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Energy Losses in Transformers01:21

Energy Losses in Transformers

1.0K
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...
1.0K
Instrument Transformers01:23

Instrument Transformers

170
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
170
Transformers in Distribution System01:27

Transformers in Distribution System

180
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...
180
Transformers01:26

Transformers

1.3K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.3K
Three-Winding Transformers01:19

Three-Winding Transformers

331
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
331
Types Of Transformers01:16

Types Of Transformers

1.1K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.1K

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Related Experiment Video

Updated: Oct 13, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder.

Imene Mitiche1, Tony McGrail2, Philip Boreham2

  • 1Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, self-supervised machine learning method for detecting anomalies in high-voltage transformer bushings. The system uses a Long Short-Term Memory Auto-Encoder to monitor current and phase angle, preventing power outages.

Keywords:
LSTManomaly detectionauto-encoderinsulation failuretransformer bushings

Related Experiment Videos

Last Updated: Oct 13, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K

Area of Science:

  • Electrical Engineering
  • Power Systems
  • Machine Learning

Background:

  • Bushing failures in high-voltage (HV) power transformers can lead to significant financial losses due to power outages.
  • Identifying insulation deterioration is crucial for preventing catastrophic bushing failures.
  • Continuous monitoring of bushing measurements can indicate equipment condition anomalies.

Purpose of the Study:

  • To develop a real-time anomaly detection method for HV transformer bushings.
  • To utilize machine learning for monitoring current magnitude and phase angle from bushing taps.
  • To ensure the reliability and health of critical power infrastructure.

Main Methods:

  • A Long Short-Term Memory Auto-Encoder (LSTMAE) network was employed for anomaly detection.
  • The LSTMAE model learns normal operational patterns of bushing current and phase angle.
  • Anomaly detection is based on evaluating changes in measurements using the Mean Absolute Error (MAE) metric.

Main Results:

  • The proposed machine learning method successfully detected anomalous events in real-world data.
  • The system demonstrated real-time anomaly detection capabilities for HV transformer bushings.
  • The approach proved to be fast, self-supervised, and flexible in identifying deviations.

Conclusions:

  • The LSTMAE-based method provides an effective solution for real-time anomaly detection in HV transformer bushings.
  • This approach enhances the reliability of power supply by preemptively identifying potential equipment failures.
  • The self-supervised and flexible nature of the method makes it suitable for practical industrial applications.