Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Load-frequency control

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Transcranial photobiomodulation mitigates neuroinflammation by suppressing the activation of neurotoxic microglia through inhibition of the cGAS-STING pathway following intracerebral hemorrhage in mice.

Chinese medical journal·2026

Same author

Polarization-controlled hybrid laser with high-power, narrow-linewidth, and widely tunable NIR emission.

Optics letters·2026

Same author

Intensity noise suppression in pulsed lasers via combining relaxation oscillation with nonlinear frequency conversion.

Optics express·2026

Same author

High frequency-reproducibility nanosecond pulsed laser with high output power.

Optics express·2026

Same author

Realization of High-Power Single-Frequency Continuous-Wave Tunable 689 nm Laser.

Micromachines·2026

Same author

Self-Heating of Top-Down Manufactured ITO Sensors for Accurately Monitoring ppb-Level O<sub>3</sub>.

Nano letters·2026

Same journal

Correction: Kang et al. Fluid Flow to Electricity: Capturing Flow-Induced Vibrations with Micro-Electromechanical-System-Based Piezoelectric Energy Harvester. <i>Micromachines</i> 2024, <i>15</i>, 581.

Micromachines·2026

Same journal

Femtosecond Laser Texturing of Wood Coatings with Bio-Based Epoxy and Wax Additives for Enhanced Hydrophobicity.

Micromachines·2026

Same journal

Engineering of Optoelectronic Devices for Renewable Energy Applications.

Micromachines·2026

Same journal

Phase Transformation and Electrochemical Behavior of Hexagonal TiO<sub>2</sub> Nanotubes Under Different Annealing Temperatures and Heating Rates.

Micromachines·2026

Same journal

Process Optimization and Predictive Modeling of Femtosecond Laser Precision Milling for Commercial PMMA Slices.

Micromachines·2026

Same journal

A Hybrid Preprocessing Multi-Objective Surrogate Model for Thermal MEMS Actuators.

Micromachines·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 3, 2025

Continuous-wave Thulium Laser for Heating Cultured Cells to Investigate Cellular Thermal Effects

Continuous-wave Thulium Laser for Heating Cultured Cells to Investigate Cellular Thermal Effects

Published on: June 30, 2017

Performance Improvement of Single-Frequency CW Laser Using a Temperature Controller Based on Machine Learning.

Haoming Qiao¹, Weina Peng¹, Pixian Jin^1,2

¹State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan 030006, China.

|July 27, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning-based temperature control system for all-solid-state lasers. The system significantly enhances laser performance, stability, and environmental adaptability.

Keywords:

BP neural network PID control machine learning stable single-frequency laser temperature control

More Related Videos

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

Published on: November 22, 2019

Related Experiment Videos

Last Updated: Sep 3, 2025

Continuous-wave Thulium Laser for Heating Cultured Cells to Investigate Cellular Thermal Effects

Continuous-wave Thulium Laser for Heating Cultured Cells to Investigate Cellular Thermal Effects

Published on: June 30, 2017

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

Published on: November 22, 2019

Area of Science:

Laser Physics
Control Systems Engineering
Artificial Intelligence

Background:

All-solid-state single-frequency continuous-wave (CW) lasers require precise temperature control for optimal performance.
Traditional control methods may struggle with environmental variations, impacting laser stability and output power.
Machine learning offers potential for adaptive and robust control solutions.

Purpose of the Study:

To develop and evaluate a novel machine learning-based temperature control system for high-power all-solid-state single-frequency CW lasers.
To enhance the output characteristics, environmental adaptability, and stability of the laser system.
To investigate the integration of back propagation (BP) neural networks with proportion-integration-differentiation (PID) control for adaptive temperature management.

Main Methods:

Implementation of a temperature control system utilizing machine learning algorithms.
Integration of a back propagation (BP) neural network with a proportion-integration-differentiation (PID) control algorithm.
Adaptive adjustment of PID control parameters based on environmental variations.

Main Results:

Dramatically enhanced control speeds and abilities for laser element temperatures.
Significant improvement in the output characteristics of the single-frequency CW laser.
Greatly improved adaptability to environmental changes and enhanced laser stability.

Conclusions:

The developed machine learning-based temperature control system effectively improves the performance of all-solid-state single-frequency CW lasers.
Adaptive control through the combination of BP neural networks and PID algorithms leads to superior temperature management.
The system demonstrates enhanced stability and environmental adaptability, crucial for high-power laser applications.