Fuzzy tuned PID controller for two area system using matlab
In this simulation model, we delve into the dynamics of two interconnected power areas, each with its governor, turbine, and generator load model. The frequency control between the areas is accomplished through PSO-tuned Fuzzy Proportional-Integral-Derivative (PID) controllers. This blog post will explore the system components, control mechanisms, and simulation results.
Two-Area System Model:
Area 1 and Area 2 connected via tie lines.
Each area has a governor, turbine, and generator load model.
Governor, Turbine, and Load Models:
Parameters for the models derived from a reputable source.
Similar models used for both Area 1 and Area 2.
PSO-Tuned Fuzzy PID Controllers:
Fuzzy PID controllers used for frequency control.
Parameters tuned using Particle Swarm Optimization (PSO).
Frequency Control Objective:
Maintain changes in frequency (Del Omega) near zero.
Achieved through coordinated power exchange between Area 1 and Area 2.
Fuzzy PID Controllers:
PSO-tuned for optimal performance.
Receives area control error and its rate of change as inputs.
Output control signals for governor adjustment.
Simulation and Results:
Load Disturbance Scenario:
Change in load conditions introduced at different time intervals.
Simulation results observed for the change in frequency.
PSO used to optimize parameters of the Fuzzy PID controllers.
Iterative process to minimize the area control error.
Visualization of Controller Parameters:
Utilizing MATLAB scopes to visualize the changing values of KP, KI, and KD.
Frequency Control under Load Changes:
Demonstrated effective frequency control under varying load conditions.
Change in frequency (Del Omega) approaching zero after disturbances.
PSO-Tuned Fuzzy PID Performance:
PSO ensures optimal tuning of Fuzzy PID parameters.
Visual representation of KP, KI, and KD during simulation.
This simulation showcases the robustness of the PSO-tuned Fuzzy PID controllers in maintaining frequency stability in a two-area power system. The controllers adapt to changing load conditions, ensuring effective power exchange between the areas.
Further exploration can involve studying the system's response to different disturbances, optimizing PSO parameters, and extending the model to multi-area systems. Additionally, real-world data integration can enhance the model's accuracy.
Thank you for joining us in understanding the intricacies of optimal frequency control in two-area power systems. Subscribe to our channel and hit the bell icon for more insightful simulations. Until next time, farewell!