The thermal power system (TPS) application is used for the present research work to control the processing unit to offer better thermal characteristics. The purpose of this paper is to regulate the error dynamics during continuous peak load prediction. In the past, several research studies have discussed this topic based on automatic generation Control (AGC) with different control approaches, such as optimal intelligent model, gain controller, fuzzy controller and fractional order controller. However, the error dynamics are still not in an optimal status due to the sudden rise in load. So, the present study has aimed to design a novel hybrid controller for regulating the error dynamics during high-load prediction.
A novel scheme called Sequence Hyena Optimal Gain Controller (SHOGC) is used as the control mechanism for the AGC-TPS. Here, continuous peak load prediction is the key feature for attaining reduced error statistics. In addition, the incorporation of the hyena’s optimal features has helped to earn the finest optimal error dynamics in different regions. At the first two regions, TPS was designed, then for the evaluation processing, the present novel solution was evaluated for four balanced regions, and the gained error values were compared with those of other traditional models.
The evaluation process in the novel solution is determined in terms of settling velocity for IE, ITE, IAE and ITAE. Area 4 has the least settling time of 4.2 s in IE, 23 s in ITE, 4.2 s in IAE and 5.8 s in ITAE.
Hence, the presented model has reduced the settling time by 5% compared to other models. However, energy efficiency is not studied in this present study; in usual cases, TPS requires high energy constraints.
