Evolutionary Optimization of Power Plant Control System Using Immunity-Inspired Algorithms

This paper presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques mimicking the biological immune system. The optimization algorithms are implemented in Matlab®, while the power plant is modeled in Dynsim®. The computational interface between these main components is described and implemented. The evolutionary optimization relies on several algorithms inspired by mechanisms of the immune system of superior organism such as cloning, affinity-based selection, seeding, and vaccination. These algorithms are expected to enhance the computational effectiveness, improve convergence, be more efficient in handling multiple local extrema, and achieve adequate balance between exploration and exploitation. The optimization environment can handle two categories of problems: optimization of constant control system parameters and optimization of variable setpoints. The functionality of the proposed optimization methodology is illustrated for the regulatory control of an acid gas removal unit as part of an integrated gasification combined cycle power plant.