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Summary: In the fast developing electric power system network, ancillary services like automatic generation control (AGC) plays a vital and significant role to ensure good quality of power supply in the system. To distribute good quality of power, a hybrid AGC system along with an efficient and intelligent controller is compelled. So, in this article, a cascaded proportional‐integral (PI)‐proportional‐derivative (PD) controller with filter (PI‐PDF) is proposed as secondary controller for AGC system. A nature inspired optimization algorithm named as moth flame optimization (MFO) algorithm is employed for simultaneous optimization of controller gains. Initially, a two‐area interconnected nonreheat thermal power system is investigated. Analysis revealed that MFO‐tuned PI controller performs better than the different optimization techniques tuned PI controller for the same system and PI‐PDF controller performs better than PI controller does. Then, the study is extended to a three‐area interconnected hybrid system with proper generation rate constraint. Area‐1 consists of solar thermal‐thermal unit; area‐2 consists of thermal‐hydro unit and area‐3 thermal‐gas unit as generating sources. Performance of PI‐PDF controller is compared with classical controllers such as PI, PID, PIDF, and PI‐PD controller without filter. Result analysis divulges that MFO‐tuned PI‐PDF controller performs better than all other controllers considered in this article. Robustness of the PI‐PDF controller is evaluated using parameter variations and random load variation. [ABSTRACT FROM AUTHOR]

Summary: A new algorithm called moth‐flame optimization (MFO) algorithm is proposed to optimize a dual‐mode controller (DMC) for a multiarea hybrid interconnected power system. Initially, a 2‐area nonreheat system is considered. The optimum gains of DMC and proportional‐integral controller are optimized using the MFO algorithm. The superiority of the proposed approach is established while comparing the results with genetic algorithm, bacterial forging optimization algorithm, differential evolution, and hybrid bacterial forging optimization algorithm particle swarm optimization for the same system. The proposed approach is further extended to 2 unequal areas of a 6‐unit hybrid‐sources interconnected power system. The optimum gain of DMC and sliding mode controller (SMC) is optimized with MFO algorithm. The performance of an MFO tuned DMC is compared with particle swarm optimization and genetic algorithm tuned DMC, MFO tuned SMC, and teaching‐learning–based optimization optimized SMC for the same system. Furthermore, robustness analysis is performed by varying the system parameters from their nominal values. It is observed that the optimum gains obtained for nominal condition need not be reset for a wide variation in system parameters. [ABSTRACT FROM AUTHOR]