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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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Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
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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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Related Experiment Video

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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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A distributed model predictive control based load frequency control scheme for multi-area interconnected power system

Yang Zheng1, Jianzhong Zhou1, Yanhe Xu1

  • 1School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.

ISA Transactions
|March 28, 2017
PubMed
Summary

This study introduces a distributed model predictive control for load frequency control (MPC-LFC) to enhance power system frequency regulation. The method uses Laguerre functions to reduce computation, ensuring stability and effective constraint handling for improved performance.

Keywords:
Constraint treatmentDistributed controlLaguerre functionsLoad frequency controlModel predictive control

Related Experiment Videos

Last Updated: Mar 5, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

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Published on: February 14, 2025

1.1K

Area of Science:

  • Electrical Engineering
  • Control Systems
  • Power Systems

Background:

  • Power systems require robust frequency regulation to maintain stability.
  • Traditional load frequency control (LFC) methods face challenges with complex dynamics and computational load.
  • Model Predictive Control (MPC) offers advanced control capabilities but can be computationally intensive.

Purpose of the Study:

  • To propose a novel distributed model predictive control based load frequency control (MPC-LFC) scheme.
  • To enhance control performance and efficiency in power system frequency regulation.
  • To address the computational burden associated with MPC in LFC.

Main Methods:

  • Utilized orthonormal Laguerre functions to approximate the predicted control trajectory, reducing computational complexity.
  • Implemented a terminal equality constraint within the online quadratic optimization to ensure closed-loop stability.
  • Formulated specific treatments for typical load frequency control constraints using Laguerre-based approaches.

Main Results:

  • The proposed distributed MPC-LFC scheme demonstrated improved control performances in simulations.
  • The use of Laguerre functions effectively reduced the computational burden for large prediction horizons.
  • The stability of the closed-loop system was successfully achieved through the proposed constraint method.
  • Effectiveness and superiority over comparative methods were validated in two different interconnected power systems.

Conclusions:

  • The distributed MPC-LFC scheme offers a computationally efficient and stable solution for power system frequency regulation.
  • Laguerre function approximation is a viable technique for reducing MPC computational load in LFC.
  • The proposed method effectively handles system constraints, leading to superior performance compared to existing approaches.