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Related Concept Videos

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.
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Distribution Reliability and Automation01:25

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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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Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
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Power System Three-Phase Short Circuits01:21

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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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Instrument Transformers01:23

Instrument Transformers

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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...
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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.
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Probabilistic Prognostics and Health Management of Power Transformers Using Dissolved Gas Analysis Sensor Data and

Fabio Norikazu Kashiwagi1, Miguel Angelo de Carvalho Michalski1, Gilberto Francisco Martha de Souza1

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This study introduces a new probabilistic framework for power transformer health management using Dissolved Gas Analysis (DGA). It improves early fault detection and predictive maintenance by analyzing gas trends with uncertainty quantification.

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DGAforecastingpower grid reliabilityprognostics and health managementtime-series forecastinguncertainty quantification

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Data Science

Background:

  • Power transformer failures cause significant grid disruptions and financial losses.
  • Traditional Dissolved Gas Analysis (DGA) methods use deterministic thresholds, ignoring uncertainty in degradation.
  • Condition monitoring of critical power grid assets requires advanced diagnostic techniques.

Purpose of the Study:

  • To develop a probabilistic framework for Prognostics and Health Management (PHM) of power transformers.
  • To enable automated fault type estimation and failure likelihood assessment from DGA data.
  • To enhance predictive maintenance strategies for power transformers.

Main Methods:

  • Integration of self-adaptive Auto Regressive Integrated Moving Average (ARIMA) modeling with probabilistic Duval's graphical methods.
  • Time-series forecasting of dissolved gas dynamics with residual-based uncertainty quantification.
  • Development of a sequential pipeline for real-time fault tracking and reliability assessment.

Main Results:

  • The proposed framework enables probabilistic fault inference without relying on labeled datasets or expert rules.
  • Demonstrated improved diagnostic reliability and early fault detection capabilities through case studies.
  • Validated against international standards (IEC, IEEE, CIGRE) for power system components.

Conclusions:

  • The probabilistic framework offers a practical and interpretable PHM solution for sensor-enabled power grids.
  • Enhanced predictive maintenance strategies can be achieved through accurate forecasting of dissolved gas trends.
  • This approach addresses the limitations of deterministic diagnostic methods in power transformer monitoring.