657 lines
25 KiB
Python
657 lines
25 KiB
Python
import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from scipy.spatial import distance_matrix
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from scipy.sparse.csgraph import minimum_spanning_tree
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from sklearn.cluster import KMeans
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from collections import defaultdict
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import networkx as nx
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import math
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# 设置matplotlib支持中文显示
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial']
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plt.rcParams['axes.unicode_minus'] = False
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# 1. 生成风电场数据（实际应用中替换为真实坐标）
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def generate_wind_farm_data(n_turbines=30, seed=42, layout='random', spacing=800):
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"""
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生成模拟风电场数据
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:param layout: 'random' (随机) 或 'grid' (规则行列)
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:param spacing: 规则布局时的风机间距(m)
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"""
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np.random.seed(seed)
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if layout == 'grid':
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# 计算行列数 (尽量接近方形)
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n_cols = int(np.ceil(np.sqrt(n_turbines)))
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n_rows = int(np.ceil(n_turbines / n_cols))
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x_coords = []
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y_coords = []
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for i in range(n_turbines):
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row = i // n_cols
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col = i % n_cols
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# 添加微小抖动模拟海上定位误差(±1%)
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jitter = spacing * 0.01
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x = col * spacing + np.random.uniform(-jitter, jitter)
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y = row * spacing + np.random.uniform(-jitter, jitter)
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x_coords.append(x)
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y_coords.append(y)
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x_coords = np.array(x_coords)
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y_coords = np.array(y_coords)
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# 升压站位置：通常位于风电场边缘或中心，这里设为离岸侧(y负方向)中心
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substation = np.array([[np.mean(x_coords), -spacing]])
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else:
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# 随机生成风机位置（扩大范围以适应更大容量）
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x_coords = np.random.uniform(0, 2000, n_turbines)
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y_coords = np.random.uniform(0, 2000, n_turbines)
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# 升压站位置
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substation = np.array([[0, 0]])
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# 随机生成风机额定功率（海上风机通常更大，6-10MW）
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power_ratings = np.random.uniform(6.0, 10.0, n_turbines) if layout == 'grid' else np.random.uniform(2.0, 5.0, n_turbines)
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# 创建DataFrame
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turbines = pd.DataFrame({
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'id': range(n_turbines),
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'x': x_coords,
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'y': y_coords,
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'power': power_ratings,
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'cumulative_power': np.zeros(n_turbines) # 用于后续计算
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})
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return turbines, substation
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# 1.5 从Excel加载数据
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def load_data_from_excel(file_path):
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"""
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从Excel文件读取风机和升压站坐标
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Excel格式要求包含列: Type (Turbine/Substation), ID, X, Y, Power
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"""
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try:
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df = pd.read_excel(file_path)
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# 标准化列名（忽略大小写）
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df.columns = [c.capitalize() for c in df.columns]
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required_cols = {'Type', 'X', 'Y', 'Power'}
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if not required_cols.issubset(df.columns):
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raise ValueError(f"Excel文件缺少必要列: {required_cols - set(df.columns)}")
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# 提取升压站数据
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substation_df = df[df['Type'].astype(str).str.lower() == 'substation']
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if len(substation_df) == 0:
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raise ValueError("未在文件中找到升压站(Substation)数据")
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substation = substation_df[['X', 'Y']].values
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# 提取风机数据
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turbines_df = df[df['Type'].astype(str).str.lower() == 'turbine'].copy()
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if len(turbines_df) == 0:
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raise ValueError("未在文件中找到风机(Turbine)数据")
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# 重置索引并整理格式
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turbines = pd.DataFrame({
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'id': range(len(turbines_df)),
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'original_id': (turbines_df['Id'].values if 'Id' in turbines_df.columns else range(len(turbines_df))),
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'x': turbines_df['X'].values,
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'y': turbines_df['Y'].values,
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'power': turbines_df['Power'].values,
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'cumulative_power': np.zeros(len(turbines_df))
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})
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print(f"成功加载: {len(turbines)} 台风机, {len(substation)} 座升压站")
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return turbines, substation
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except Exception as e:
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print(f"读取Excel文件失败: {str(e)}")
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raise
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# 2. 基于最小生成树(MST)的集电线路设计
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def design_with_mst(turbines, substation):
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"""使用最小生成树算法设计集电线路拓扑"""
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# 合并风机和升压站数据
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all_points = np.vstack([substation, turbines[['x', 'y']].values])
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n_points = len(all_points)
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# 计算距离矩阵
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dist_matrix = distance_matrix(all_points, all_points)
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# 计算最小生成树
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mst = minimum_spanning_tree(dist_matrix).toarray()
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# 提取连接关系
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connections = []
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for i in range(n_points):
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for j in range(n_points):
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if mst[i, j] > 0:
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# 确定节点类型：0是升压站，1+是风机
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source = 'substation' if i == 0 else f'turbine_{i-1}'
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target = 'substation' if j == 0 else f'turbine_{j-1}'
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connections.append((source, target, mst[i, j]))
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return connections
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# 3. 基于扇区聚类（改进版K-means）的集电线路设计
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def design_with_kmeans(turbines, substation, n_clusters=3):
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"""
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使用基于角度的K-means聚类设计集电线路拓扑 (避免交叉)
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原理：将风机按相对于升压站的角度划分扇区，确保出线不交叉
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"""
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# 准备风机坐标数据
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turbine_coords = turbines[['x', 'y']].values
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substation_coord = substation[0]
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# 计算每台风机相对于升压站的角度和单位向量
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# 使用 (cos, sin) 也就是单位向量进行聚类，可以完美处理 -180/+180 的周期性问题
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dx = turbine_coords[:, 0] - substation_coord[0]
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dy = turbine_coords[:, 1] - substation_coord[1]
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# 归一化为单位向量 (对应角度特征)
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magnitudes = np.sqrt(dx**2 + dy**2)
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# 处理重合点避免除零
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with np.errstate(divide='ignore', invalid='ignore'):
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unit_x = dx / magnitudes
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unit_y = dy / magnitudes
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unit_x[magnitudes == 0] = 0
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unit_y[magnitudes == 0] = 0
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# 构建特征矩阵：主要基于角度(单位向量)，可根据需要加入少量距离权重
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# 如果纯粹按角度，设为单位向量即可
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features = np.column_stack([unit_x, unit_y])
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# 执行K-means聚类
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kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
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turbines['cluster'] = kmeans.fit_predict(features)
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# 为每个簇找到最佳连接点（离升压站最近的风机）
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cluster_connection_points = {}
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for cluster_id in range(n_clusters):
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cluster_turbines = turbines[turbines['cluster'] == cluster_id]
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if len(cluster_turbines) == 0:
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continue
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distances_to_substation = np.sqrt(
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(cluster_turbines['x'] - substation_coord[0])**2 +
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(cluster_turbines['y'] - substation_coord[1])**2
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)
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closest_idx = distances_to_substation.idxmin()
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cluster_connection_points[cluster_id] = closest_idx
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# 为每个簇内构建MST
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cluster_connections = []
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for cluster_id in range(n_clusters):
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# 获取当前簇的风机
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cluster_turbines = turbines[turbines['cluster'] == cluster_id]
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if len(cluster_turbines) == 0:
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continue
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cluster_indices = cluster_turbines.index.tolist()
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# 计算簇内距离矩阵
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coords = cluster_turbines[['x', 'y']].values
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dist_matrix = distance_matrix(coords, coords)
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# 计算簇内MST
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mst = minimum_spanning_tree(dist_matrix).toarray()
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# 添加簇内连接
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for i in range(len(cluster_indices)):
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for j in range(len(cluster_indices)):
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if mst[i, j] > 0:
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source = f'turbine_{cluster_indices[i]}'
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target = f'turbine_{cluster_indices[j]}'
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cluster_connections.append((source, target, mst[i, j]))
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# 添加簇到升压站的连接
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substation_connections = []
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for cluster_id, turbine_idx in cluster_connection_points.items():
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turbine_coord = turbines.loc[turbine_idx, ['x', 'y']].values
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distance = np.sqrt(
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(turbine_coord[0] - substation_coord[0])**2 +
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(turbine_coord[1] - substation_coord[1])**2
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)
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substation_connections.append((f'turbine_{turbine_idx}', 'substation', distance))
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return cluster_connections + substation_connections, turbines
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# 常量定义
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VOLTAGE_LEVEL = 66000 # 66kV
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POWER_FACTOR = 0.95
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# 4. 电缆选型函数（简化版）
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def select_cable(power, length, is_offshore=False):
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"""
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基于功率和长度选择合适的电缆截面
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:param is_offshore: 是否为海上环境（成本更高）
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"""
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# 成本乘数：海缆材料+敷设成本通常是陆缆的4-6倍
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cost_multiplier = 5.0 if is_offshore else 1.0
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# 电缆规格库: (截面mm², 载流量A, 电阻Ω/km, 基准价格元/m)
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cable_specs = [
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(35, 150, 0.524, 80),
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(70, 215, 0.268, 120),
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(95, 260, 0.193, 150),
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(120, 295, 0.153, 180),
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(150, 330, 0.124, 220),
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(185, 370, 0.0991, 270),
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(240, 425, 0.0754, 350),
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(300, 500, 0.0601, 450), # 增加大截面适应海上大功率
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(400, 580, 0.0470, 600)
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]
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# 估算电流
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# power是MW, 换算成W需要 * 1e6
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current = (power * 1e6) / (np.sqrt(3) * VOLTAGE_LEVEL * POWER_FACTOR)
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# 选择满足载流量的最小电缆
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selected_spec = None
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for spec in cable_specs:
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if current <= spec[1] * 0.8: # 80%负载率
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selected_spec = spec
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break
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if selected_spec is None:
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selected_spec = cable_specs[-1]
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resistance = selected_spec[2] * length / 1000 # 电阻(Ω)
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cost = selected_spec[3] * length * cost_multiplier # 电缆成本(含敷设)
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return {
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'cross_section': selected_spec[0],
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'current_capacity': selected_spec[1],
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'resistance': resistance,
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'cost': cost,
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'current': current
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}
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def get_max_cable_capacity_mw():
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"""计算最大电缆(400mm2)能承载的最大功率(MW)"""
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# 400mm2载流量580A
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max_current = 580 * 0.8 # 80%降额
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max_power_w = np.sqrt(3) * VOLTAGE_LEVEL * max_current * POWER_FACTOR
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return max_power_w / 1e6 # MW
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# 5. 计算集电线路方案成本
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def evaluate_design(turbines, connections, substation, is_offshore=False):
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"""评估设计方案的总成本和损耗"""
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total_cost = 0
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total_loss = 0
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# 创建连接图
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graph = nx.Graph()
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graph.add_node('substation')
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for i, row in turbines.iterrows():
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graph.add_node(f'turbine_{i}')
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# 添加边
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for source, target, length in connections:
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graph.add_edge(source, target, weight=length)
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# 计算每台风机的累积功率（从叶到根）
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power_flow = defaultdict(float)
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# 按照后序遍历（从叶到根）
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nodes = list(nx.dfs_postorder_nodes(graph, 'substation'))
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for node in nodes:
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if node == 'substation':
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continue
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# 1. 如果节点是风机，先加上自身的功率
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if node.startswith('turbine_'):
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turbine_id = int(node.split('_')[1])
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power_flow[node] += turbines.loc[turbine_id, 'power']
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# 2. 找到上游节点（父节点）并将累积功率传递上去
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try:
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# 找到通往升压站的最短路径上的下一个节点
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path = nx.shortest_path(graph, source=node, target='substation')
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if len(path) > 1:
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parent = path[1] # path[0]是node自己，path[1]是父节点
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power_flow[parent] += power_flow[node]
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except nx.NetworkXNoPath:
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pass
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# DEBUG: 打印最大功率流
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max_power = max(power_flow.values()) if power_flow else 0
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print(f"DEBUG: 最大线路功率 = {max_power:.2f} MW")
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# 计算成本和损耗
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detailed_connections = []
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for source, target, length in connections:
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# 确定该段线路承载的总功率
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if source.startswith('turbine_') and target.startswith('turbine_'):
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# 风机间连接，取下游节点功率
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if nx.shortest_path_length(graph, 'substation', source) < nx.shortest_path_length(graph, 'substation', target):
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power = power_flow[target]
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else:
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power = power_flow[source]
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elif source == 'substation':
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# 从升压站出发的连接
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power = power_flow[target]
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elif target == 'substation':
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# 连接到升压站
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power = power_flow[source]
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else:
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power = 0
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# 电缆选型
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cable = select_cable(power, length, is_offshore=is_offshore)
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# 记录详细信息
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detailed_connections.append({
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'source': source,
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'target': target,
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'length': length,
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'power': power,
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'cable': cable
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})
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# 累计成本
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total_cost += cable['cost']
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# 计算I²R损耗 (简化版)
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loss = (cable['current'] ** 2) * cable['resistance'] * 3 # 三相
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total_loss += loss
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return {
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'total_cost': total_cost,
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'total_loss': total_loss,
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'num_connections': len(connections),
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'details': detailed_connections
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}
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# 6.5 导出CAD图纸 (DXF格式)
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def export_to_dxf(turbines, substation, connections_details, filename):
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"""
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将设计方案导出为DXF格式 (CAD通用格式)
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:param connections_details: evaluate_design返回的'details'列表
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"""
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import ezdxf
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from ezdxf.enums import TextEntityAlignment
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doc = ezdxf.new(dxfversion='R2010')
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msp = doc.modelspace()
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# 1. 建立图层
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doc.layers.add('Substation', color=1) # Red
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doc.layers.add('Turbines', color=5) # Blue
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doc.layers.add('Cable_35mm', color=3) # Green
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doc.layers.add('Cable_70mm', color=130)
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doc.layers.add('Cable_95mm', color=150)
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doc.layers.add('Cable_120mm', color=4) # Cyan
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doc.layers.add('Cable_150mm', color=6) # Magenta
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doc.layers.add('Cable_185mm', color=30) # Orange
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doc.layers.add('Cable_240mm', color=1) # Red
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doc.layers.add('Cable_300mm', color=250)
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doc.layers.add('Cable_400mm', color=7) # White/Black
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# 2. 绘制升压站
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sx, sy = substation[0, 0], substation[0, 1]
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# 绘制一个矩形代表升压站
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size = 50
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msp.add_lwpolyline([(sx-size, sy-size), (sx+size, sy-size),
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(sx+size, sy+size), (sx-size, sy+size), (sx-size, sy-size)],
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close=True, dxfattribs={'layer': 'Substation'})
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msp.add_text("Substation", dxfattribs={'layer': 'Substation', 'height': 20}).set_placement((sx, sy+size+10), align=TextEntityAlignment.CENTER)
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# 3. 绘制风机
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for i, row in turbines.iterrows():
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tx, ty = row['x'], row['y']
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# 绘制圆形代表风机
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msp.add_circle((tx, ty), radius=15, dxfattribs={'layer': 'Turbines'})
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# 添加文字标签
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label_id = str(int(row['original_id']) if 'original_id' in row else int(row['id']))
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msp.add_text(label_id, dxfattribs={'layer': 'Turbines', 'height': 15}).set_placement((tx, ty-30), align=TextEntityAlignment.CENTER)
|
||
|
||
# 4. 绘制连接电缆
|
||
for conn in connections_details:
|
||
source, target = conn['source'], conn['target']
|
||
section = conn['cable']['cross_section']
|
||
|
||
# 获取坐标
|
||
if source == 'substation':
|
||
p1 = (substation[0, 0], substation[0, 1])
|
||
else:
|
||
tid = int(source.split('_')[1])
|
||
p1 = (turbines.loc[tid, 'x'], turbines.loc[tid, 'y'])
|
||
|
||
if target == 'substation':
|
||
p2 = (substation[0, 0], substation[0, 1])
|
||
else:
|
||
tid = int(target.split('_')[1])
|
||
p2 = (turbines.loc[tid, 'x'], turbines.loc[tid, 'y'])
|
||
|
||
# 确定图层
|
||
layer_name = f'Cable_{section}mm'
|
||
if layer_name not in doc.layers:
|
||
doc.layers.add(layer_name)
|
||
|
||
# 绘制直线
|
||
msp.add_line(p1, p2, dxfattribs={'layer': layer_name})
|
||
|
||
# 添加电缆型号文字（可选，在线的中点）
|
||
# mid_x = (p1[0] + p2[0]) / 2
|
||
# mid_y = (p1[1] + p2[1]) / 2
|
||
# msp.add_text(f"{section}mm", dxfattribs={'layer': layer_name, 'height': 10}).set_pos((mid_x, mid_y), align='CENTER')
|
||
|
||
try:
|
||
doc.saveas(filename)
|
||
print(f"成功导出DXF文件: {filename}")
|
||
except Exception as e:
|
||
print(f"导出DXF失败: {e}")
|
||
|
||
# 6. 可视化函数
|
||
def visualize_design(turbines, substation, connections, title, ax=None, show_costs=True):
|
||
"""可视化集电线路设计方案"""
|
||
if ax is None:
|
||
fig, ax = plt.subplots(figsize=(10, 8))
|
||
|
||
# 绘制风机
|
||
ax.scatter(turbines['x'], turbines['y'], c='white', edgecolors='#333333', s=80, linewidth=1.0, zorder=3, label='Turbines')
|
||
|
||
# 标记风机ID
|
||
for i, row in turbines.iterrows():
|
||
# 显示原始ID（如果存在）
|
||
label_id = int(row['original_id']) if 'original_id' in row else int(row['id'])
|
||
# 仅在风机数量较少时显示ID，避免密集恐惧
|
||
if len(turbines) < 50:
|
||
ax.annotate(str(label_id), (row['x'], row['y']), ha='center', va='center', fontsize=6, zorder=4)
|
||
|
||
# 绘制升压站
|
||
ax.scatter(substation[0, 0], substation[0, 1], c='red', s=250, marker='s', zorder=5, label='Substation')
|
||
|
||
# 颜色映射：使用鲜艳且区分度高的颜色
|
||
color_map = {
|
||
35: '#00FF00', # 亮绿 (Lime) - 最小
|
||
70: '#008000', # 深绿 (Green)
|
||
95: '#00FFFF', # 青色 (Cyan)
|
||
120: '#0000FF', # 纯蓝 (Blue)
|
||
150: '#800080', # 紫色 (Purple)
|
||
185: '#FF00FF', # 洋红 (Magenta)
|
||
240: '#FFA500', # 橙色 (Orange)
|
||
300: '#FF4500', # 红橙 (OrangeRed)
|
||
400: '#FF0000' # 纯红 (Red) - 最大
|
||
}
|
||
|
||
# 统计电缆使用情况
|
||
cable_counts = defaultdict(int)
|
||
|
||
# 绘制连接
|
||
# 判断传入的是简单连接列表还是详细连接列表
|
||
is_detailed = len(connections) > 0 and isinstance(connections[0], dict)
|
||
|
||
# 收集图例句柄
|
||
legend_handles = {}
|
||
|
||
for conn in connections:
|
||
if is_detailed:
|
||
source, target, length = conn['source'], conn['target'], conn['length']
|
||
section = conn['cable']['cross_section']
|
||
|
||
# 统计
|
||
cable_counts[section] += 1
|
||
|
||
# 获取颜色和线宽
|
||
color = color_map.get(section, 'black')
|
||
# 线宽范围 1.0 ~ 3.5
|
||
linewidth = 1.0 + (section / 400.0) * 2.5
|
||
label_text = f'{section}mm² Cable'
|
||
else:
|
||
source, target, length = conn
|
||
section = 0
|
||
color = 'black'
|
||
linewidth = 1.0
|
||
label_text = 'Connection'
|
||
|
||
# 获取坐标
|
||
if source == 'substation':
|
||
x1, y1 = substation[0, 0], substation[0, 1]
|
||
else:
|
||
turbine_id = int(source.split('_')[1])
|
||
x1, y1 = turbines.loc[turbine_id, 'x'], turbines.loc[turbine_id, 'y']
|
||
|
||
if target == 'substation':
|
||
x2, y2 = substation[0, 0], substation[0, 1]
|
||
else:
|
||
turbine_id = int(target.split('_')[1])
|
||
x2, y2 = turbines.loc[turbine_id, 'x'], turbines.loc[turbine_id, 'y']
|
||
|
||
# 绘制线路
|
||
line, = ax.plot([x1, x2], [y1, y2], color=color, linewidth=linewidth, alpha=0.9, zorder=2)
|
||
|
||
# 保存图例（去重）
|
||
if is_detailed and section not in legend_handles:
|
||
legend_handles[section] = line
|
||
|
||
# 打印统计信息
|
||
if is_detailed:
|
||
print(f"[{title.splitlines()[0]}] 电缆统计:")
|
||
for section in sorted(cable_counts.keys()):
|
||
print(f" {section}mm²: {cable_counts[section]} 条")
|
||
|
||
# 设置图形属性
|
||
ax.set_title(title, fontsize=10)
|
||
ax.set_xlabel('X (m)')
|
||
ax.set_ylabel('Y (m)')
|
||
|
||
# 构建图例
|
||
sorted_sections = sorted(legend_handles.keys())
|
||
handles = [legend_handles[s] for s in sorted_sections]
|
||
labels = [f'{s}mm²' for s in sorted_sections]
|
||
|
||
# 添加风机和升压站图例
|
||
handles.insert(0, ax.collections[0]) # Turbines
|
||
labels.insert(0, 'Turbines')
|
||
handles.insert(1, ax.collections[1]) # Substation
|
||
labels.insert(1, 'Substation')
|
||
|
||
ax.legend(handles, labels, loc='upper right', fontsize=8, framealpha=0.9)
|
||
ax.grid(True, linestyle='--', alpha=0.3)
|
||
ax.set_aspect('equal')
|
||
|
||
return ax
|
||
|
||
# 7. 主函数：比较两种设计方法
|
||
def compare_design_methods(excel_path=None):
|
||
"""
|
||
比较MST和K-means两种设计方法 (海上风电场场景)
|
||
:param excel_path: Excel文件路径，如果提供则从文件读取数据
|
||
"""
|
||
if excel_path:
|
||
print(f"正在从 {excel_path} 读取坐标数据...")
|
||
try:
|
||
turbines, substation = load_data_from_excel(excel_path)
|
||
scenario_title = "Offshore Wind Farm (Imported Data)"
|
||
except Exception:
|
||
print("回退到自动生成数据模式...")
|
||
return compare_design_methods(excel_path=None)
|
||
else:
|
||
print("正在生成海上风电场数据 (规则阵列布局)...")
|
||
# 使用规则布局，间距800m
|
||
turbines, substation = generate_wind_farm_data(n_turbines=30, layout='grid', spacing=800)
|
||
scenario_title = "Offshore Wind Farm (Grid Layout)"
|
||
|
||
is_offshore = True
|
||
|
||
# 方法1：最小生成树
|
||
# 注意：MST方法在不考虑容量约束时，可能会导致根部线路严重过载
|
||
mst_connections = design_with_mst(turbines, substation)
|
||
mst_evaluation = evaluate_design(turbines, mst_connections, substation, is_offshore=is_offshore)
|
||
|
||
# 方法2：K-means聚类 (容量受限聚类)
|
||
# 计算总功率和所需的最小回路数
|
||
total_power = turbines['power'].sum()
|
||
max_cable_mw = get_max_cable_capacity_mw()
|
||
min_clusters_needed = int(np.ceil(total_power / max_cable_mw))
|
||
|
||
# 增加一定的安全裕度 (1.2倍) 并确保至少有一定数量的簇
|
||
n_clusters = max(int(min_clusters_needed * 1.2), 4)
|
||
if len(turbines) < n_clusters: # 避免簇数多于风机数
|
||
n_clusters = len(turbines)
|
||
|
||
print(f"系统设计参数: 总功率 {total_power:.1f} MW, 单回路最大容量 {max_cable_mw:.1f} MW")
|
||
print(f"计算建议回路数(簇数): {n_clusters} (最小需求 {min_clusters_needed})")
|
||
|
||
kmeans_connections, clustered_turbines = design_with_kmeans(turbines.copy(), substation, n_clusters=n_clusters)
|
||
kmeans_evaluation = evaluate_design(turbines, kmeans_connections, substation, is_offshore=is_offshore)
|
||
|
||
# 创建结果比较
|
||
results = {
|
||
'MST Method': mst_evaluation,
|
||
'K-means Method': kmeans_evaluation
|
||
}
|
||
|
||
# 可视化
|
||
fig, axes = plt.subplots(1, 2, figsize=(20, 10))
|
||
|
||
# 可视化MST方法
|
||
visualize_design(turbines, substation, mst_evaluation['details'],
|
||
f"MST Design - {scenario_title}\nTotal Cost: ¥{mst_evaluation['total_cost']/10000:.2f}万\nTotal Loss: {mst_evaluation['total_loss']:.2f} kW",
|
||
ax=axes[0])
|
||
|
||
# 可视化K-means方法
|
||
visualize_design(clustered_turbines, substation, kmeans_evaluation['details'],
|
||
f"Sector Clustering (Angular) ({n_clusters} clusters) - {scenario_title}\nTotal Cost: ¥{kmeans_evaluation['total_cost']/10000:.2f}万\nTotal Loss: {kmeans_evaluation['total_loss']:.2f} kW",
|
||
ax=axes[1])
|
||
|
||
plt.tight_layout()
|
||
output_filename = 'wind_farm_design_imported.png' if excel_path else 'offshore_wind_farm_design.png'
|
||
plt.savefig(output_filename, dpi=300)
|
||
|
||
# 导出DXF
|
||
dxf_filename = 'wind_farm_design.dxf'
|
||
# 默认导出更优的方案（通常K-means扇区聚类在海上更合理，或者成本更低者）
|
||
# 这里我们导出Sector Clustering的结果
|
||
export_to_dxf(clustered_turbines, substation, kmeans_evaluation['details'], dxf_filename)
|
||
|
||
plt.show()
|
||
|
||
# 打印详细结果
|
||
print(f"\n===== 海上风电场设计方案比较 ({'导入数据' if excel_path else '自动生成'}) =====")
|
||
for method, eval_data in results.items():
|
||
print(f"\n{method}:")
|
||
print(f" 总成本: ¥{eval_data['total_cost']:,.2f} ({eval_data['total_cost']/10000:.2f}万元)")
|
||
print(f" 预估总损耗: {eval_data['total_loss']:.2f} kW")
|
||
print(f" 连接数量: {eval_data['num_connections']}")
|
||
|
||
return results
|
||
|
||
# 8. 执行比较
|
||
if __name__ == "__main__":
|
||
import os
|
||
# 检查是否存在 coordinates.xlsx，存在则优先使用
|
||
default_excel = 'coordinates.xlsx'
|
||
if os.path.exists(default_excel):
|
||
results = compare_design_methods(excel_path=default_excel)
|
||
else:
|
||
results = compare_design_methods() |