Optimization of Automatic Generation Control and Economic Load Dispatch for Two Area Six Unit Interconnected Power System

Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest route to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated in this work using two approaches: first, the Adaptive Adjustment Approach of the Control Parameters, and second, the Adaptive Variable Weights method. Those two methods are utilized for updating the wolf position, accelerate convergence, and cut down on time. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, dynamic obstacles, and an environment with a dynamic target. The online optimization method is performed using two phases which are the sensors reading phase and the path calculation phase. The proposed approach can solve a local minima problem in the static obstacles. A comparison study result between the proposed method and two other algorithms revealed that the proposed algorithm performed better in avoiding obstacles, which include the situation with the local minima. Finally, when put to comparison with Hybrid Fuzzy-Wind Driven Optimization and Adaptive Particle Swarm Optimization the average improvement rates in route length are 2.86% and 4.70391%, respectively.

show abstract

“…Omega wolves follow the upper layer wolves and reposition themselves. The next ( 12) and ( 13) are suggested in this approach [24]:…”

Section: B Mathematical Model Of the Huntingmentioning

confidence: 99%

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Abed

Lutfy

Al-Doori

2022

IJEECS

View full text Add to dashboard Cite

show abstract

Optimization of Automatic Generation Control and Economic Load Dispatch for Two Area Six Unit Interconnected Power System

Cited by 1 publication

References 36 publications

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Contact Info

Product

Resources

About