Abstract
The PID controller still remains a widely used and very effective means of achieving stability in control systems. Generally, the performance of the controller is determined by the proportional, integral and derivative gains of the controller. The classical techniques: Ziegler-Nichols (ZN) open loop method; ZN closed loop method; Chien-Hrones-Reswick (C-H-R) load rejection method; and meta-heuristic technique: the fuzzy logic algorithm, are used to determine the tuning parameters of the PID controller in this study. The performance comparison of these controllers is done for automatic generation control (AGC) of a multi-source single-area hydro-thermal-gas power system. In such power systems, each source has a participation factor that determines its contribution to total power generation. The root mean square error (RMSE) is deployed to determine the proportionate balance of each generator’s output with its corresponding participation factor. The performance comparison of the controllers using Simulink/MATLAB shows that the fuzzy-PID controller achieved the most proportionate generation balance.
Highlights
It has widely been reported that load disturbances in power systems result in corresponding system frequency changes (Fitri et al, 2017; Rahman et al, 2017)
The multi-source single-area power system is subjected to a 1% (0.01pu) load disturbance using each classically and meta-heuristically tuned controllers
The comparison was done for automatic generation control (AGC) of a multi-source single area power system
Summary
It has widely been reported that load disturbances in power systems result in corresponding system frequency changes (Fitri et al, 2017; Rahman et al, 2017). The AGC is the system for adjusting the power output of generators. These generators usually receive kinetic energy from turbines which in turn receive potential energy from various fuel sources which include: hydro, steam (thermal), gas (combustion), wind, etc. Each of these sources acts alone in single-source power generation. When two or more are combined, they create multi-source power generation. In multi-source (hybrid) power generation, each generator has a participation factor that determines its contribution to total power generation. The generators’ outputs change in proportion to its participation factor. It has been established that the summation of participation factors of all participating generators, in each control area, is equal to unity (Barisal and Mishra, 2017)
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