Application of Opposition-Based Learning Jumping Spider Optimization Algorithm in Gas Turbine Coupled Cooling System

Abstract

A gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear system; however, the randomness and large disturbances make it difficult to control the variables precisely. In order to solve the problem of precise process control for multi-input and multi-output coupled systems with flow, pressure, and temperature, this article conducts the following research: (1) Designing a secondary circuit for waste hot water and establishing a water-circulating gas turbine cooling system to improve the efficiency of waste heat utilization. (2) Identifying the coupled system model and establishing a mathematical model of the coupling relationship based on the characteristic data of input and output signals in the gas turbine cooling system. (3) Designing a coupled-system decoupling compensator to weaken the relationships between variables, realizing the decoupling between coupled variables. (4) An Opposition-based Learning Jumping Spider Optimization Algorithm is proposed to be combined with the PID control algorithm, and the parameters of the PID controller are adjusted to solve the intelligent control problems of heat exchanger water inlet flow rate, pressure, and temperature in the gas turbine cooling system. After simulation verification, the gas turbine cooling system based on an Opposition-based Learning Jumping Spider Optimization Algorithm can realize the constant inlet flow rate, with an error of no more than 1 m3/h, constant inlet water temperature, with an error of no more than 0.2 °C, and constant main-pipe pressure, with an error of no more than 0.01 MPa. Experimental results show that a gas turbine cooling system based on the Opposition-based Learning Jumping Spider Optimization Algorithm can accurately realize the internal variable controls. At the same time, it can provide a reference for decoupling problems in strongly coupled systems, the controller parameter optimization problems, and process control problems in complex systems.

Application of a Multi-Strategy Improved Sparrow Search Algorithm in Bridge Crane PID Control Systems

“…Experimental results indicate that the improved bat control algorithm achieved the best increase in trajectory tracking accuracy, and the proposed control method can be applied to other systems. Wang et al [22] proposed an opposition-based learning jumping spider optimization algorithm to adjust the parameters of PID controllers, aiming to address the intelligent control problems related to the inlet flow rate, pressure, and temperature of heat exchangers in gas turbine cooling systems.…”