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Load-frequency control01:28

Load-frequency control

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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Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies
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Artificial neural networks for laser frequency stabilization.

Lisa Winkler, Christian Nölleke

    Optics Express
    |October 20, 2023
    PubMed
    Summary

    An artificial neural network reliably identifies laser frequency stabilization references from spectra. Trained on simulated data, it accurately identifies iodine absorption lines in real-world measurements, proving robust across conditions.

    Area of Science:

    • Atomic, Molecular and Chemical Physics
    • Laser Physics
    • Computational Science

    Background:

    • Stabilizing laser emission frequency is crucial for precision applications.
    • Absolute references, like molecular absorption lines, are commonly used for frequency stabilization.
    • Automating the identification of these reference lines from spectral data is a key challenge.

    Purpose of the Study:

    • To develop an automated method for reliably identifying molecular absorption lines in laser spectra.
    • To demonstrate the effectiveness of an artificial neural network for this spectral analysis task.
    • To validate the neural network's performance using the iodine spectrum as a case study.

    Main Methods:

    • An artificial neural network (ANN) was designed and trained.

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  • The ANN was trained exclusively on simulated spectral data.
  • The trained ANN was then tested on experimentally measured spectral data, specifically targeting iodine absorption lines.

    • Main Results:

    • The artificial neural network successfully identified the desired absorption lines from the spectrum.
    • The network demonstrated robustness, performing accurately despite significant variations in operating and environmental conditions.
    • The use of simulated data for training proved effective for real-world application.

    Conclusions:

    • Artificial neural networks offer a viable automated solution for identifying spectral references in laser frequency stabilization.
    • Training ANNs with simulated data is a practical approach that generalizes well to measured data.
    • The developed method enhances the reliability and automation of laser frequency stabilization systems.