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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
Energy Losses in Transformers01:21

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the copper windings...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
Reclosers and Fuses01:26

Reclosers and Fuses

Automatic circuit reclosers enhance the protection of distribution circuits by interrupting and auto-reclosing an AC circuit according to a preset sequence. They effectively manage temporary faults on overhead distribution lines, often caused by tree limbs or wildlife, by briefly disrupting service to improve overall reliability. However, contact with reclosers or energized broken conductors on the ground can pose serious hazards.
A comprehensive protection scheme for radial distribution...
Zones of Protection01:16

Zones of Protection

In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
Protective zones are defined by closed dashed lines, containing one or more components. A key characteristic of these zones is the strategic placement of...

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Related Experiment Videos

A Data-Driven Spatiotemporal Risk Assessment Framework for Transformer Overload in Distributed Renewable Energy

Chengjun Xie1, Chenhao Sun1, Yanzheng Liu1

  • 1State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science and Technology, Changsha 410114, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Transformer Overload Risk Assessment (TORA) approach to predict transformer overload risks in renewable energy systems. TORA uses heterogeneous sensor data for improved transformer condition monitoring and maintenance decisions.

Keywords:
cloud-based dynamic feature risk assessmentcloud–edge parameter alignment and adjustmentcondition monitoringedge-based static feature risk assessmentrisk score fusiontransformer overload risk assessment

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Predictive Maintenance

Background:

  • Distributed renewable energy systems introduce load fluctuations, increasing distribution transformer overload risk.
  • Transformer overload accelerates insulation aging, causes overheating, and reduces operational reliability.
  • Current transformer condition monitoring relies on heterogeneous data, making accurate risk prediction challenging due to resource limitations.

Purpose of the Study:

  • To propose a robust Transformer Overload Risk Assessment (TORA) approach for predicting overload risk under non-stationary load conditions.
  • To develop a method for integrating static and dynamic features from multisource sensing data for comprehensive risk assessment.
  • To enhance sensor-driven maintenance allocation and transformer condition monitoring.

Main Methods:

  • Constructing a feature matrix combining static (long-term drift) and dynamic (short-term fluctuations) features.
  • Utilizing Edge-based Static Feature Risk Assessment (E-SFRA) and Cloud-based Dynamic Feature Risk Assessment (C-DFRA) models.
  • Implementing a periodic calibration model (CE-PAA) via a cloud-edge loop for feedback.
  • Applying risk score fusion (RSF) to integrate static and dynamic risk assessments.

Main Results:

  • The TORA approach effectively transforms heterogeneous monitoring signals into calibrated risk information.
  • Demonstrated utility in a single power plant scenario for multisource sensor data fusion.
  • Provided valuable support for transformer condition monitoring and maintenance decision-making.

Conclusions:

  • The TORA approach offers a robust solution for transformer overload risk prediction in dynamic power systems.
  • It enables effective integration of multisource data for enhanced operational reliability.
  • Further validation with diverse field datasets is recommended to assess cross-scenario generalization.