scholarly journals Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3561 ◽  
Author(s):  
Kunicki ◽  
Borucki ◽  
Cichoń ◽  
Frymus
Keyword(s):  
Hot Spot ◽  
Electrical Power ◽  
Real Life ◽  
Power Transformers ◽  
Measuring Systems ◽  
On Line ◽  
Method Accuracy ◽  
Technical Assessment ◽  
Spot Temperature

A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

A Novel Approach to Investigate the Hot-Spot Temperature Rise in Power Transformers

2015 ◽  
Vol 51 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Longnv Li ◽  
Shuangxia Niu ◽  
S. L. Ho ◽  
W. N. Fu ◽  
Yan Li
Keyword(s):  
Temperature Rise ◽  
Hot Spot ◽  
Power Transformers ◽  
Novel Approach ◽  
Spot Temperature

Oil Additives for the Evaluation of Hot Spot Temperature in Power Transformers During Overload Tests

1982 ◽  
Vol EI-17 (5) ◽  
pp. 414-422 ◽  
Author(s):  
M. Duval ◽  
J. Aubin ◽  
Y. Giguere ◽  
G. Pare ◽  
Y. Langhame
Keyword(s):  
Hot Spot ◽  
Power Transformers ◽  
Oil Additives ◽  
Spot Temperature

scholarly journals Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 39 ◽  
Author(s):  
Julia Vopava ◽  
Christian Koczwara ◽  
Anna Traupmann ◽  
Thomas Kienberger
Keyword(s):  
Power Grid ◽  
Electrical Power ◽  
Real Life ◽  
Transport Sector ◽  
Additional Stress ◽  
Distribution Grid ◽  
Power Sources ◽  
Grid Model ◽  
The Impact

To achieve climate goals, it is necessary to decarbonise the transport sector, which requires an immediate changeover to alternative power sources (e.g., battery powered vehicles). This change will lead to an increase in the demand for electrical energy, which will cause additional stress on power grids. It is therefore necessary to evaluate energy and power requirements of a future society using e-mobility. Therefore, we present a new approach to investigate the influence of increasing e-mobility on a distribution grid level. This includes the development of a power grid model based on a cellular approach, reducing computation efforts, and allowing time and spatially resolved grid stress analysis based on different load and renewable energy source scenarios. The results show that by using the simplified grid model at least seven times, more scenarios can be calculated in the same time. In addition, we demonstrate the capability of this novel approach by analysing the influence of different penetrations of e-mobility on the grid load using a case study, which is calculated using synthetic charging load profiles based on a real-life mobility data. The results from this case study show an increase on line utilisations with increasing e-mobility and the influence of producers at the same connection point as e-mobility.

Real-Time Temperature On-Line Monitoring and Analysis System for Transformers

2014 ◽  
Vol 521 ◽  
pp. 409-413 ◽  
Author(s):  
Ya Bo Chen ◽  
Yue Sun ◽  
Xu Ri Sun ◽  
Ge Hao Sheng ◽  
Xiu Chen Jiang
Keyword(s):  
Real Time ◽  
Hot Spot ◽  
Load Ratio ◽  
Distribution Transformers ◽  
Insulation Aging ◽  
On Line ◽  
Analysis System ◽  
Operation Status ◽  
Process Fault Diagnosis ◽  
Spot Temperature

The safe operation of power transformers mainly depends on proper functioning of insulation, whose status is revealed by temperatures. Applying ZigBee wireless network, a real-time temperature on-line monitoring and analysis system is developed to view the operation status of underground distribution transformers and process fault diagnosis. Furthermore, using the top-oil and hot-spot temperature calculation method in IEEE Std C57.91-1995, the system can compute a prediction of those temperatures with current load ratio and ambient temperature. System will display early warnings if temperatures are much higher than expected ones, in which way insulation aging can be handled in advance. Insulation fault and big disasters will be prevented.

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