Abstract
This paper presents an application of the recently introduced Antlion Optimizer (ALO) to find the parameters of primary governor loop of thermal generators for successful Automatic Generation Control (AGC) of two-area interconnected power system. Two standard objective functions, Integral Square Error (ISE) and Integral Time Absolute Error (ITAE), have been employed to carry out this parameter estimation process. The problem is transformed in optimization problem to obtain integral gains, speed regulation, and frequency sensitivity coefficient for both areas. The comparison of the regulator performance obtained from ALO is carried out with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) based regulators. Different types of perturbations and load changes are incorporated to establish the efficacy of the obtained design. It is observed that ALO outperforms all three optimization methods for this real problem. The optimization performance of ALO is compared with other algorithms on the basis of standard deviations in the values of parameters and objective functions.
Highlights
With the increase in the interconnection of the utilities, complexity in power system operation and control has emerged as a challenging problem in front of design engineers
Different Automatic Generation Control (AGC) regulator settings are obtained with the application of four algorithms (GA, Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Antlion Optimizer (ALO)) on two standard objective functions (ISE and Integral Time Absolute Error (ITAE))
This paper presents an application of the recently introduced algorithm ALO to find optimal parameters of the AGC regulator
Summary
With the increase in the interconnection of the utilities, complexity in power system operation and control has emerged as a challenging problem in front of design engineers. In the modern optimal control, the determination of the parameters of primary governor loop is performed to enhance the system’s damping performance In recent years, this field has emerged as a potential area of research. Teaching Learning Based Optimization (TLBO) is applied to find the scaling factors and integral gains for two thermal units’ interconnected power systems in [26]. The search of a proper set of parameters (integral and differential gains, primary loop parameters) by which Area Control Error (ACE) can be reduced to zero is a major objective to solve AGC problem. (1) To solve the optimization process by ISE and ITAE objective functions to find out the parameters of primary governor loop, that is, speed regulation constant (R), frequency bias (D), and integral gains (KI).
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