Performance evaluation of objective functions in automatic generation control of thermal power system using ant colony optimization technique-designed proportional–integral–derivative controller

Abstract

This work presents the performance evaluation of different commonly used objective functions in load frequency control (LFC)/automatic generation control (AGC) of single/multi-area non-reheat thermal power systems. The commonly used objective functions in LFC/AGC of power system are integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The objective functions are used to tune proportional–integral–derivative (PID) controller values in single-area, two-area, three-area and four-area power systems with one percent Step Load Perturbation (1% SLP) in area 1. The gain values of proportional controller gain $$({K}_{\mathrm{p}})$$ , integral controller gain $$({K}_{\mathrm{i}})$$ and derivative controller gain $$({K}_{\mathrm{d}})$$ values are tuned by using artificial intelligence (AI)-based ant colony optimization (ACO) technique with aforementioned different objective functions. The cumulative performance of the investigated power systems with different objective function-based ACO-PID controller response reveals that the objective function performance is varied with the increase in the power system size. The performance of power systems is measured by considering time domain specification analysis, namely the settling time, undershoot and peak overshoot. The results established that the objective functions performance is diverse based on the power system size. In addition, the ITSE-based PID controller response guarantees minimum peak undershoot in all power system’s responses compared to ISE-, ITAE- and IAE-based controller response.