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Related Concept Videos

Load-frequency control01:28

Load-frequency control

126
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

Multimachine Stability

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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.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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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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Turbine-Governor Control01:17

Turbine-Governor Control

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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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Time and frequency -Domain Interpretation of PI Control01:27

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Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
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Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

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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.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
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A novel artificial intelligence based multistage controller for load frequency control in power systems.

Mostafa Jabari1, Davut Izci2,3, Serdar Ekinci2

  • 1Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Scientific Reports
|November 28, 2024
PubMed
Summary

This study introduces an optimized load frequency control (LFC) strategy using a novel bio-dynamic grasshopper optimization algorithm (BDGOA) to stabilize power systems. The new controller significantly reduces frequency and tie-line power fluctuations, enhancing overall grid stability.

Keywords:
Advanced optimization methodsArtificial intelligenceBio-dynamic grasshopper optimization algorithmHybrid power systemsIntelligent controlLoad frequency controlMulti-stage controller

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Renewable Energy Integration

Background:

  • Power systems face frequency and tie-line power fluctuations due to imbalances between power generation and demand.
  • Load Frequency Control (LFC) is critical for maintaining stable power system operation.
  • Integrating renewable sources like photovoltaic (PV) power plants adds complexity to LFC.

Purpose of the Study:

  • To optimize the parameters of a novel multi-stage TDn(1+PI) controller for a two-area power system.
  • To improve the dynamic performance of the power system, specifically reducing frequency and tie-line power oscillations.
  • To apply a new meta-heuristic optimization technique, the bio-dynamic grasshopper optimization algorithm (BDGOA), for controller tuning.

Main Methods:

  • Development of a multi-stage TDn(1+PI) controller combining tilt-derivative with N filter (TDn) and proportional-integral (PI) elements.
  • Application of the bio-dynamic grasshopper optimization algorithm (BDGOA) for tuning the proposed controller's parameters.
  • Simulation and analysis of a two-area power system including a reheat thermal generator and a PV power plant under various load conditions and system nonlinearities.

Main Results:

  • The BDGOA-TDn(1+PI) controller significantly reduced overshoot in system frequency and tie-line power changes (by up to 75%).
  • Faster settling times for oscillations were achieved (up to 60% improvement).
  • A lower integral of time-weighted absolute error (ITAE) was observed (50% reduction), indicating superior performance compared to conventional controllers.

Conclusions:

  • The proposed BDGOA-TDn(1+PI) controller offers a robust and effective solution for load frequency control in complex power systems.
  • The BDGOA algorithm provides efficient parameter tuning for advanced LFC controllers.
  • The controller demonstrates significant improvements in stability and dynamic response, even under challenging operating conditions and nonlinearities.