The project proposes a novel Artificial Intelligence technique known as Ant Colony Optimization (ACO) for optimal tuning of PID controllers for load frequency control. The design algorithm is implemented within a hydrothermal power system that comprises two control areas: one for hydroelectric power and the other for thermal power with a reheat stage. To ensure that the system accurately reflects real-world conditions, various nonlinearities such as the Generation Rate Constraint (GRC) and Dead Band are incorporated, alongside a broad spectrum of parameters. Three different cost functions have been suggested for tuning the PID controllers. The system has been tested for various load changes to reveal the effectiveness and robustness of the proposed technique.
Article Details
Unique Paper ID: 164807
Publication Volume & Issue: Volume 10, Issue 12
Page(s): 1637 - 1639
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