Abstract
This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.
Highlights
Load Frequency Control (LFC) is a crucial topic in power system operation and control of supplying sufficient and dependable electric power with better tone
Many technical papers have been distributed with the LFC in power system literature by using analogy models, such as [5] [6]
The advanced power system model is simulated in a separate program considering a 10% step load change in the area
Summary
Load Frequency Control (LFC) is a crucial topic in power system operation and control of supplying sufficient and dependable electric power with better tone. An electric energy system must be maintained at a desired oper-. (2016) Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems. The primary destination of the LFC is to maintain zero steady state errors for frequency deviation, and right tracking load demands in a multi-area power system [1]-[4]. Many technical papers have been distributed with the LFC in power system literature by using analogy models, such as [5] [6]. In [6], a new approach is reported for optimal control of power systems
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