Survey of different sensors employed for the power transformer monitoring

Samimi, Mohammad Hamed; Ilkhechi, Hossein Dadashi

doi:10.1049/iet-smt.2019.0103

Cited by 39 publications

(10 citation statements)

References 74 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…{(𝒙 𝟏 , 𝒇(𝒙 𝟏 )), (𝒙 𝟐 , 𝒇(𝒙 𝟐 )), … , (𝒙 𝒏 , 𝒇(𝒙 𝒏 ))} (6) and the interpolation formula is expressed as:…”

Section: Newton Interpolation Preprocessingmentioning

confidence: 99%

“…These methods mainly include expert systems [2] , artificial neural networks [3] , Petri nets [4] , Bayesian networks [5] , and so on. For example, Samimi et al [6] give the comprehensive use of sensors including electrical sensors, mechanical sensors, optical sensors, chemical sensors, and acoustic sensors to monitor the operational statuses of substations. These sensors can monitor the parameters such as current, voltage, partial discharge, gas content, temperature, and oil content.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault detection method for substation in smart power grid: a random forest approach

Cai,

Xia,

Zhu

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

View full text Add to dashboard Cite

As the scale and complexity of power grid continue to increase, some abnormal events pose a significant threat to the security and stability of the power grid. This paper presents a novel approach based on the Random Forest algorithm for the substation fault detection. The approach is designed to improve the accuracy and efficiency of fault detection of substation equipment. By utilizing the machine learning techniques, specifically taking the decision tree algorithm as the base model, and following the principles of ensemble learning, a Random Forest algorithm is designed from multiple decision trees. The bagging technique is employed to create an ensemble learning model. The data preprocessing phase includes some crucial steps such as feature selection, interpolation preprocessing, normalization preprocessing, and the creations of training and testing sets. Additionally, appropriate evaluation metrics are chosen to assess the performance of our proposed approach. The experiments conducted on substation fault data collected from Suzhou City from January 2023 to August 2023 demonstrate the effectiveness of the approach. The dataset comprises six monitoring signals regarding the substations, with labels indicating the respective types of substation faults. The experiment results indicate that our proposed Random Forest-based approach achieves a extremely high accuracy rate compared to other baseline approaches. This approach can also help to enhance the substation fault detection capabilities and ensure the reliability and stability of power grid system.

show abstract

“…{(𝒙 𝟏 , 𝒇(𝒙 𝟏 )), (𝒙 𝟐 , 𝒇(𝒙 𝟐 )), … , (𝒙 𝒏 , 𝒇(𝒙 𝒏 ))} (6) and the interpolation formula is expressed as:…”

Section: Newton Interpolation Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault detection method for substation in smart power grid: a random forest approach

Cai,

Xia,

Zhu

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

View full text Add to dashboard Cite

show abstract

“…It is therefore of paramount importance to correctly measure and control this parameter. It is observed that use of optical fiber sensors in the linings of phase windings can accurately determine the thermal hot spots of the transformer windings [ [6] , [7] , [8] , [9] , [10] ]. However, this technique is capital intensive and also not practically applicable to in-service and aged transformers without retiring them from operation which may disrupt the continuous supply of power.…”

Section: Introductionmentioning

confidence: 99%

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Mharakurwa,

Gicheru

2024

Heliyon

View full text Add to dashboard Cite

“…The increasing renewable electricity market results in challenges in such system and power transformers. A failure of power transformer can lead to serious consequential losses, therefore, monitoring of the transformer condition and power lines is essential in order to obtain reliable power supply network [1][2][3][4]. Degradation of transformers is caused by ageing process and corrosion, which leads to deterioration of transformer performance [5,6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Optical Fiber Macrobendings in Temperature Sensor Dedicated for Power Transformer Monitoring

Guzowski,

Łakomski,

Shatarah

2023

PAR

View full text Add to dashboard Cite

Optical fiber sensing techniques are recognized as very promising in diagnostic and condition monitoring of power transformers. According to the IEC standard 60076-2, the winding hot-spot temperature can be designated with the optical fiber sensor. In this paper, the investigation of the influence of macrobending of the optical fiber temperature sensor on the sensing performance is presented. The obtained results prove that the optical fiber sensor wrapped six times around the 14 mm cylinder still provides temperature sensing abilities.

show abstract

Survey of different sensors employed for the power transformer monitoring

Cited by 39 publications

References 74 publications

Fault detection method for substation in smart power grid: a random forest approach

Fault detection method for substation in smart power grid: a random forest approach

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Evaluation of Optical Fiber Macrobendings in Temperature Sensor Dedicated for Power Transformer Monitoring

Contact Info

Product

Resources

About