Nonlinear predictive control using particle swarm optimization: Applications to power systems

Abstract

Although classical control is still the workhorse in the majority of control engineering applications, it is well recognized that this linear control method is not always the optimum way to deal with the typical highly nonlinear plants. This is especially true for multivariable systems with a variety of constraints. Due to recent and continual increment in the complexity of systems and tighter product specifications, the quality requirements from automatic control have increased greatly. At the same time, the available computing power has rose to fantastic levels. Consequently, computationally intensive control methods can be applied to complex systems comparatively easily. Model Predictive Control (MPC) techniques were developed to obtain tighter control and they were applied successfully to several industrial applications. MPC requires the solution of a constrained optimization problem at each sampling instant. This optimization is carried out by various techniques like linear programming (LP), quadratic programming (QP), dynamic programming (DP) and heuristics like Genetic Algorithms (GA). In this thesis, a new implementation of MPC is proposed using Particle Swarm Optimization (PSO) algorithm. The proposed method formulates the MPC as an optimization problem and PSO is used to optimize it. This gives numerous advantages like adaptability, possibility of varying control objectives, and enhanced capability of handling constraints. The proposed method is applied to an area of engineering systems that has been relatively unexplored by MPC, i.e. power systems. Both SISO and MIMO nonlinear systems are considered. Three practical Power System problems are considered and the proposed technique is applied to them. Keywords: MPC, Model Predictive Control, Nonlinear Control, Particle Swarm Optimization, Prediction Horizon, Control of Power Systems, Load Frequency Control, Synchronous Machine on Infinite Bus, Fossil Fuel Power Unit, Boiler-Turbine System, Predictive Control of Power Systems.