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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...

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Multimachine Stability

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Related Experiment Video

Updated: Apr 17, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Automatic Learning of Fine Operating Rules for Online Power System Security Control.

Hongbin Sun, Feng Zhao, Hao Wang

IEEE Transactions on Neural Networks and Learning Systems

|February 14, 2015

Summary

An automated system discovers fine operating rules for smart grid security control. These rules, derived from real-time data and simulations, ensure accurate and interpretable power system security management.

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Interactive and Visualized Online Experimentation System for Engineering Education and Research

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Last Updated: Apr 17, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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Published on: February 14, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Area of Science:

Electrical Engineering
Power Systems
Control Systems

Background:

Smart grids require adaptive security control mechanisms.
Existing methods for deriving operating rules may not be efficient for real-time applications.

Purpose of the Study:

To develop an automatic system for discovering fine operating rules for smart grid security control.
To adapt security control to the dynamic nature of modern power systems.

Main Methods:

Utilizing real-time system state to identify critical flowgates.
Employing continuation power flow analysis for initial transfer capability computation.
Applying Monte Carlo simulations, feature selection, and linear least squares fitting for rule derivation.

Main Results:

The developed system successfully derived accurate and interpretable fine operating rules.
Validation on academic and Chinese provincial power systems confirmed the system's efficacy.
High-performance computing enabled online refreshing of rules every 15 minutes.

Conclusions:

The derived fine operating rules are suitable for real-time power system security control.
The automated discovery system enhances the adaptability and efficiency of smart grid security.