基于改进模糊C-均值聚类的陆上风电场集电线路回路划分与拓扑结构优化

doi:10.12096/j.2096-4528.pgt.23040

发电技术 ›› 2024, Vol. 45 ›› Issue (4): 675-683.DOI: 10.12096/j.2096-4528.pgt.23040

基于改进模糊C-均值聚类的陆上风电场集电线路回路划分与拓扑结构优化

易海¹, 吕宙安¹, 张伶俐¹, 陈希¹, 柳典¹, 黄雨薇², 韩星星², 许昌²

^1.中国三峡新能源(集团)股份有限公司, 北京市西城区 101100
^2.河海大学能源与电气学院, 江苏省南京市 211100

收稿日期:2023-12-04 修回日期:2024-02-20 出版日期:2024-08-31 发布日期:2024-08-27
通讯作者: 许昌
作者简介:易海(1985)，男，高级工程师，主要从事新能源发电项目集电线路与送出线路工程优化设计及技术研究工作，yi_hai@ctg.com.cn；
许昌(1985)，男，博士，教授，主要从事风电场设计和运行优化方面的研究工作，本文通信作者，zhuifengxu@163.com。
基金资助:
国家自然科学基金项目(52106238);三峡新能源科技项目(2021-491);政府间双边创新合作项目(BZ2021019)

Onshore Wind Farm Collector Circuit Division and Topology Optimization Based on Improved Fuzzy C-Means Clustering

Hai YI¹, Zhouan LÜ¹, Lingli ZHANG¹, Xi CHEN¹, Dian LIU¹, Yuwei HUANG², Xingxing HAN², Chang XU²

^1.China Three Gorges Renewables (Group) Co. , Ltd. , Xicheng District, Beijing 101100, China
^2.College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, Jiangsu Province, China

Received:2023-12-04 Revised:2024-02-20 Published:2024-08-31 Online:2024-08-27
Contact: Chang XU
Supported by:
National Natural Science Foundation of China(52106238);China Three Gorges Renewables Technology Project(2021-491);Bilateral Innovation Cooperation Projects Between Governments(BZ2021019)

摘要/Abstract

摘要：

目的在“双碳”目标以及我国能源结构加速转型的双重驱动下，风电产业规模不断快速增长，亟须降本增效以应对平价上网压力。集电线路的造价在投资中占比较大，存在可观的优化空间。为了降低投资成本，提出了一种改进模糊C-均值(fuzzy C-means，FCM)聚类算法。方法利用改进FCM聚类算法对陆上风电场集电线路回路进行划分。该算法综合考虑了方位角与欧式距离，以保障回路间线路不交叉，并使相邻机组聚集到同一回路；引入机位到聚类中心距离的修正因子，通过调节其数值限制回路容量。在回路划分的基础上，利用动态Prim算法对各回路进行集电线路优化选线。最后，通过某陆上风电场算例验证方法的有效性。结果与只考虑方位角的聚类方法相比，考虑方位角和间距的改进FCM算法优化效果更好，单回、双回连接对应的集电线路总造价分别降低了2.6%和5.4%。结论所提算法能够有效降低集电线路的总造价，具有一定的应用价值，可为风电场集电线路设计提供参考。

关键词: 陆上风电场, 集电线路, 拓扑结构优化, 模糊C-均值(FCM)聚类算法, 动态Prim算法

Abstract:

Objectives Driven by the dual carbon goal and the accelerated transformation of China’s energy structure, the scale of the wind power industry continues to grow rapidly, and there is an urgent need to reduce costs and increase efficiency to cope with the pressure of grid parity. The cost of collector lines accounts for a relatively large proportion of investment, and there is considerable space for optimization. In order to reduce the investment cost, an improved fuzzy C-means (FCM) clustering algorithm was proposed. Methods The improved FCM clustering algorithm was used to divide the collector circuit of onshore wind farm. The algorithm comprehensively considered the azimuth angle and the Euclidean distance to ensure that the lines between the circuits were not crossed, and the adjacent units were gathered to the same circuit. The correction factor of the distance from the machine position to the cluster center was introduced, and the circuit capacity was limited by adjusting the distance correction factor. On the basis of circuit division, the dynamic Prim algorithm was used to optimize the line selection of each circuit. Finally, the effectiveness of the method was verified by an example of an onshore wind farm. Results Compared with the clustering method only considering the azimuth angle, the improved FCM algorithm considering azimuth angle and spacing has better optimization effect. The total cost of single-circuit and double-circuit collection lines is reduced by 2.6% and 5.4%, respectively. Conclusions The proposed algorithm can effectively reduce the total cost of collector lines, and has certain application value. It can provide a reference for the design of wind farm collector lines.

Key words: onshore wind farm, collector line, topology optimization, fuzzy C-means (FCM) clustering algorithm, dynamic Prim algorithm

中图分类号:

TK 89

易海, 吕宙安, 张伶俐, 陈希, 柳典, 黄雨薇, 韩星星, 许昌. 基于改进模糊C-均值聚类的陆上风电场集电线路回路划分与拓扑结构优化[J]. 发电技术, 2024, 45(4): 675-683.

Hai YI, Zhouan LÜ, Lingli ZHANG, Xi CHEN, Dian LIU, Yuwei HUANG, Xingxing HAN, Chang XU. Onshore Wind Farm Collector Circuit Division and Topology Optimization Based on Improved Fuzzy C-Means Clustering[J]. Power Generation Technology, 2024, 45(4): 675-683.

图/表 12

图1 欧式距离聚类结果和期望聚类结果对比

Fig. 1 Comparison of clustering results of Euclidean distance and expected clustering results

图2 考虑方位角和间距的改进FCM回路划分算法流程

Fig. 2 Improved FCM circuit division algorithm flow considering azimuth and spacing

图3 连接各机组的最小生成树及机组参考间距

Fig. 3 Minimum spanning tree connecting each turbine and reference spacing between turbines

图4 适用于方向数据的距离计算方法示意图

Fig. 4 Schematic diagram of the distance calculation method suitable for direction data

表1 集电线路造价

Tab. 1 Collector line construction cost

输送容量/MW	≤8	≤16	≤24	≤48
造价/(万元/km)	25	30	35	45

图5 动态Prim算法生成集电线路拓扑结构示意图

Fig. 5 Schematic diagram of topological structure of collector lines generated by dynamic Prim algorithm

图6 某已建成风电场机组与升压站分布图

Fig. 6 A completed wind farm machine and booster station distribution map

图7 人工经验设计的线路拓扑结构

Fig. 7 Line topology designed by artificial experience

图8 采用动态Prim算法优化的线路拓扑结构

Fig. 8 Line topology optimized by dynamic Prim algorithm

图9 考虑方位角的FCM方法优化的线路拓扑结构

Fig.9 Line topology optimized by FCM method considering azimuth angle

图10 综合考虑方位角和间距的FCM方法优化的线路拓扑结构

Fig. 10 Line topology optimized by FCM method considering azimuth angle and distance

表2 不同方法得到的集电线路总造价对比

Tab. 2 Comparison of total cost of collector lines obtained by different methods

方法	单回集电线路总造价/万元	双回集电线路总造价/万元
人工经验设计	—	1 300.00
动态Prim算法	—	1 005.44
考虑方位角的FCM算法+动态Prim算法	1 079.30	1 122.04
综合考虑方位角和间距的FCM算法+动态Prim算法	1 050.86	1 061.09

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基于改进模糊C-均值聚类的陆上风电场集电线路回路划分与拓扑结构优化

Onshore Wind Farm Collector Circuit Division and Topology Optimization Based on Improved Fuzzy C-Means Clustering

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