Optimized proportional integral derivative (PID) controller for the exhaust temperature control of a gas turbine system using particle swarm optimization

Abstract

In this paper, the particle swarm optimization (PSO) technique is used in optimising the proportional integral derivative (PID) controller parameters for the exhaust temperature control of a gas turbine system. The performance of the PID controller whose parameters are tuned based on the PSO method (PSO-PID) is compared with the conventional PID (CPID) controller that employs the Ziegler-Nichols method. A new performance criterion, known as multipurpose performance criterion (MPPC) is proposed and used in the PSO algorithm. Time domain performance of the PSO-PID controller, such as the maximum overshoot , rise time , settling time and absolute error (AE) are being optimized based on the MPPC and compared with other performance criteria such as the integral of time multiplied by absolute error (ITAE), integral of time multiplied by square error (ITSE), integral square error (ISE) and integral of absolute error (IAE). Result shows that the PSO technique, combined with the MPPC performance criterion is very effective to yield optimal transient response of the gas turbine exhaust temperature. An adjustable weighting factor in the MPPC technique makes it more reliable, consistent and flexible as compared to the commonly used performance criteria.

Design Techniques Study of PID Controllers

“…At the same year, in [14] authors designed multipurpose performance criterion (MPPC) that used PSO algorithm in PID controller parameters optimization process. In this system PID controller controlled the temperature of exhaust's gas in turbine system.…”

“…Therefore, the operating conditions of the system and the whole loop performance were changed. These changes were resulted by the nonlinearities processes in aging process, control channel, production properties of raw materials, strategy changes, and equipment maintenance cycles changes [14][15][16].…”

Fractional IMC design for fractional order gas turbine model

“…The IMC controller obtained in (12) is converted into the standard feedback controller using (2). Thus from (2), (9), (11) and (12) …”

“…Uncontrolled runaway of the turbine is prevented by controlling the fuel supply line which limits the amount of fuel fed to the turbine thus limiting its speed. The conventional Proportional-Integral-Derivative (PID) controllers along with many other popular techniques like model predictive control, fuzzy logic etc., have been very widely used for such tasks, due to the simplicity of their implementation and design [2]- [13]. However, for designing an efficient controller it is very essential to have an appropriate mathematical model of the plant.…”

Tuning PID Controllers Using Artificial Intelligence Techniques

“…Step 5: The aforementioned steps are repeated until the swarm converges to an optimal or sub-optimal solution [10]. The genetic Algorithm advantages are:  Optimization with continuous or discrete variables.…”