Offshore wind farm collector system layout optimization based on self-tracking minimum spanning tree

Abstract

Summary This paper presents a complete model of collector system layout optimization for offshore wind farms, which consists of both internal and external designs. The internal design uses the algorithm of self‐tracking minimum spanning tree (ST‐MST) combined with added functions of automatic cable selecting (ACS) and cable crossing avoiding (CCA) to form a radial topology. In addition, the features above are also integrated with the use of direct current transmission in the external design, which applies a self‐designed searching method to find the optimal locations for VSC converters. The internal and external designs constitute the whole model of the offshore wind farm. More specifically, the internal connection begins with the Fuzzy C‐means clustering method to divide the farm into clusters, where each center of the cluster locates a substation connecting to the turbines belonging to it. The external connection of the wind farm considers the use of high voltage direct current, which saves a great amount of reactive power in long distance transmission. Both the internal and external designs apply ST‐MST with ACS and CCA, while the external connection is also given a higher priority as it uses the cables with larger cross sections compared with internal connection.

Comprehensive layout optimization of the transmission system in a deepwater wind farm cluster

“…However, this research did not prove that the global optimum value could be obtained on this straight line (Yang et al, 2022). Regarding the optimization of the collection line path, it is common to divide the circuits using k-means clustering (Shin and Kim, 2016) or fuzzy C-means clustering (Zuo et al, 2019) and then connect the cables with the shortest path using the minimum spanning tree (MST) method. Cazzaro D et al encoded the positions of the wind turbines and substations and used a heuristic algorithm to optimize the path of the collection lines (Cazzaro et al, 2020).…”

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

“…In [22], a low complexity method based on FCM is proposed to directly acquire a feasible design solution, but the obtained capital cost is relatively high. To further reduce the capital cost, different optimization techniques including DMST [23], binary integer programming [24], DMST and GA [25], the Clark and Wright saving algorithm [26], have been combined with FCM. Similarly, the K-means clustering is often not used alone for optimal design of CST.…”

“…Crossing submarine cables (see Fig. 1) leads to extra expense for building one cable on the top of another, additional reactive power loss, and higher damage risk [23]. Hence, any two different submarine cables should satisfy the no cross constraint, which is employed to each segment of feeder.…”

Heuristic algorithms for the Wind Farm Cable Routing problem

“…In [35] a heuristic based on minimum spanning trees is developed and enhanced with an adaptive particle swarm optimization; this heuristic is then compared, on three offshore wind farm scenarios, to deterministic methods based on minimum spanning trees and on a dynamic variant of it. The work [52] presents an algorithm of self-tracking minimum spanning trees that works with a fuzzy C-means clustering.…”