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Related Concept Videos

Load-frequency control01:28

Load-frequency control

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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Multimachine Stability01:25

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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.
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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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Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
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Generator Voltage Control01:21

Generator Voltage Control

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Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
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Control of Power Flow01:30

Control of Power Flow

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There are several methods to control power flow in power systems:
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Revealing drivers and risks for power grid frequency stability with explainable AI

Johannes Kruse1,2, Benjamin Schäfer3,4, Dirk Witthaut1,2

  • 1Forschungszentrum Jülich, Institute of Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE), 52425 Jülich, Germany.

Patterns (New York, N.Y.)
|November 25, 2021
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Summary

This study introduces an explainable machine learning model to predict electric power grid frequency stability. Load and generation ramps are key drivers, with varying impacts from different generation technologies across European grids.

Keywords:
RoCoFexplainable artificial intelligenceexplanationsfrequencymachine learningnadirpower gridpower systemstability

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Area of Science:

  • Power Systems Engineering
  • Machine Learning Applications
  • Grid Stability Analysis

Background:

  • Electric power system stability relies on maintaining strict grid frequency limits.
  • Frequency deviations and control efforts increase due to fluctuations and external impacts.
  • Traditional machine learning models offer limited insights due to their black-box nature.

Purpose of the Study:

  • To introduce an explainable machine learning model for predicting frequency stability indicators.
  • To identify key features and risk factors influencing frequency stability.
  • To provide insights into the dynamics of European synchronous areas.

Main Methods:

  • Development of an explainable machine learning model.
  • Application of Shapley additive explanations (SHAP) for feature importance.
  • Analysis of frequency stability indicators across three European synchronous areas.

Main Results:

  • The model accurately predicts frequency stability indicators.
  • Load and generation ramps significantly influence frequency gradients.
  • Three classes of generation technologies exhibit distinct impacts on frequency stability.
  • Control efforts are influenced by ramps and electricity prices, varying by grid and time.

Conclusions:

  • Explainable AI provides valuable insights into power system frequency dynamics.
  • Understanding feature importance aids in managing grid stability.
  • Renewable energy's role and forecasting errors differ across European grids.