Investigation on Molecular Dynamics Simulation for Predicting Kinematic Viscosity of Natural Ester Insulating Oil

Zheng, Hanbo; Feng, Yan; Li, Xufan; Yang, Hang; Lv, Weijie; Li, Songjiang

doi:10.1109/tdei.2022.3198763

Cited by 12 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the viscosity of natural ester is influenced by temperature, and mobility and viscosity are closely related. By extrapolating the viscosity versus temperature relationship, it is possible to determine how mobility is affected by temperature [23]. Correlation of the mobility of electrons in natural ester with viscosity using the Lorentz radius formula [21].…”

Section: Discharge Mechanisms and Simulation Modelingmentioning

confidence: 99%

Numerical Analysis of Streamer Discharge Propagation Characteristics in Natural Ester

Zheng

Yang

Feng

et al. 2022

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Natural ester is viewed as the most promising future alternative to mineral oil due to its high degradability, but its inferior breakdown characteristics prevent its application of large power equipment. The purpose of this research is to investigate the breakdown characteristics of natural ester. First, the natural ester's ionization potential and molecular number density are calculated. Then, an improved natural ester streamer discharge model is developed using the Poisson's equation and the carrier continuity equation, taking into account the effect of temperature on carrier mobility and incorporating the computed ionization potential and other factors. The results for different lightning impulse voltages were analyzed by COMSOL software. Comparing the mineral oil, the results indicate that the natural ester breakdown voltage is close to and slightly lower than that of the mineral oil but the acceleration voltage is very similar to the breakdown voltage. The lower ability of esters in prevention against the development of fast and energetic streamers is consistent with the description of the experimental results. In addition, the model takes into account the effect of temperature on the carrier mobility. As the applied voltage increases, the change of temperature in the streamer channel accelerates the carrier motion and promotes the propagation of the streamer.

show abstract

Section: Discharge Mechanisms and Simulation Modelingmentioning

confidence: 99%

Numerical Analysis of Streamer Discharge Propagation Characteristics in Natural Ester

Zheng

Yang

Feng

et al. 2022

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…In recent years, tree ensemble machine learning models [13,14], such as extreme gradient boosting [15] and gradient boosting trees [16], have received increasing attention in industry and academic research due to their open architecture, low computing cost, and robustness. The authors of [17] compared the performance of random forest (RF), extreme regression tree (ET) and support vector machine regression (SVR) for the prediction of photovoltaic power; the ET model achieved the best performance in terms of forecasting accuracy, calculation cost, and stability indices.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation

et al. 2023

View full text Add to dashboard Cite

With the increasing integration of wind and photovoltaic power, the security and stability of the power system operations are greatly influenced by the intermittency and fluctuation of these renewable sources of energy generation. The accurate and reliable short-term forecasting of renewable energy generation can effectively reduce the impacts of uncertainty on the power system. In this paper, we propose an adaptive, data-driven stacking ensemble learning framework for the short-term output power forecasting of renewable energy. Five base-models are adaptively selected via the determination coefficient (R2) indices from twelve candidate models. Then, cross-validation is used to increase the data diversity, and Bayesian optimization is used to tune hyperparameters. Finally, base modes with different weights determined by minimizing the cross-validation error are ensembled using a linear model. Four datasets in different seasons from wind farms and photovoltaic power stations are used to verify the proposed model. The results illustrate that the proposed stacking ensemble learning model for renewable energy power forecasting can adapt to dynamic changes in data and has better prediction precision and a stronger generalization performance compared to the benchmark models.

show abstract

“…The theory of quantum mechanics means that molecular simulation technology is not only limited to the study of chemistry, but also extended to electrical, physical, biological and material science and other disciplines, promoting the development of interdisciplinary research and providing a new means for people to understand the material world, in addition to experimental methods and theoretical methods [15]. Many scholars have applied molecular simulation methods to the field of high-voltage insulation, such as the synergistic effect of electric fields and temperature on insulating oil [16,17], the microscopic mechanism of the overheating of insulating oil [18,19], the aging and degradation of insulating materials [20,21], and the diffusion of gas molecules in oil paper insulation systems [22,23]. Meanwhile, molecular simulation has been successfully used in the study of PVDF, the static and dynamical mechanical properties of PVDF [24], elastic properties of PVDF crystals [25], and the effect of the addition of ionic liquids to PVDF [26].…”

Section: Introductionmentioning

confidence: 99%

Microscopic Mechanism of Electrical Aging of PVDF Cable Insulation Material

Pang

Zheng

et al. 2023

Polymers

Self Cite

View full text Add to dashboard Cite

In this study, the quantum chemical method was used to investigate the microscopic characteristics of α-poly viny difluoride (PVDF) molecules under the influence of an electric field, and the impact of mechanical stress and electric field polarization on the insulation performance of PVDF was analyzed through the material’s structural and space charge characteristics. The findings reveal that long-term polarization of an electric field leads to a gradual decline in stability and a reduction in the energy gap of the front orbital, resulting in the improved conductivity of PVDF molecules and a change in the reactive active site of the molecular chain. When the energy gap reaches a certain value, a chemical bond fracture occurs, with the C-H and C-F bonds at the ends of the backbone breaking first to form free radicals. This process is triggered by an electric field of 8.7414 × 109 V/m, which leads to the emergence of a virtual frequency in the infrared spectrogram and the eventual breakdown of the insulation material. These results are of great significance in understanding the aging mechanism of electric branches in PVDF cable insulation and optimizing the modification of PVDF insulation materials.

show abstract

Investigation on Molecular Dynamics Simulation for Predicting Kinematic Viscosity of Natural Ester Insulating Oil

Cited by 12 publications

References 22 publications

Numerical Analysis of Streamer Discharge Propagation Characteristics in Natural Ester

Numerical Analysis of Streamer Discharge Propagation Characteristics in Natural Ester

An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation

Microscopic Mechanism of Electrical Aging of PVDF Cable Insulation Material

Contact Info

Product

Resources

About