Flower Pollination Algorithm Optimized PI-PD Cascade Controller in Automatic Generation Control of a Multi-area Power System

• FPA algorithm is used for the first time in AGC of 4 area thermal system for optimization.
• Performances of several controllers are evaluated with SLP and random load pattern.
• PI-PD cascade controller is applied for the first time in AGC.
• Comparison of responses of some controllers revels that PI-PD cascade is best.
• Sensitivity analysis reveals the robustness of the optimum gains.

This article presents automatic generation control (AGC) of an interconnected four area thermal system. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. A maiden attempt has been made to apply a Proportional integral-Proportional derivative (PI-PD) cascade controller in AGC. Controller gains are optimized simultaneously using Flower Pollination Algorithm (FPA), a recent evolutionary computational technique. Performance of classical controllers such as Integral (I), Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PI-PD cascade controller. Investigations reveal that in this comparison PI-PD cascade controller provides much better response than others. The performances comparison of several objective functions are evaluated and explored that integral squared error is better than others for the system with the PI-PD cascade controller. Sensitivity analysis reveals that the FPA optimized PI-PD cascade controller parameters obtained at nominal condition of loading, size, position of disturbance and system parameter such as inertia constant, H are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters. The system dynamic performances are studied with 1% step load perturbation, random load in Area 1.