Optimisation for offshore wind farm cable connection layout using adaptive particle swarm optimisation minimum spanning tree method
- Authors:
-
-
Hou, Peng
;
Close
0000-0002-5837-8431
Department of Energy Technology, The Faculty of Engineering and Science, Aalborg University -
Hu, Weihao
;
Close0000-0003-3816-7263
Department of Energy Technology, The Faculty of Engineering and Science, Aalborg University -
Chen, Zhe
Close0000-0002-2919-4481
Department of Energy Technology, The Faculty of Engineering and Science, Aalborg University
-
Hou, Peng
;
- DOI:
- 10.1049/iet-rpg.2015.0340
- Abstract:
- The wind farm layout optimization problem is similar to the classic mathematical problem of finding the Minimum Spanning Tree (MST) of a weighted undirected graph. Due to the cable currentcarrying capacity limitation, the cable sectional area should be carefully selected to meet the system operational requirement and this constraint should be considered during the MST formulation process. Hence, traditional MST algorithm cannot ensure a minimal cable investment layout. In this paper, a new method to optimize the offshore wind farm cable connection layout is presented. The algorithm is formulated based on concept of MST and further improved by Adaptive Particle Swarm Optimization (APSO) algorithm. Since the location of the Offshore Substation (OS) has a significant impact on both the layout formulation and total cost of cables, the optimized location of OS is expected to be found together with the optimized cable connection layout. The proposed method is compared with the MST and Dynamic MST (DMST) methods and simulation results show the effectiveness of the proposed method.
- Type:
- Journal article
- Language:
- English
- Published in:
- Iet Renewable Power Generation, 2016, Vol 10, Issue 5, p. 694-702
- Keywords:
- Minimum Spanning Tree (MST); Current-carrying capacity; Cost-effective wind farm; Adaptive Particle Swarm Optimization (APSO) algorithm; Offshore Substation (OS)
- Main Research Area:
- Science/technology
- Publication Status:
- Published
- Review type:
- Peer Review
- Submission year:
- 2016
- Scientific Level:
- Scientific
- ID:
- 2303669966
