DESIGN OPTIMUM PROPORSIONAL-INTEGRAL CONTROL IN INTEGRATED BOILER-HEADER DURING PARTIALLY TRIP WITH GENETIC ALGORITHM, FLOWER POLLINATION ALGORITHM, AND DEEP REINFORCEMENT LEARNING

• Main Author: Hadiwinata S, Antonius
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/69793
• Institution: Institut Teknologi Bandung

Realibility and efficiency from steam power plant is growing as the development in knowledge of control techniques and optimization methods. The Integration between two boilers and one steam header as a steam reservoir and two turbines as a power generator, makes steam power generation more reliable. In this study, a simulation was carried out with the condition of one boiler trip, the pressure on the header would decrease along with the loss of steam rate from the tripping boiler. Boilers that are still running will increase their steam production to compensate for the lost steam rate so that there is no reduction in the power produced by the turbine. The mathematical model of the boiler-turbine system used is a nonlinear model of the boiler-turbine system that was introduced by R.D. Bell and K.J. Astrom in 1987 which is consisting of three inputs and three outputs. The three inputs are the fuel flow rate, the steam rate, and the feed water rate to the drum, while the three outputs are the boiler steam pressure, the power generated by the turbine, and the deviation of the water level in the steam drum. This research was conducted based on model of a steam power plant in Riau using a Proporsional-Integral controller. These PI controller are usually tuned manually by experienced people and require a long observation time. In this study, the PI controller was optimized using the genetic algorithm (GA), flower pollination (FPA) algorithm, and deep reinforcement learning (DRL), specifically the deep deterministic policy gradient (DDPG) algorithm to optimize the PI controller automatically. From the simulation results, it was found that the flower pollination algorithm has a very good test time of 1 minute to perform optimization with the results of steam pressure control and power control being generated by 5% overshoot with a settling time of 6.3 seconds and 1.9 seconds, respectively. while the water level control the best optimization method is DRL with an overshoot of 9.3% and a settling time of 47.6 seconds.