ANN Tuned PID : two-area load frequency control

Neural Network tuned PID controller for two-area load frequency control using MATLAB

Brief explanation of the steps involved in implementing a Neural Network tuned PID controller for two-area load frequency control using MATLAB:

1. Neural Network Training:

Load training data consisting of input-output pairs. Create a neural network with a specified architecture (number of hidden layers and nodes). Train the neural network using the training data to capture the relationship between inputs and desired outputs.

2. Neural Network Tuned PID Controller:

Define the parameters of a PID controller (proportional, integral, and derivative gains). Set initial conditions and desired setpoints for the controlled system. Simulate the control loop over a specified time period. Calculate control signals using the trained neural network and PID equations. Combine these signals to adjust the control inputs to the system.

3. Two-Area Load Frequency Control System:

Define the system dynamics for the two-area load frequency control. Include parameters such as masses, damping, and stiffness for each area. Implement the system dynamics in a function that calculates the rate of change of states over time based on control inputs.

4. Simulation and Analysis:

Simulate the controlled system using the defined dynamics and control signals. Analyze the response of the system, such as frequency deviations, over the simulation time. Assess the performance of the Neural Network tuned PID controller by observing how well it maintains the desired setpoints for the frequency of each area.

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