An EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier

Sami, Shoaib Meraj; Bhuiyan, ohammed Imamul Hassan

doi:10.1109/icece51571.2020.9393037

Cited by 6 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is proved to be efficient in handling data uncertainty and transformer faults. An empirical mode data decomposition method along with AI technique is used in [52], to calculate the transformer faults. In this work, a large set of parameters are decomposed, and the optimal parameter are classified to find the transformer fault.…”

Section: Artificial Intelligence In Power Trannsformer Fault Identifi...mentioning

confidence: 99%

Artificial Intelligence and its applications in Renewable Integrated Power Systems

E.R.¹,

Joy²,

Bansal³

et al. 2022

Preprint

View full text Add to dashboard Cite

Electric power system is undergoing major changes, due to large-scale integration of renewable energy, complicated network structure and increased energy demand. Developing autonomous systems, intelligent and digital communication technologies are becoming more and more urgent to modernize, strengthen and stabilize the power grid. Among several innovative efforts, artificial intelligence (AI) is becoming more popular due to its human like thinking logic, advanced wisdom, reasoning, learning capability and knowledge representation. AI approaches have demonstrated precise, faster and scalable outcomes, thus can be ideal to handle complex non-linear power systems. The recent technological developments in AI and hybrid techniques, makes it possible for solving large-scale complex power systems problems like control, planning, scheduling, prediction etc. This article has briefly discussed seven major areas, where AI has been used easily to handle constraints such as power system stability assessments, power system forecasting studies, power quality problems, optimization of generation scheduling, 5G network data communications, SCADA intrusion detection and transformer fault identification.

show abstract

Section: Artificial Intelligence In Power Trannsformer Fault Identifi...mentioning

confidence: 99%

Artificial Intelligence and its applications in Renewable Integrated Power Systems

E.R.¹,

Joy²,

Bansal³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…An oil-immersed transformer in the long-term operation process can incur an electrical fault or thermal fault. When these faults occur, the insulating materials in the transformer will age and decompose, resulting in some characteristic fault gases, such as C 2 H 2 , CH 4 , and H 2 [8][9][10][11][12]. Studies have shown that detecting the composition and content of these characteristic fault gases is helpful to discover and judge the type and degree of faults.…”

Section: Introductionmentioning

confidence: 99%

Gas-Sensing Properties of Dissolved Gases in Insulating Material Adsorbed on SnO2–GeSe Monolayer

Guo

Liang²,

Yang³

et al. 2022

Chemosensors

View full text Add to dashboard Cite

In a transformer, the insulation materials will produce different dissolved gases due to various faults in the operation of the transformer, in which C2H2, CH4, and H2 are the main dissolved gases. In this study, the adsorption characteristics of the above three gases on the SnO2–GeSe monolayer surface were discussed and analyzed based on the density functional theory. The adsorption energy, transfer charge, geometric structure parameters, electronic density of states, electronic local function, charge difference density, and recovery time were calculated and compared to characterize the gas-sensing adsorption mechanism. The results showed that the SnO2–GeSe monolayer exhibited good adsorption capacity, selectivity, and repeatability for the three characteristic dissolved gases. After adsorbing CH4 gas molecules, the conductivity of the SnO2–GeSe monolayer decreased. After adsorbing C2H2 and H2 gas molecules, the conductivity of the SnO2–GeSe monolayer increased. Therefore, the SnO2–GeSe monolayer has great application potential in the real-time monitoring of dissolved gases in insulating materials, which may become a new type of resistive gas sensor.

show abstract

Power Transformer Fault Diagnosis with Intrinsic Time-Scale Decomposition and XGBoost Classifier

Sami

Bhuiyan

2021

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

An EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier

Cited by 6 publications

References 16 publications

Artificial Intelligence and its applications in Renewable Integrated Power Systems

Artificial Intelligence and its applications in Renewable Integrated Power Systems

Gas-Sensing Properties of Dissolved Gases in Insulating Material Adsorbed on SnO2–GeSe Monolayer

Power Transformer Fault Diagnosis with Intrinsic Time-Scale Decomposition and XGBoost Classifier

Contact Info

Product

Resources

About