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Transformers in Distribution System01:27

Transformers in Distribution System

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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...
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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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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...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Types Of Transformers01:16

Types Of Transformers

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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...
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Related Experiment Video

Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Anomaly detection in urban lighting systems using autoencoder and transformer algorithms.

Tomasz Śmiałkowski1, Andrzej Czyżewski2

  • 1TSTRONIC Sp. z o. o., 19 Benzynowa St., 83-011, Gdańsk, Poland. tomasz.smialkowski@tstronic.eu.

Scientific Reports
|October 15, 2025
PubMed
Summary

Machine learning algorithms effectively detect anomalies in road lighting systems. The Autoencoder model shows superior accuracy and efficiency for real-time energy consumption analysis and system management.

Keywords:
Anomaly detectionAutoencoderComputational efficiencyTransformerUrban lighting systems

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Road lighting management systems require continuous, real-time anomaly detection.
  • Online anomaly detection is crucial for maintaining system reliability and efficiency.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning algorithms for anomaly detection in lighting systems.
  • To compare the performance of Autoencoder and Transformer algorithms against traditional methods.

Main Methods:

  • Analysis of electricity meter records using Autoencoder and Transformer machine learning algorithms.
  • Comparison with a simple energy consumption comparison mechanism.
  • Evaluation using classification metrics: error matrix, sensitivity, precision, and F1-score.

Main Results:

  • Autoencoder achieved a high F1-score of 0.9565 with lower computational cost.
  • Transformer demonstrated effective anomaly detection with an F1-score of 0.8125.
  • Autoencoder implementation on an ILED platform provided anomaly detection within a 15-minute delay.

Conclusions:

  • Machine learning algorithms offer significant advantages for anomaly detection in lighting systems.
  • Autoencoder is a highly accurate and resource-efficient solution for urban lighting management.
  • The study highlights the potential to improve lighting system reliability and efficiency through AI-driven anomaly detection.