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Transformers in Distribution System01:27

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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.
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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...
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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:
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The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient 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.
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Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers.

Chandra Prakash Beura1, Jorim Wolters1, Stefan Tenbohlen1

  • 1Institute of Power Transmission and High Voltage Technology (IEH), University of Stuttgart, 70569 Stuttgart, Germany.

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Summary
This summary is machine-generated.

A new pathfinding algorithm speeds up AI training for power transformer monitoring. This method efficiently generates data for artificial intelligence (AI) systems to precisely locate partial discharge (PD) sources, improving transformer reliability.

Keywords:
UHFcondition monitoringlocalizationpartial dischargepathfinding algorithmpower transformer

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Area of Science:

  • Electrical Engineering
  • Computational Electromagnetics
  • Artificial Intelligence

Background:

  • Power transformers require robust monitoring systems for detecting partial discharge (PD) sources.
  • Current AI-based PD localization methods rely on costly experimental or simulation data.
  • Existing electromagnetic simulations generate excessive data, hindering efficient AI training.

Purpose of the Study:

  • To develop a computationally efficient method for generating training data for AI-based PD localization in power transformers.
  • To bypass time-consuming experimental data collection and electromagnetic simulations.
  • To enhance the precision and usability of AI monitoring systems for PD sources.

Main Methods:

  • Utilized Dijkstra's algorithm combined with line-of-sight propagation algorithms.
  • Applied the algorithm to a 3D CAD model of a 300 MVA power transformer.
  • Calculated electromagnetic wave paths and time-of-flight information from PD sources.

Main Results:

  • The pathfinding algorithm generated accurate time-of-flight data for AI training.
  • Achieved accuracy comparable to traditional electromagnetic simulation software.
  • Demonstrated significantly faster computation times compared to simulations.

Conclusions:

  • A tailored pathfinding algorithm offers an efficient alternative for generating AI training data for PD localization.
  • This approach enhances the feasibility of AI-based monitoring systems for power transformers.
  • The method promises improved reliability and precision in detecting PD sources.