Self Tuning PID Controller with Genetic Algorithm for Main Steam Temperature System

Abstract

The main steam temperature management is a significant approach to improve the thermal power plant's economic efficiency. In addition, the boiler's primary steam temperature is one of the boiler's most critical operational characteristics. The primary steam temperature exhibits nonlinear, time-varying, and long-time delay characteristics when subjected to various perturbations. The standard cascade PID control technique makes it difficult to get a stable control result. To address the control dilemmas around the main steam temperature, numerous philosophers have proposed various early forms of temperature regulation. By conducting simulations or field trials, these control strategies are shown to be effective. These approaches outperform the traditional cascade PID algorithm in terms of control impact. A detailed mathematical model of the controlled item is required; the control algorithms contain dead zones; online optimization is highly involute; calculation time is excessively lengthy; all of these drawbacks must be taken into account when using these control techniques. Main steam temperature control systems in thermal power plants have high inertia and lengthy delay characteristics, which impair system control performance. A Self-Tuned -PID control using Genetic Algorithm (GA) is presented for the primary steam temperature system in order to improve control performance. The standard PID controller's self-regulation optimization problem is solved by using a Genetic Algorithm.. It is guaranteed that the system's parameters are always optimum by the use of Genetic Algorithm. The simulation results are carried out in comparison to various control approaches. Self Tuned PID based on Genetic Algorithm has a faster replication rate, less overshoot and inaccuracy, higher tracking performance, and less lag impact, according to simulation data.