⚡🧠 MATLAB Simulation of Fuzzy-Tuned PID Controller for Two-Area AGC System
- lms editor
- Dec 5, 2025
- 3 min read
A complete explanation of load-frequency control using fuzzy intelligence in interconnected power systems
Maintaining frequency stability is one of the most critical challenges in large power systems. When two or more areas are interconnected through tie-lines, a sudden change in load in one area can directly affect the frequency and power flow in the other. To ensure smooth system operation, Automatic Generation Control (AGC) is applied to keep the frequency deviation close to zero.
In this article, we explore the MATLAB simulation of a two-area power system controlled by a Fuzzy-Tuned PID controller, which offers faster response and improved dynamic behavior compared to conventional integral controllers.
🌐 Introduction to Two-Area AGC System
A standard two-area interconnected power system consists of:
Governor
Turbine
Generator-load dynamics
Tie-line power flow
Frequency control loop for each area
Each area is affected by load variations within its own region and by disturbances in the neighboring area due to tie-line coupling.
The objective of AGC is to maintain the change in frequency (Δω) of both areas as close to zero as possible, regardless of load variations.
🔄 How Load Change Affects the System
When load increases in Area 1 or Area 2:
Frequency begins to drop
Tie-line exchanges power to compensate
Both areas experience transient deviations in Δω
AGC must restore frequency and maintain scheduled tie-line flow
Traditionally, an integral controller is used to minimize Area Control Error (ACE). However, it has limitations such as:
Slower settling time
Poor disturbance rejection
Fixed gain parameters
To overcome these issues, a Fuzzy-Tuned PID Controller is implemented.
🧠 Fuzzy-Tuned PID Controller – How It Works
The fuzzy controller receives two inputs:
Area Control Error (ACE)
Rate of Change of ACE
Using fuzzy logic rules, the controller dynamically adjusts:
Kp (Proportional Gain)
Ki (Integral Gain)
Kd (Derivative Gain)
These gains are multiplied with the error signals and combined to produce a robust control action that drives Δω₁ = 0 and Δω₂ = 0.
✔️ Why Fuzzy Tuning?
Adaptive gain tuning
Fast transient response
High robustness to load variations
Better handling of nonlinearities in power system models
⚙️ System Modeling in MATLAB/Simulink
The two-area system includes:
🔸 Area 1 Components:
Governor transfer function
Turbine dynamics
Generator-load model
🔸 Area 2 Components:
Identical structure and parameter set
These parameters are referenced from a standard AGC textbook and used in the Simulink implementation.
🔸 Tie-Line Power Flow
The areas exchange power based on frequency deviation and load condition.
🔸 Controller Implementation
Both areas use Fuzzy-Tuned PID controllers connected to their respective governors.
🔍 Simulation Scenarios & Results
➡️ Case 1: Load Change of 0.2 p.u.
Initially Δω₁ and Δω₂ show deviations
After ~25 seconds, both frequencies return to zero
System achieves stable operation despite the disturbance
➡️ Case 2: Step Change at t = 50s
Load changes from 0.2 p.u. → 0.1 p.u.
Disturbance is observed
Fuzzy PID compensates quickly
Frequencies stabilize to zero again
➡️ Case 3: Load Increased to 0.3 p.u.
Larger disturbance results in higher deviation
Controller adjusts Kp, Ki, and Kd dynamically
Δω₁ and Δω₂ converge back to zero efficiently
📊 Fuzzy Logic Structure
The fuzzy file contains:
Inputs: Error and Change of Error
Outputs: Kp, Ki, Kd
Membership functions for all variables
Rule base defining the relationship between error and PID parameters
During simulation, Kp, Ki, Kd adjust continuously based on real-time operating conditions. A scope is used to visualize these variations.
🎯 Key Advantages of Fuzzy-Tuned PID in AGC
Significant reduction in settling time
Improved disturbance rejection
Enhanced robustness under varying load profiles
Better handling of nonlinearities in turbine and generator models
Stable tie-line power exchange between the two areas
🏁 Conclusion
This MATLAB simulation demonstrates the powerful capabilities of a Fuzzy-Tuned PID Controller in managing AGC for a two-area interconnected system. The controller dynamically tunes PID gains in real time, enabling faster frequency recovery and stable system operation under a wide range of load disturbances.
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