

The Analysis on Temperature Raise of EHV Transformer Winding Based on the oil dissolved gas analysis Technology

Asia-Pacific Journal of Advanced Research in Electrical and Electronics Engineering ◽

10.21742/ajaeee.2017.1.1.07 ◽

2017 ◽

Vol 1 (1) ◽

pp. 54-60

Author(s):

Xue Peng ◽

◽

Wei Wei ◽

Wang Ying ◽

Lu Xiuhe ◽

...

Keyword(s):

Gas Analysis ◽

Dissolved Gas ◽

Dissolved Gas Analysis ◽

Transformer Winding

Download Full-text

An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method

Water Resources Management ◽

10.1007/s11269-021-02879-3 ◽

2021 ◽

Author(s):

Fugang LI ◽

Guangwen MA ◽

Shijun CHEN ◽

Weibin HUANG

Keyword(s):

Short Term Memory ◽

Entropy Method ◽

Ensemble Modeling ◽

Variational Mode Decomposition ◽

Short Term ◽

Term Memory ◽

Modeling Approach ◽

Energy Entropy ◽

Mode Decomposition ◽

Reservoir Inflow

Download Full-text

Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽

10.3390/en14144242 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4242

Author(s):

Fausto Valencia ◽

Hugo Arcos ◽

Franklin Quilumba

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Finite Element ◽

Power Transformer ◽

Network Models ◽

Activation Function ◽

Neural Network Models ◽

Transformer Winding ◽

Artificial Neural ◽

Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

Download Full-text

Analysis method of the cavitation vibration signals in poppet valve based on EEMD

Advances in Mechanical Engineering ◽

10.1177/1687814021998114 ◽

2021 ◽

Vol 13 (2) ◽

pp. 168781402199811

Author(s):

Beibei Li ◽

Qiao Zhao ◽

Huaiyi Li ◽

Xiumei Liu ◽

Jichao Ma ◽

...

Keyword(s):

Ensemble Empirical Mode Decomposition ◽

Center Frequency ◽

Analysis Method ◽

Vibration Signals ◽

Poppet Valve ◽

Mode Decomposition ◽

Marginal Spectrum ◽

Cavitation Intensity ◽

Eemd Method ◽

Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

Download Full-text