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Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

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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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Line Protection with Impedance Relays01:27

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Coordinating time-delay overcurrent relays in complex radial systems and directional overcurrent relays in multi-source transmission loops can be challenging. Impedance relays address these issues by responding to the voltage-to-current ratio, specifically measuring the apparent impedance of a line. These relays become more sensitive during faults as current increases and voltage decreases, thereby reducing the apparent impedance.
Under normal conditions, low load currents keep the measured...
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Bus Impedance Matrix01:24

Bus Impedance Matrix

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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
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Directional Relays01:25

Directional Relays

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Directional relays, essential for managing unidirectional fault currents, enhance the safety and efficiency of power systems. On power lines equipped with directional relays, faults downstream (to the right) of the current transformer typically cause the fault current to lag the bus voltage by approximately 90 degrees, known as the forward direction. In contrast, upstream (left-side) faults may result in the fault current leading the bus voltage by nearly 90 degrees, termed the reverse...
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Reclosers and Fuses01:26

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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.
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Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation, and Optimal Measurement

Andrei Afonin1, Michael Chertkov2

  • 1Department of Intelligent Information Systems and Technologies, Moscow Institute of Physics and Technologies, Moscow, Russia.

Frontiers in Big Data
|September 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces advanced machine learning for power transmission systems. It develops neural networks to locate faulty lines, predict post-fault states, and optimize phasor measurement unit (PMU) placement for improved grid reliability.

Keywords:
fault localizationneural networksphysics-informed machine learningpower systemstate estimation

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Area of Science:

  • Electrical Engineering
  • Applied Mathematics
  • Computer Science

Background:

  • Power transmission systems require robust monitoring for reliability.
  • Phasor Measurement Units (PMUs) offer critical data but coverage is often limited.
  • Accurate fault detection and localization are essential for grid stability.

Purpose of the Study:

  • To develop and compare neural network (NN) architectures for faulty line localization using PMU data.
  • To create machine learning schemes for predicting post-fault states and estimating system parameters.
  • To design an algorithm for optimizing PMU placement in power grids.

Main Methods:

  • Utilized a sequence of NNs, including linear regression, feed-forward NNs, AlexNet, graph convolutional NNs, and neural ordinary differential equations (ODEs).
  • Trained models on recorded pre-fault and post-fault states from PMUs under varying observability levels.
  • Developed dynamics-informed and neural ODE-based schemes for state prediction and parameter estimation.

Main Results:

  • Compared the performance of various NNs for fault localization across different observability scenarios.
  • Successfully demonstrated the capability of trained schemes to predict post-fault states and estimate system parameters.
  • Designed an NN-based algorithm for discovering optimal PMU placements.

Conclusions:

  • Advanced NN architectures show promise for accurate fault localization in under-observed power systems.
  • Integrated machine learning approaches can enhance power system monitoring, prediction, and control.
  • Optimized PMU placement strategies are crucial for maximizing the effectiveness of monitoring systems.