Real‐time monitoring of transformer hot‐spot temperature based on nameplate data

Rezaeealam, Behrooz; Askary, Saeid

doi:10.1049/gtd2.12884

Cited by 3 publications

(1 citation statement)

References 20 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Monitoring alone is insufficient, and it is necessary to detect abnormal energy consumption data in real-time [46]. In article [47], real-time monitoring was presented ac-cording to IEEE Std C57.110-2018 [48], which evaluates the increase in the temperature of the transformer due to harmonic distortion. Publication [49] presented monitoring of insulation ageing, and neural networks were used for evaluation in publication [50].…”

Section: Introductionmentioning

confidence: 99%

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Plienis,

Deveikis,

Jonaitis

et al. 2023

Energies

View full text Add to dashboard Cite

The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the increasing integration of renewable energy sources introduces heightened fluctuations, rendering the prediction and simulation of working modes more challenging. This paper presents an improved algorithm for calculating power transformer losses attributed to harmonics, with a comprehensive validation against simulation results obtained from the Power Factory application and real-world measurements. The advantages of the algorithm are that all evaluations are performed in real-time based on single-point measurements, and the algorithm was easy to implement in a Programmable Logic Controller (PLC). This allows us to receive the exchange of information to energy monitoring systems (EMSs) or with Power factor Correction Units (PFCUs) and control it. To facilitate a more intuitive understanding and visualization of potential hazardous scenarios related to resonance, an extra Dijkstra algorithm was implemented. This augmentation enables the identification of conditions, wherein certain branches exhibit lower resistance than the grid connection point, indicating a heightened risk of resonance and the presence of highly distorted currents. Recognizing that monitoring alone does not inherently contribute to increased energy efficiency, the algorithm was further expanded to assess transformer losses across a spectrum of Power Factory Correction Units power levels. Additionally, a command from a PLC to a PFCU can now be initiated to change the capacitance level and near-resonance working mode. These advancements collectively contribute to a more robust and versatile methodology for evaluating power transformer losses, offering enhanced accuracy and the ability to visualize potentially critical resonance scenarios.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Plienis,

Deveikis,

Jonaitis

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

Research on Winding Electrodynamic Force and Hot Spot Temperature Rise of Environmental Stereo Wound Core Transformer

Wu,

Tian,

Cheng

et al. 2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Exploring transformer fault detection using RFID technology

2024

RFT

View full text Add to dashboard Cite

Real-time monitoring and fault diagnosis of transformers are essential for the stable power system operation. This paper presents an RFID-based transformer fault feature extraction and classification algorithm. Experiments show that monitored current signals are stable while the temperature peak is 356°C. Hilbert decomposition reveals regular current and voltage patterns that can be used as fault indicators. Signal strength classification accuracy reached 80% . At rated load, the transformer temperature soared to 186°C, indicating overheating issues. The monitoring during a sample day showed that overload events were concentrated from 16:00-20:00, which required attention. The approach helps accurately identify transformer fault types from real-time RFID data for proactive maintenance. Compared to reactive repairs after failures, this not only improves employee productivity but also reduces costs. Based on customized RFID deployment, the algorithm contributes to the stability and economy of power infrastructure.

show abstract

Real‐time monitoring of transformer hot‐spot temperature based on nameplate data

Cited by 3 publications

References 20 publications

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

Research on Winding Electrodynamic Force and Hot Spot Temperature Rise of Environmental Stereo Wound Core Transformer

Exploring transformer fault detection using RFID technology

Contact Info

Product

Resources

About