Large Wind Farm Layout Optimization Using Nature Inspired Meta-heuristic Algorithms

Abstract

Wind farm layout optimization (WFLOP) is a complex constrained optimization problem. In this paper, an application of biogeography based optimization (BBO) algorithm for large WFLOP is presented and its performance is compared against three other major meta-heuristic algorithms namely genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). The performance is evaluated on the basis of a modified cost of energy fitness function across 25 different wind farm scenarios. Further statistical inference has been drawn on the basis of 99% Confidence Intervals and Student’s t-test which shows that the fitness function values obtained using BBO are better when compared to the other algorithms for the same number of iterations. The execution time in case of PSO is largest, while GA takes the lowest amount of time.

Keywords:

• BBO
• evolutionary computation
• GA
• Large wind farm
• Layout optimization
• PSO
• Statistical inference

Additional information

Notes on contributors

Sanjeev K. Aggarwal

S K Aggarwal completed his MTech and PhD from NIT, Kurukshetra, Haryana, India in 2004 and 2010, respectively. He has more than 20 years of teaching, research, and industrial experience. At present, he is a faculty member in the EIED of TIET, Patiala, India. His main areas of interest are power system operation and control, and artificial intelligence applications in power systems.

Lalit M. Saini

L M Saini completed his BTech from Panjab University, Chandigarh, India and M.Tech and PhD from Kurukshetra University, Haryana, India. He has more than 25 years of teaching experience. At present he is working as professor and head in EE Department, National Institute of Technology, Kurukshetra, India. His main areas of interest are AI applications in power systems, forecasting and optimization. Email: [email protected]

Vijay Sood

V Sood received the PhD degree from the University of Bradford, Bradford, UK. He is currently professor at Ontario Tech University (formerly UOIT), Oshawa, Ontario. He has extensive experience in HVDC and FACTS systems and their controllers. He has authored two text books in this area. His research focuses on the monitoring, control, and protection of power systems and on the integration of renewable energy systems into the smart grid. He is registered professional engineer in the province of Ontario and Life Fellow of the IEEE, Engineering Institute of Canada, and Emeritus Fellow of the Canadian Academy of Engineering. Email: [email protected]