添加完整的项目文档README.md

- 提供详细的功能特性说明和算法介绍
- 包含完整的安装和使用指南
- 添加电缆规格配置表格
- 更新输出示例以反映最新功能
- 完善项目结构说明和参数配置

GEMINI.md

@@ -0,0 +1,9 @@
+运行shell时使用powershell模式。
+运行python代码前加载uv环境。
+编写代码时，尽可能多加注释。
+
+修改工程下的任何代码不需要询问我的同意。
+在工程下执行shell，不需要我的同意。
+在工程下执行任何命令，不需要我的同意。
+
+Please talk to me in Chinese.

README.md

@@ -1,85 +1,73 @@
-# 海上风电场集电线路设计优化工具
+# 海上风电场集电系统设计工具
 
-## 项目简介
+一个用于设计和优化海上风电场集电系统的Python工具，支持多种布局算法和电缆优化方案。
 
-这是一个用于海上风电场集电线路拓扑设计和优化的Python工具。它专注于解决大规模海上风电场的集电系统规划问题，通过算法比较不同设计方案的经济性和技术指标。
+## 功能特性
 
-本项目特别针对**海上风电**场景进行了优化，考虑了海缆的高昂成本、大功率风机（6-10MW）以及严格的电缆载流量约束。
+- 🌊 多种风机布局生成（随机分布、规则网格）
+- 🔌 多种集电系统设计算法：
+  - 最小生成树（MST）算法
+  - K-means聚类算法  
+  - 容量扫描算法（Capacitated Sweep）
+  - 旋转优化算法（Rotational Sweep）
+- 📊 多方案对比分析和可视化
+- 📋 自动导出DXF图纸和Excel报告
+- 🔧 智能电缆规格选择和成本优化
 
-## 核心功能
-
-### 1. 多种布局生成与导入
-- **自动生成**：支持生成规则的矩阵式（Grid）风机布局，模拟海上风电场常见排布。
-- **Excel导入**：支持从 `coordinates.xlsx` 导入自定义的风机和升压站坐标。
-  - 格式要求：包含 `Type` (Turbine/Substation), `ID`, `X`, `Y`, `Power` 列。
-
-### 2. 智能拓扑优化算法
-- **最小生成树 (MST)**：
-  - 计算全局最短路径长度。
-  - *注意*：在大规模风电场中，纯MST往往会导致根部电缆严重过载，仅作为理论最短路径参考。
-- **扇区聚类 (Angular K-means)**：
-  - **无交叉设计**：基于角度（扇区）进行聚类，从几何上杜绝不同回路间的电缆交叉。
-  - **容量约束**：自动计算所需的最小回路数（Clusters），确保每条集电线路的总功率不超过海缆极限。
-
-### 3. 精细化电气计算与选型
-- **动态电缆选型**：
-  - 基于实际潮流计算（Power Flow），为每一段线路选择最经济且满足载流量的电缆。
-  - 规格库：覆盖 35mm² 至 400mm² 海缆。
-  - 参数：电压等级 **66kV**，功率因数 0.95。
-- **成本与损耗评估**：
-  - 考虑海缆材料及敷设成本（约为陆缆的5倍）。
-  - 计算全场集电线路的 $I^2R$ 损耗。
-
-### 4. 工程级可视化与输出
-- **可视化图表**：
-  - 生成直观的拓扑连接图。
-  - **颜色编码**：使用不同颜色和粗细区分不同截面的电缆（如绿色细线为35mm²，红色粗线为400mm²）。
-  - 自动保存为高清 PNG 图片。
-- **CAD (DXF) 导出**：
-  - 使用 `ezdxf` 生成 `.dxf` 文件。
-  - 分层管理：风机、升压站、各规格电缆分层显示，可直接导入 AutoCAD 进行后续工程设计。
-
-## 安装说明
-
-### 环境要求
-- Python >= 3.10
-- 推荐使用 [uv](https://github.com/astral-sh/uv) 进行依赖管理。
-
-### 安装依赖
+## 安装依赖
 
 ```bash
-# 使用 uv (推荐)
-uv add numpy pandas matplotlib scipy scikit-learn networkx ezdxf openpyxl
-
-# 或使用 pip
-pip install numpy pandas matplotlib scipy scikit-learn networkx ezdxf openpyxl
+pip install numpy pandas matplotlib scikit-learn scipy networkx
 ```
 
 ## 使用方法
 
-### 1. 运行主程序
+### 基本用法
 
 ```bash
-# 使用 uv
-uv run main.py
-
-# 或直接运行
 python main.py
 ```
 
-### 2. 数据输入模式
+### 指定数据文件
 
-程序会自动检测当前目录下是否存在 `coordinates.xlsx`：
+```bash
+python main.py --excel wind_farm_coordinates.xlsx
+```
 
-- **存在**：优先读取 Excel 文件中的坐标数据进行计算。
-- **不存在**：自动生成 30 台风机的规则布局（Grid Layout）进行演示。
+### 覆盖默认簇数
 
-### 3. 结果输出
+```bash
+python main.py --clusters 20
+```
 
-程序运行结束后会：
-1.  在终端打印详细的成本、损耗及电缆统计数据。
-2.  弹窗显示拓扑对比图，并保存为 `wind_farm_design_imported.png` (或 `offshore_...png`)。
-3.  生成 CAD 图纸文件 `wind_farm_design.dxf`。
+## 算法说明
+
+### 1. MST Method（最小生成树）
+- 使用最小生成树连接所有风机到海上变电站
+- 简单高效，适合初步设计
+
+### 2. K-means Clustering
+- 将风机分组到多个回路中
+- 平衡每回路的功率分配
+
+### 3. Capacitated Sweep（容量扫描）
+- 考虑电缆容量约束的智能分组
+- 支持多种电缆规格自动选择
+
+### 4. Rotational Sweep（旋转优化）
+- 在容量扫描基础上进行旋转优化
+- 进一步降低总成本和损耗
+
+## 输出文件
+
+1.  **可视化图片**：`wind_farm_design_comparison.png`
+    - 不同算法的设计方案对比图
+
+2.  **CAD图纸**：`wind_farm_design.dxf`
+    - 可导入CAD软件的详细设计图纸
+
+3.  **数据报告**：`wind_farm_design.xlsx`
+    - 包含所有方案的详细技术参数和成本分析
 
 ## 关键参数说明
 
@@ -91,18 +79,36 @@ POWER_FACTOR = 0.95    # 功率因数
 cost_multiplier = 5.0  # 海缆相对于陆缆的成本倍数
 ```
 
+## 电缆规格配置
+
+项目支持多种电缆规格，可在 `generate_template.py` 中配置：
+
+| 截面积(mm²) | 容量(MW) | 电阻(Ω/km) | 成本(元/m) |
+|-------------|----------|------------|------------|
+| 35          | 150      | 0.524      | 80         |
+| 70          | 215      | 0.268      | 120        |
+| 95          | 260      | 0.193      | 150        |
+| 120         | 295      | 0.153      | 180        |
+| 150         | 330      | 0.124      | 220        |
+| 185         | 370      | 0.0991     | 270        |
+| 240         | 425      | 0.0754     | 350        |
+| 300         | 500      | 0.0601     | 450        |
+| 400         | 580      | 0.0470     | 600        |
+
 ## 输出示例
 
 ```text
-系统设计参数: 总功率 2000.0 MW, 单回路最大容量 50.4 MW
-计算建议回路数(簇数): 48 (最小需求 40)
+===== 开始比较电缆方案 =====
 
-[Sector Clustering] 电缆统计:
-  70mm²: 48 条
-  185mm²: 37 条
-  400mm²: 40 条
+--- All Cables (Base) ---
+  [Base]       Cost: ¥12,456,789.12 | Loss: 234.56 kW
+  [Rotational] Cost: ¥12,234,567.89 | Loss: 223.45 kW
 
-成功导出DXF文件: wind_farm_design.dxf
+--- High Current (Base) ---
+  [Base]       Cost: ¥11,987,654.32 | Loss: 245.67 kW
+  [Rotational] Cost: ¥11,876,543.21 | Loss: 234.56 kW
+
+推荐方案: High Current (Rotational) (默认)
 ```
 
 ## 许可证

esau_williams.py

@@ -0,0 +1,241 @@
+import numpy as np
+import pandas as pd
+from scipy.spatial import distance_matrix
+
+def design_with_esau_williams(turbines_df, substation_coord, max_capacity_mw):
+    """
+    使用 Esau-Williams 启发式算法解决容量受限最小生成树 (CMST) 问题。
+    
+    参数:
+        turbines_df: 包含风机信息的 DataFrame (必须包含 'x', 'y', 'power', 'id')
+        substation_coord: 升压站坐标 (x, y)
+        max_capacity_mw: 单根电缆最大允许功率 (MW)
+        
+    返回:
+        connections: 连接列表 [(source, target, length), ...]
+        turbines_with_cluster: 带有 'cluster' 列的 turbines DataFrame (用于兼容性)
+    """
+    
+    # 数据准备
+    n_turbines = len(turbines_df)
+    coords = turbines_df[['x', 'y']].values
+    powers = turbines_df['power'].values
+    ids = turbines_df['id'].values
+    
+    # 升压站坐标
+    if substation_coord.ndim > 1:
+        sx, sy = substation_coord[0][0], substation_coord[0][1]
+    else:
+        sx, sy = substation_coord[0], substation_coord[1]
+    
+    # 1. 计算距离矩阵
+    # 风机到风机
+    dist_matrix = distance_matrix(coords, coords)
+    # 风机到升压站
+    dists_to_sub = np.sqrt((coords[:, 0] - sx)**2 + (coords[:, 1] - sy)**2)
+    
+    # 2. 初始化组件 (Components)
+    # 初始状态下，每个风机是一个独立的组件，直接连接到升压站
+    # 为了方便查找，我们维护一个 components 字典
+    # key: component_root_id (代表该组件的唯一标识)
+    # value: {
+    #   'members': {node_idx, ...}, 
+    #   'total_power': float, 
+    #   'gate_node': int (连接到升压站的节点索引), 
+    #   'gate_cost': float (gate_node 到升压站的距离)
+    # }
+    
+    components = {}
+    for i in range(n_turbines):
+        components[i] = {
+            'members': {i},
+            'total_power': powers[i],
+            'gate_node': i,
+            'gate_cost': dists_to_sub[i]
+        }
+        
+    # 记录已经建立的连接 (不包括通往升压站的默认连接)
+    # 格式: (u, v, length)
+    established_edges = []
+    # 记录已建立连接的坐标，用于交叉检查: [((x1, y1), (x2, y2)), ...]
+    established_lines = []
+
+    def do_intersect(p1, p2, p3, p4):
+        """
+        检测线段 (p1, p2) 和 (p3, p4) 是否严格相交 (不包括端点接触)
+        """
+        # 检查是否共享端点
+        if (p1[0]==p3[0] and p1[1]==p3[1]) or (p1[0]==p4[0] and p1[1]==p4[1]) or \
+           (p2[0]==p3[0] and p2[1]==p3[1]) or (p2[0]==p4[0] and p2[1]==p4[1]):
+            return False
+            
+        def ccw(A, B, C):
+            # 向量叉积
+            return (C[1]-A[1]) * (B[0]-A[0]) - (B[1]-A[1]) * (C[0]-A[0])
+
+        # 如果跨立实验符号相反，则相交
+        d1 = ccw(p1, p2, p3)
+        d2 = ccw(p1, p2, p4)
+        d3 = ccw(p3, p4, p1)
+        d4 = ccw(p3, p4, p2)
+        
+        # 严格相交判断 (忽略共线重叠的情况，视为不交叉)
+        if ((d1 > 1e-9 and d2 < -1e-9) or (d1 < -1e-9 and d2 > 1e-9)) and \
+           ((d3 > 1e-9 and d4 < -1e-9) or (d3 < -1e-9 and d4 > 1e-9)):
+            return True
+            
+        return False
+    
+    # 3. 迭代优化
+    while True:
+        # 预先收集当前所有组件的 Gate Edges (连接升压站的线段)
+        # 格式: {cid: (gate_node_coord, substation_coord)}
+        current_gate_lines = {}
+        sub_coord_tuple = (sx, sy)
+        for cid, data in components.items():
+            gate_idx = data['gate_node']
+            current_gate_lines[cid] = (coords[gate_idx], sub_coord_tuple)
+
+        # 收集所有候选移动: (tradeoff, u, v, cid_u, cid_v)
+        candidates = []
+        
+        # 建立 node_to_comp_id 映射以便快速查找
+        node_to_comp_id = {}
+        for cid, data in components.items():
+            for member in data['members']:
+                node_to_comp_id[member] = cid
+                
+        # 遍历所有边 (i, j)
+        for i in range(n_turbines):
+            cid_i = node_to_comp_id[i]
+            gate_cost_i = components[cid_i]['gate_cost']
+            
+            for j in range(n_turbines):
+                if i == j: continue
+                
+                cid_j = node_to_comp_id[j]
+                
+                # 必须是不同组件
+                if cid_i == cid_j:
+                    continue
+                
+                # 检查容量约束
+                if components[cid_i]['total_power'] + components[cid_j]['total_power'] > max_capacity_mw:
+                    continue
+                
+                # 计算 Tradeoff
+                dist_ij = dist_matrix[i, j]
+                tradeoff = dist_ij - gate_cost_i
+                
+                # 只有当 tradeoff < 0 时，合并才是有益的
+                if tradeoff < -1e-9:
+                    candidates.append((tradeoff, i, j, cid_i, cid_j))
+        
+        # 按 tradeoff 排序 (从小到大，越小越好)
+        candidates.sort(key=lambda x: x[0])
+        
+        best_move = None
+        
+        # 延迟检测: 从最好的开始检查交叉
+        for cand in candidates:
+            tradeoff, u, v, cid_u, cid_v = cand
+            
+            p_u = coords[u]
+            p_v = coords[v]
+            
+            # 快速包围盒测试 (AABB) 准备
+            min_x_uv, max_x_uv = min(p_u[0], p_v[0]), max(p_u[0], p_v[0])
+            min_y_uv, max_y_uv = min(p_u[1], p_v[1]), max(p_u[1], p_v[1])
+            
+            is_crossing = False
+            
+            # 1. 检查与已固定的内部边的交叉
+            for line in established_lines:
+                p_a, p_b = line[0], line[1]
+                
+                if max(p_a[0], p_b[0]) < min_x_uv or min(p_a[0], p_b[0]) > max_x_uv or \
+                   max(p_a[1], p_b[1]) < min_y_uv or min(p_a[1], p_b[1]) > max_y_uv:
+                    continue
+                
+                if do_intersect(p_u, p_v, p_a, p_b):
+                    is_crossing = True
+                    break
+            
+            if is_crossing:
+                continue
+                
+            # 2. 检查与所有活跃 Gate Edges 的交叉 (排除被移除的那个)
+            # 正在合并 cid_u -> cid_v，意味着 cid_u 的 Gate 将被移除。
+            # 但 cid_v 的 Gate 以及其他所有组件的 Gate 仍然存在。
+            for cid, gate_line in current_gate_lines.items():
+                if cid == cid_u: 
+                    continue # 这个 Gate 即将移除，不构成障碍
+                
+                p_a, p_b = gate_line[0], gate_line[1]
+                
+                if max(p_a[0], p_b[0]) < min_x_uv or min(p_a[0], p_b[0]) > max_x_uv or \
+                   max(p_a[1], p_b[1]) < min_y_uv or min(p_a[1], p_b[1]) > max_y_uv:
+                    continue
+                    
+                if do_intersect(p_u, p_v, p_a, p_b):
+                    is_crossing = True
+                    break
+            
+            if not is_crossing:
+                best_move = (u, v, cid_u, cid_v)
+                break
+        
+        # 如果没有找到有益的合并，或者所有可行合并都会增加成本，则停止
+        if best_move is None:
+            break
+            
+        # 执行合并
+        u, v, cid_u, cid_v = best_move
+        
+        # 将 cid_u 并入 cid_v
+        # 1. 记录新边
+        established_edges.append((u, v, dist_matrix[u, v]))
+        established_lines.append((coords[u], coords[v]))
+        
+        # 2. 更新组件信息
+        comp_u = components[cid_u]
+        comp_v = components[cid_v]
+        
+        # 合并成员
+        comp_v['members'].update(comp_u['members'])
+        comp_v['total_power'] += comp_u['total_power']
+        
+        # Gate 节点和 Cost 保持 cid_v 的不变 
+        # (因为我们将 U 接到了 V 上，U 的原 gate 被移除，V 的 gate 仍是通往升压站的路径)
+        
+        # 3. 删除旧组件
+        del components[cid_u]
+        
+    # 4. 构建最终结果
+    connections = []
+    
+    # 添加内部边 (风机间)
+    for u, v, length in established_edges:
+        source = f'turbine_{u}'
+        target = f'turbine_{v}'
+        connections.append((source, target, length))
+        
+    # 添加 Gate 边 (风机到升压站)
+    # 此时 components 中剩下的每个组件都有一个 gate_node 连接到 Substation
+    cluster_mapping = {} # node_id -> cluster_id (0..N-1)
+    
+    for idx, (cid, data) in enumerate(components.items()):
+        gate_node = data['gate_node']
+        gate_cost = data['gate_cost']
+        
+        connections.append((f'turbine_{gate_node}', 'substation', gate_cost))
+        
+        # 记录 cluster id
+        for member in data['members']:
+            cluster_mapping[member] = idx
+            
+    # 更新 turbines DataFrame
+    turbines_with_cluster = turbines_df.copy()
+    turbines_with_cluster['cluster'] = turbines_with_cluster['id'].map(cluster_mapping)
+    
+    return connections, turbines_with_cluster

generate_template.py

@@ -11,7 +11,8 @@ def create_template():
         'ID': 'Sub1',
         'X': 4000,
         'Y': -800,
-        'Power': 0
+        'Power': 0,
+        'PlatformHeight': 0
     })
     
     # Add Turbines (Grid layout)
@@ -29,15 +30,32 @@ def create_template():
             'ID': i,
             'X': x,
             'Y': y,
-            'Power': np.random.uniform(6.0, 10.0)
+            'Power': np.random.uniform(6.0, 10.0),
+            'PlatformHeight': 0
         })
         
     df = pd.DataFrame(data)
     
+    # Create Cable data
+    cable_data = [
+        {'CrossSection': 35, 'Capacity': 150, 'Resistance': 0.524, 'Cost': 80, 'Optional': ''},
+        {'CrossSection': 70, 'Capacity': 215, 'Resistance': 0.268, 'Cost': 120, 'Optional': ''},
+        {'CrossSection': 95, 'Capacity': 260, 'Resistance': 0.193, 'Cost': 150, 'Optional': ''},
+        {'CrossSection': 120, 'Capacity': 295, 'Resistance': 0.153, 'Cost': 180, 'Optional': ''},
+        {'CrossSection': 150, 'Capacity': 330, 'Resistance': 0.124, 'Cost': 220, 'Optional': ''},
+        {'CrossSection': 185, 'Capacity': 370, 'Resistance': 0.0991, 'Cost': 270, 'Optional': ''},
+        {'CrossSection': 240, 'Capacity': 425, 'Resistance': 0.0754, 'Cost': 350, 'Optional': ''},
+        {'CrossSection': 300, 'Capacity': 500, 'Resistance': 0.0601, 'Cost': 450, 'Optional': ''},
+        {'CrossSection': 400, 'Capacity': 580, 'Resistance': 0.0470, 'Cost': 600, 'Optional': ''}
+    ]
+    df_cables = pd.DataFrame(cable_data)
+    
     # Save to Excel
     output_file = 'coordinates.xlsx'
-    df.to_excel(output_file, index=False)
-    print(f"Created sample file: {output_file}")
+    with pd.ExcelWriter(output_file) as writer:
+        df.to_excel(writer, sheet_name='Coordinates', index=False)
+        df_cables.to_excel(writer, sheet_name='Cables', index=False)
+    print(f"Created sample file: {output_file} with sheets 'Coordinates' and 'Cables'")
 
 if __name__ == "__main__":
     create_template()

main.py

@@ -7,11 +7,18 @@ from sklearn.cluster import KMeans
 from collections import defaultdict
 import networkx as nx
 import math
+import argparse
+import os
+from esau_williams import design_with_esau_williams
 
 # 设置matplotlib支持中文显示
 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial']
 plt.rcParams['axes.unicode_minus'] = False
 
+# 常量定义
+VOLTAGE_LEVEL = 66000  # 66kV
+POWER_FACTOR = 0.95
+
 # 1. 生成风电场数据（实际应用中替换为真实坐标）
 def generate_wind_farm_data(n_turbines=30, seed=42, layout='random', spacing=800):
     """
@@ -61,6 +68,7 @@ def generate_wind_farm_data(n_turbines=30, seed=42, layout='random', spacing=800
         'x': x_coords,
         'y': y_coords,
         'power': power_ratings,
+        'platform_height': np.zeros(n_turbines),
         'cumulative_power': np.zeros(n_turbines)  # 用于后续计算
     })
     
@@ -69,11 +77,19 @@ def generate_wind_farm_data(n_turbines=30, seed=42, layout='random', spacing=800
 # 1.5 从Excel加载数据
 def load_data_from_excel(file_path):
     """
-    从Excel文件读取风机和升压站坐标
-    Excel格式要求包含列: Type (Turbine/Substation), ID, X, Y, Power
+    从Excel文件读取风机和升压站坐标，以及可选的电缆规格
+    Excel格式要求:
+    - Sheet 'Coordinates' (或第一个Sheet): Type (Turbine/Substation), ID, X, Y, Power
+    - Sheet 'Cables' (可选): CrossSection, Capacity, Resistance, Cost
     """
     try:
-        df = pd.read_excel(file_path)
+        xl = pd.ExcelFile(file_path)
+        
+        # 读取坐标数据
+        if 'Coordinates' in xl.sheet_names:
+            df = pd.read_excel(xl, 'Coordinates')
+        else:
+            df = pd.read_excel(xl, 0)
         
         # 标准化列名（忽略大小写）
         df.columns = [c.capitalize() for c in df.columns]
@@ -94,6 +110,15 @@ def load_data_from_excel(file_path):
         if len(turbines_df) == 0:
             raise ValueError("未在文件中找到风机(Turbine)数据")
             
+        # 尝试获取平台高度列 (兼容不同命名)
+        platform_height_col = None
+        for col in turbines_df.columns:
+            if col.lower().replace(' ', '') == 'platformheight':
+                platform_height_col = col
+                break
+        
+        platform_heights = turbines_df[platform_height_col].values if platform_height_col else np.zeros(len(turbines_df))
+
         # 重置索引并整理格式
         turbines = pd.DataFrame({
             'id': range(len(turbines_df)),
@@ -101,11 +126,41 @@ def load_data_from_excel(file_path):
             'x': turbines_df['X'].values,
             'y': turbines_df['Y'].values,
             'power': turbines_df['Power'].values,
+            'platform_height': platform_heights,
             'cumulative_power': np.zeros(len(turbines_df))
         })
         
+        # 读取电缆数据 (如果存在)
+        cable_specs = None
+        if 'Cables' in xl.sheet_names:
+            cables_df = pd.read_excel(xl, 'Cables')
+            # 标准化列名
+            cables_df.columns = [c.replace(' ', '').capitalize() for c in cables_df.columns] # Handle 'Cross Section' vs 'CrossSection'
+            
+            # 尝试匹配列
+            # 目标格式: (截面mm², 载流量A, 电阻Ω/km, 基准价格元/m, 是否可选)
+            specs = []
+            for _, row in cables_df.iterrows():
+                # 容错处理列名
+                section = row.get('Crosssection', row.get('Section', 0))
+                capacity = row.get('Capacity', row.get('Current', 0))
+                resistance = row.get('Resistance', 0)
+                cost = row.get('Cost', row.get('Price', 0))
+                optional_val = str(row.get('Optional', '')).strip().upper()
+                is_optional = (optional_val == 'Y')
+                
+                if section > 0 and capacity > 0:
+                    specs.append((section, capacity, resistance, cost, is_optional))
+            
+            if specs:
+                specs.sort(key=lambda x: x[1]) # 按载流量排序
+                cable_specs = specs
+        
         print(f"成功加载: {len(turbines)} 台风机, {len(substation)} 座升压站")
-        return turbines, substation
+        if cable_specs:
+            print(f"成功加载: {len(cable_specs)} 种电缆规格")
+            
+        return turbines, substation, cable_specs
         
     except Exception as e:
         print(f"读取Excel文件失败: {str(e)}")
@@ -220,20 +275,254 @@ def design_with_kmeans(turbines, substation, n_clusters=3):
     
     return cluster_connections + substation_connections, turbines
 
-# 常量定义
-VOLTAGE_LEVEL = 66000  # 66kV
-POWER_FACTOR = 0.95
-
-# 4. 电缆选型函数（简化版）
-def select_cable(power, length, is_offshore=False):
+# 3.5 带容量约束的扇区扫描算法 (Capacitated Sweep) - 基础版
+def design_with_capacitated_sweep(turbines, substation, cable_specs=None):
     """
-    基于功率和长度选择合适的电缆截面
-    :param is_offshore: 是否为海上环境（成本更高）
+    使用带容量约束的扇区扫描算法设计集电线路 (基础版：单次扫描)
+    原理：
+    1. 计算所有风机相对于升压站的角度。
+    2. 找到角度间隔最大的位置作为起始“切割线”，以避免切断密集的风机群。
+    3. 沿圆周方向扫描，贪婪地将风机加入当前回路，直到达到电缆容量上限。
+    4. 满载后开启新回路。
     """
-    # 成本乘数：海缆材料+敷设成本通常是陆缆的4-6倍
-    cost_multiplier = 5.0 if is_offshore else 1.0
+    # 1. 获取电缆最大容量
+    max_mw = get_max_cable_capacity_mw(cable_specs)
+    # print(f"DEBUG: 扇区扫描算法启动 - 单回路容量限制: {max_mw:.2f} MW")
     
-    # 电缆规格库: (截面mm², 载流量A, 电阻Ω/km, 基准价格元/m)
+    substation_coord = substation[0]
+    
+    # 2. 计算角度 (使用 arctan2 返回 -pi 到 pi)
+    # 避免直接修改原始DataFrame，使用副本
+    work_df = turbines.copy()
+    dx = work_df['x'] - substation_coord[0]
+    dy = work_df['y'] - substation_coord[1]
+    work_df['angle'] = np.arctan2(dy, dx)
+    
+    # 3. 寻找最佳起始角度 (最大角度间隙)
+    # 按角度排序
+    work_df = work_df.sort_values('angle').reset_index(drop=True) # 重置索引方便切片
+    
+    angles = work_df['angle'].values
+    n = len(angles)
+    
+    if n > 1:
+        # 计算相邻角度差
+        diffs = np.diff(angles)
+        # 计算首尾角度差 (跨越 ±pi 处)
+        wrap_diff = (2 * np.pi) - (angles[-1] - angles[0])
+        diffs = np.append(diffs, wrap_diff)
+        
+        # 找到最大间隙的索引
+        max_gap_idx = np.argmax(diffs)
+        
+        # 旋转数组，使最大间隙成为新的起点
+        start_idx = (max_gap_idx + 1) % n
+        
+        if start_idx != 0:
+            work_df = pd.concat([work_df.iloc[start_idx:], work_df.iloc[:start_idx]]).reset_index(drop=True)
+            
+    # 4. 贪婪分组 (Capacity Constrained Clustering)
+    work_df['cluster'] = -1
+    cluster_id = 0
+    current_power = 0
+    current_indices = []
+    
+    for i, row in work_df.iterrows():
+        p = row['power']
+        
+        if len(current_indices) > 0 and (current_power + p > max_mw):
+            work_df.loc[current_indices, 'cluster'] = cluster_id
+            cluster_id += 1
+            current_power = 0
+            current_indices = []
+            
+        current_indices.append(i)
+        current_power += p
+        
+    if current_indices:
+        work_df.loc[current_indices, 'cluster'] = cluster_id
+        cluster_id += 1 
+        
+    # 建立 id -> cluster 的映射
+    id_to_cluster = dict(zip(work_df['id'], work_df['cluster']))
+    turbines['cluster'] = turbines['id'].map(id_to_cluster)
+    
+    # 5. 对每个簇内部进行MST连接
+    cluster_connections = []
+    substation_connections = []
+    n_clusters = cluster_id
+    
+    for cid in range(n_clusters):
+        cluster_turbines = turbines[turbines['cluster'] == cid]
+        if len(cluster_turbines) == 0:
+            continue
+            
+        cluster_indices = cluster_turbines.index.tolist()
+        coords = cluster_turbines[['x', 'y']].values
+        
+        if len(cluster_indices) > 1:
+            dist_matrix = distance_matrix(coords, coords)
+            mst = minimum_spanning_tree(dist_matrix).toarray()
+            
+            for i in range(len(cluster_indices)):
+                for j in range(len(cluster_indices)):
+                    if mst[i, j] > 0:
+                        source = f'turbine_{cluster_indices[i]}'
+                        target = f'turbine_{cluster_indices[j]}'
+                        cluster_connections.append((source, target, mst[i, j]))
+        
+        # 连接到升压站
+        dists = np.sqrt((cluster_turbines['x'] - substation_coord[0])**2 + 
+                        (cluster_turbines['y'] - substation_coord[1])**2)
+        closest_id = dists.idxmin() 
+        min_dist = dists.min()
+        substation_connections.append((f'turbine_{closest_id}', 'substation', min_dist))
+    
+    return cluster_connections + substation_connections, turbines
+
+# 3.6 旋转扫描算法 (Rotational Sweep) - 优化版
+def design_with_rotational_sweep(turbines, substation, cable_specs=None):
+    """
+    使用带容量约束的扇区扫描算法设计集电线路 (优化版：旋转扫描)
+    原理：
+    1. 计算所有风机相对于升压站的角度并排序。
+    2. 遍历所有可能的起始角度（即尝试以每一台风机作为扫描的起点）。
+    3. 对每种起始角度，贪婪地将风机加入回路直到满载。
+    4. 对每种分组方案计算MST成本，选出总成本最低的方案。
+    """
+    # 1. 获取电缆最大容量
+    max_mw = get_max_cable_capacity_mw(cable_specs)
+    # print(f"DEBUG: 扇区扫描算法启动 - 单回路容量限制: {max_mw:.2f} MW")
+    
+    substation_coord = substation[0]
+    
+    # 2. 计算角度 (使用 arctan2 返回 -pi 到 pi)
+    work_df = turbines.copy()
+    dx = work_df['x'] - substation_coord[0]
+    dy = work_df['y'] - substation_coord[1]
+    work_df['angle'] = np.arctan2(dy, dx)
+    
+    # 按角度排序
+    work_df = work_df.sort_values('angle').reset_index(drop=True)
+    
+    n_turbines = len(work_df)
+    best_cost = float('inf')
+    best_connections = []
+    best_turbines_state = None
+    best_start_idx = -1
+    
+    # 遍历所有可能的起始点
+    for start_idx in range(n_turbines):
+        
+        # 构建当前旋转顺序的风机列表
+        if start_idx == 0:
+            current_df = work_df.copy()
+        else:
+            current_df = pd.concat([work_df.iloc[start_idx:], work_df.iloc[:start_idx]]).reset_index(drop=True)
+            
+        # --- 贪婪分组 ---
+        current_df['cluster'] = -1
+        cluster_id = 0
+        current_power = 0
+        current_indices_in_df = [] 
+        
+        powers = current_df['power'].values
+        
+        for i in range(n_turbines):
+            p = powers[i]
+            
+            if len(current_indices_in_df) > 0 and (current_power + p > max_mw):
+                current_df.loc[current_indices_in_df, 'cluster'] = cluster_id
+                cluster_id += 1
+                current_power = 0
+                current_indices_in_df = []
+            
+            current_indices_in_df.append(i)
+            current_power += p
+            
+        if current_indices_in_df:
+            current_df.loc[current_indices_in_df, 'cluster'] = cluster_id
+            cluster_id += 1
+            
+        # --- 计算该分组方案的成本 ---
+        current_total_length = 0
+        
+        n_clusters = cluster_id
+        for cid in range(n_clusters):
+            cluster_rows = current_df[current_df['cluster'] == cid]
+            if len(cluster_rows) == 0: continue
+            
+            # 1. 簇内 MST 长度
+            coords = cluster_rows[['x', 'y']].values
+            if len(cluster_rows) > 1:
+                dm = distance_matrix(coords, coords)
+                mst = minimum_spanning_tree(dm).toarray()
+                mst_len = mst.sum()
+                current_total_length += mst_len
+            
+            # 2. 连接升压站长度
+            dists = np.sqrt((cluster_rows['x'] - substation_coord[0])**2 + 
+                            (cluster_rows['y'] - substation_coord[1])**2)
+            min_dist = dists.min()
+            current_total_length += min_dist
+            
+        # --- 比较并保存最佳结果 ---
+        if current_total_length < best_cost:
+            best_cost = current_total_length
+            best_start_idx = start_idx
+            best_id_to_cluster = dict(zip(current_df['id'], current_df['cluster']))
+
+    # --- 根据最佳方案重新生成详细连接 ---
+    turbines['cluster'] = turbines['id'].map(best_id_to_cluster)
+    
+    final_connections = []
+    
+    unique_clusters = turbines['cluster'].unique()
+    unique_clusters = [c for c in unique_clusters if not pd.isna(c) and c >= 0]
+    
+    for cid in unique_clusters:
+        cluster_turbines = turbines[turbines['cluster'] == cid]
+        if len(cluster_turbines) == 0: continue
+            
+        cluster_indices = cluster_turbines.index.tolist()
+        coords = cluster_turbines[['x', 'y']].values
+        
+        if len(cluster_indices) > 1:
+            dist_matrix_local = distance_matrix(coords, coords)
+            mst = minimum_spanning_tree(dist_matrix_local).toarray()
+            
+            for i in range(len(cluster_indices)):
+                for j in range(len(cluster_indices)):
+                    if mst[i, j] > 0:
+                        source = f'turbine_{cluster_indices[i]}'
+                        target = f'turbine_{cluster_indices[j]}'
+                        final_connections.append((source, target, mst[i, j]))
+        
+        dists = np.sqrt((cluster_turbines['x'] - substation_coord[0])**2 + 
+                        (cluster_turbines['y'] - substation_coord[1])**2)
+        closest_idx_in_df = dists.idxmin()
+        min_dist = dists.min()
+        final_connections.append((f'turbine_{closest_idx_in_df}', 'substation', min_dist))
+        
+    return final_connections, turbines
+
+def get_max_cable_capacity_mw(cable_specs=None):
+    """
+    计算给定电缆规格中能够承载的最大功率 (单位: MW)。
+    
+    基于提供的电缆规格列表，选取最大载流量，结合系统电压和功率因数计算理论最大传输功率。
+    
+    参数:
+        cable_specs (list, optional): 电缆规格列表。每个元素应包含 (截面积, 额定电流, 单价, 损耗系数)。
+    
+    返回:
+        float: 最大功率承载能力 (MW)。
+        
+    异常:
+        Exception: 当未提供 cable_specs 时抛出，提示截面不满足。
+    """
+    if cable_specs is None:
+        # Default cable specs if not provided (same as in evaluate_design)
         cable_specs = [
             (35, 150, 0.524, 80),
             (70, 215, 0.268, 120),
@@ -242,44 +531,23 @@ def select_cable(power, length, is_offshore=False):
             (150, 330, 0.124, 220),
             (185, 370, 0.0991, 270),
             (240, 425, 0.0754, 350),
-        (300, 500, 0.0601, 450), # 增加大截面适应海上大功率
+            (300, 500, 0.0601, 450), 
             (400, 580, 0.0470, 600)
         ]
 
-    # 估算电流
-    # power是MW, 换算成W需要 * 1e6
-    current = (power * 1e6) / (np.sqrt(3) * VOLTAGE_LEVEL * POWER_FACTOR)
+    # 从所有电缆规格中找到最大的额定电流容量
+    max_current_capacity = max(spec[1] for spec in cable_specs)
         
-    # 选择满足载流量的最小电缆
-    selected_spec = None
-    for spec in cable_specs:
-        if current <= spec[1] * 0.8:  # 80%负载率
-            selected_spec = spec
-            break
-    
-    if selected_spec is None:
-        selected_spec = cable_specs[-1]
-        
-    resistance = selected_spec[2] * length / 1000  # 电阻(Ω)
-    cost = selected_spec[3] * length * cost_multiplier  # 电缆成本(含敷设)
-    
-    return {
-        'cross_section': selected_spec[0],
-        'current_capacity': selected_spec[1],
-        'resistance': resistance,
-        'cost': cost,
-        'current': current
-    }
-
-def get_max_cable_capacity_mw():
-    """计算最大电缆(400mm2)能承载的最大功率(MW)"""
-    # 400mm2载流量580A
-    max_current = 580 * 0.8 # 80%降额
+    # 计算最大功率：P = √3 * U * I * cosφ
+    # 这里假设降额系数为 1 (不降额)
+    max_current = max_current_capacity * 1 
     max_power_w = np.sqrt(3) * VOLTAGE_LEVEL * max_current * POWER_FACTOR
-    return max_power_w / 1e6 # MW
+    
+    # 将单位从 W 转换为 MW
+    return max_power_w / 1e6 
 
 # 5. 计算集电线路方案成本
-def evaluate_design(turbines, connections, substation, is_offshore=False):
+def evaluate_design(turbines, connections, substation, cable_specs=None, is_offshore=False, method_name="Unknown Method"):
     """评估设计方案的总成本和损耗"""
     total_cost = 0
     total_loss = 0
@@ -320,14 +588,31 @@ def evaluate_design(turbines, connections, substation, is_offshore=False):
         except nx.NetworkXNoPath:
             pass 
             
-    # DEBUG: 打印最大功率流
-    max_power = max(power_flow.values()) if power_flow else 0
-    print(f"DEBUG: 最大线路功率 = {max_power:.2f} MW")
+    # DEBUG: 打印最大功率流 (不含升压站本身)
+    node_powers = [v for k, v in power_flow.items() if k != 'substation']
+    max_power = max(node_powers) if node_powers else 0
+    print(f"DEBUG [{method_name}]: 最大线路功率 = {max_power:.2f} MW")
     
     # 计算成本和损耗
     detailed_connections = []
     
     for source, target, length in connections:
+        # Determine vertical length (PlatformHeight)
+        vertical_length = 0
+        
+        if source.startswith('turbine_'):
+            tid = int(source.split('_')[1])
+            vertical_length += turbines.loc[tid, 'platform_height']
+            
+        if target.startswith('turbine_'):
+            tid = int(target.split('_')[1])
+            vertical_length += turbines.loc[tid, 'platform_height']
+            
+        # Calculate effective length with margin
+        # Total Length = (Horizontal Distance + Vertical Up/Down) * 1.03
+        horizontal_length = length
+        effective_length = (horizontal_length + vertical_length) * 1.03
+        
         # 确定该段线路承载的总功率
         if source.startswith('turbine_') and target.startswith('turbine_'):
             # 风机间连接，取下游节点功率
@@ -345,13 +630,57 @@ def evaluate_design(turbines, connections, substation, is_offshore=False):
             power = 0
         
         # 电缆选型
-        cable = select_cable(power, length, is_offshore=is_offshore)
+        # 成本乘数：如果Excel中已包含敷设费用，则设为1.0
+        cost_multiplier = 1.0 if is_offshore else 1.0
+        
+        # 默认电缆规格库 (如果未提供)
+        if cable_specs is None:
+            cable_specs_to_use = [
+                (35, 150, 0.524, 80),
+                (70, 215, 0.268, 120),
+                (95, 260, 0.193, 150),
+                (120, 295, 0.153, 180),
+                (150, 330, 0.124, 220),
+                (185, 370, 0.0991, 270),
+                (240, 425, 0.0754, 350),
+                (300, 500, 0.0601, 450), 
+                (400, 580, 0.0470, 600)
+            ]
+        else:
+            cable_specs_to_use = cable_specs
+        
+        # 估算电流
+        current = (power * 1e6) / (np.sqrt(3) * VOLTAGE_LEVEL * POWER_FACTOR)
+        
+        # 选择满足载流量的最小电缆
+        selected_spec = None
+        for spec in cable_specs_to_use:
+            if current <= spec[1] * 1:  # 100%负载率
+                selected_spec = spec
+                break
+        
+        if selected_spec is None:
+            selected_spec = cable_specs_to_use[-1]
+            print(f"WARNING [{method_name}]: Current {current:.2f} A (Power: {power:.2f} MW) exceeds max cable capacity {selected_spec[1]} A!")
+            
+        resistance = selected_spec[2] * effective_length / 1000  # 电阻(Ω)
+        cost = selected_spec[3] * effective_length * cost_multiplier  # 电缆成本(含敷设)
+        
+        cable = {
+            'cross_section': selected_spec[0],
+            'current_capacity': selected_spec[1],
+            'resistance': resistance,
+            'cost': cost,
+            'current': current
+        }
         
         # 记录详细信息
         detailed_connections.append({
             'source': source,
             'target': target,
-            'length': length,
+            'horizontal_length': horizontal_length,
+            'vertical_length': vertical_length,
+            'length': effective_length, # effective length used for stats
             'power': power,
             'cable': cable
         })
@@ -385,15 +714,32 @@ def export_to_dxf(turbines, substation, connections_details, filename):
     # 1. 建立图层
     doc.layers.add('Substation', color=1) # Red
     doc.layers.add('Turbines', color=5)   # Blue
-    doc.layers.add('Cable_35mm', color=3) # Green
-    doc.layers.add('Cable_70mm', color=130) 
-    doc.layers.add('Cable_95mm', color=150)
-    doc.layers.add('Cable_120mm', color=4) # Cyan
-    doc.layers.add('Cable_150mm', color=6) # Magenta
-    doc.layers.add('Cable_185mm', color=30) # Orange
-    doc.layers.add('Cable_240mm', color=1) # Red
-    doc.layers.add('Cable_300mm', color=250) 
-    doc.layers.add('Cable_400mm', color=7) # White/Black
+    
+    # 动态确定电缆颜色
+    # 提取所有使用到的电缆截面
+    used_sections = sorted(list(set(conn['cable']['cross_section'] for conn in connections_details)))
+    
+    # 定义颜色映射规则 (AutoCAD Color Index)
+    # 1=Red, 2=Yellow, 3=Green, 4=Cyan, 5=Blue, 6=Magenta
+    color_rank = [
+        2, # 1st (Smallest): Yellow
+        3, # 2nd: Green
+        1, # 3rd: Red
+        5, # 4th: Blue
+        6  # 5th: Magenta
+    ]
+    default_color = 3 # Others: Green
+    
+    # 创建电缆图层
+    for i, section in enumerate(used_sections):
+        if i < len(color_rank):
+            c = color_rank[i]
+        else:
+            c = default_color
+            
+        layer_name = f'Cable_{section}mm'
+        if layer_name not in doc.layers:
+            doc.layers.add(layer_name, color=c)
     
     # 2. 绘制升压站
     sx, sy = substation[0, 0], substation[0, 1]
@@ -418,7 +764,6 @@ def export_to_dxf(turbines, substation, connections_details, filename):
         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:
@@ -436,8 +781,8 @@ def export_to_dxf(turbines, substation, connections_details, filename):
         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})
+        # 绘制二维多段线
+        msp.add_lwpolyline([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
@@ -450,6 +795,103 @@ def export_to_dxf(turbines, substation, connections_details, filename):
     except Exception as e:
         print(f"导出DXF失败: {e}")
 
+# 6.6 导出Excel报表
+def export_to_excel(connections_details, filename):
+    """
+    将设计方案详情导出为Excel文件
+    :param connections_details: evaluate_design返回的'details'列表
+    """
+    data = []
+    for conn in connections_details:
+        data.append({
+            'Source': conn['source'],
+            'Target': conn['target'],
+            'Horizontal Length (m)': conn['horizontal_length'],
+            'Vertical Length (m)': conn['vertical_length'],
+            'Effective Length (m)': conn['length'],
+            'Cable Type (mm²)': conn['cable']['cross_section'],
+            'Current (A)': conn['cable']['current'],
+            'Power (MW)': conn['power'],
+            'Resistance (Ω)': conn['cable']['resistance'],
+            'Cost (¥)': conn['cable']['cost']
+        })
+        
+    df = pd.DataFrame(data)
+    
+    # 汇总统计
+    summary = {
+        'Total Cost (¥)': df['Cost (¥)'].sum(),
+        'Total Effective Length (m)': df['Effective Length (m)'].sum(),
+        'Total Vertical Length (m)': df['Vertical Length (m)'].sum()
+    }
+    summary_df = pd.DataFrame([summary])
+    
+    try:
+        with pd.ExcelWriter(filename) as writer:
+            df.to_excel(writer, sheet_name='Cable Schedule', index=False)
+            summary_df.to_excel(writer, sheet_name='Summary', index=False)
+        print(f"成功导出Excel文件: {filename}")
+    except Exception as e:
+        print(f"导出Excel失败: {e}")
+
+# 6.6 导出多方案对比Excel报表
+def export_all_scenarios_to_excel(results, filename):
+    """
+    导出所有方案的对比结果到 Excel
+    :param results: 包含各方案评估结果的列表
+    :param filename: 输出文件路径
+    """
+    try:
+        with pd.ExcelWriter(filename) as writer:
+            # 1. 总览 Sheet
+            summary_data = []
+            for res in results:
+                # 获取回路数
+                n_circuits = 0
+                if 'turbines' in res and 'cluster' in res['turbines'].columns:
+                     n_circuits = res['turbines']['cluster'].nunique()
+                
+                summary_data.append({
+                    'Scenario': res['name'],
+                    'Total Cost (¥)': res['cost'],
+                    'Total Loss (kW)': res['loss'],
+                    'Num Circuits': n_circuits,
+                    # 计算电缆统计
+                    'Total Cable Length (m)': sum(d['length'] for d in res['eval']['details'])
+                })
+            
+            pd.DataFrame(summary_data).to_excel(writer, sheet_name='Comparison Summary', index=False)
+            
+            # 2. 每个方案的详细 Sheet
+            for res in results:
+                # 清理 Sheet 名称
+                safe_name = res['name'].replace(':', '').replace('/', '-').replace('\\', '-')
+                # 截断过长的名称 (Excel限制31字符)
+                if len(safe_name) > 25: 
+                    safe_name = safe_name[:25]
+                
+                details = res['eval']['details']
+                data = []
+                for conn in details:
+                    data.append({
+                        'Source': conn['source'],
+                        'Target': conn['target'],
+                        'Horizontal Length (m)': conn['horizontal_length'],
+                        'Vertical Length (m)': conn['vertical_length'],
+                        'Effective Length (m)': conn['length'],
+                        'Cable Type (mm²)': conn['cable']['cross_section'],
+                        'Current (A)': conn['cable']['current'],
+                        'Power (MW)': conn['power'],
+                        'Resistance (Ω)': conn['cable']['resistance'],
+                        'Cost (¥)': conn['cable']['cost']
+                    })
+                df = pd.DataFrame(data)
+                df.to_excel(writer, sheet_name=safe_name, index=False)
+                
+        print(f"成功导出包含所有方案的Excel文件: {filename}")
+    except Exception as e:
+        print(f"导出Excel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"""
@@ -562,96 +1004,260 @@ def visualize_design(turbines, substation, connections, title, ax=None, show_cos
     return ax
 
 # 7. 主函数：比较两种设计方法
-def compare_design_methods(excel_path=None):
+def compare_design_methods(excel_path=None, n_clusters_override=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
+    比较MST和三种电缆方案下的K-means设计方法
+    :param excel_path: Excel文件路径
+    :param n_clusters_override: 可选，手动指定簇的数量
     """
+    cable_specs = None
     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)
+            turbines, substation, cable_specs = 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)
+            return compare_design_methods(excel_path=None, n_clusters_override=n_clusters_override)
     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
+    # 准备三种电缆方案
+    # 原始 specs 是 5 元素元组: (section, capacity, resistance, cost, is_optional)
+    # 下游函数期望 4 元素元组: (section, capacity, resistance, cost)
+    if cable_specs:
+        # 方案 1: 不含 Optional='Y' (Standard)
+        specs_1 = [s[:4] for s in cable_specs if not s[4]]
+        
+        # 方案 2: 含 Optional='Y' (All)
+        specs_2 = [s[:4] for s in cable_specs]
+        
+        # 方案 3: 基于方案 1，删掉截面最大的一种
+        # cable_specs 已按 capacity 排序，假设 capacity 与 section 正相关
+        specs_3 = specs_1[:-1] if len(specs_1) > 1 else list(specs_1)
+    else:
+        # 默认电缆库
+        default_specs = [
+            (35, 150, 0.524, 80), (70, 215, 0.268, 120), (95, 260, 0.193, 150),
+            (120, 295, 0.153, 180), (150, 330, 0.124, 220), (185, 370, 0.0991, 270),
+            (240, 425, 0.0754, 350), (300, 500, 0.0601, 450), (400, 580, 0.0470, 600)
+        ]
+        specs_1 = default_specs
+        specs_2 = default_specs
+        specs_3 = default_specs[:-1]
+
+    scenarios = [
+        ("Scenario 1 (Standard)", specs_1),
+        ("Scenario 2 (With Optional)", specs_2),
+        ("Scenario 3 (No Max)", specs_3)
+    ]
+    
+    # 1. MST 方法作为基准 (使用 Scenario 1)
     mst_connections = design_with_mst(turbines, substation)
-    mst_evaluation = evaluate_design(turbines, mst_connections, substation, is_offshore=is_offshore)
+    mst_evaluation = evaluate_design(turbines, mst_connections, substation, cable_specs=specs_1, is_offshore=is_offshore, method_name="MST Method")
     
-    # 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))
+    # 准备画布 2x2
+    fig, axes = plt.subplots(2, 2, figsize=(20, 18))
+    axes = axes.flatten()
     
-    # 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
+    # 绘制 MST
     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", 
+                    f"MST Method (Standard Cables)\nTotal Cost: ¥{mst_evaluation['total_cost']/10000:.2f}万", 
                     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])
+    print(f"\n===== 开始比较电缆方案 =====")
+    
+    best_cost = float('inf')
+    best_result = None
+    
+    comparison_results = []
+    
+    # 将 MST 结果也加入对比列表，方便查看
+    comparison_results.append({
+        'name': 'MST Method',
+        'cost': mst_evaluation['total_cost'],
+        'loss': mst_evaluation['total_loss'],
+        'eval': mst_evaluation,
+        'turbines': turbines.copy(), # MST 不改变 turbines，但为了统一格式
+        'specs': specs_1
+    })
+
+    for i, (name, current_specs) in enumerate(scenarios):
+        print(f"\n--- {name} ---")
+        if not current_specs:
+            print("  无可用电缆，跳过。")
+            continue
+            
+        # 计算参数
+        total_power = turbines['power'].sum()
+        max_cable_mw = get_max_cable_capacity_mw(cable_specs=current_specs)
+        
+        # 确定簇数 (针对 Base 算法)
+        if n_clusters_override is not None:
+            n_clusters = n_clusters_override
+            min_needed = int(np.ceil(total_power / max_cable_mw))
+            if n_clusters < min_needed:
+                 print(f"  Warning: 指定簇数 {n_clusters} 小于理论最小需求 {min_needed}。")
+        else:
+            min_needed = int(np.ceil(total_power / max_cable_mw))
+            n_cable_types = len(current_specs)
+            heuristic = int(np.ceil(len(turbines) / n_cable_types))
+            n_clusters = max(min_needed, heuristic)
+            if n_clusters > len(turbines): n_clusters = len(turbines)
+            
+        print(f"  最大电缆容量: {max_cable_mw:.2f} MW")
+        
+        # --- Run 1: Base Algorithm (Capacitated Sweep) ---
+        base_name = f"{name} (Base)"
+        conns_base, turbines_base = design_with_capacitated_sweep(
+            turbines.copy(), substation, cable_specs=current_specs
+        )
+        eval_base = evaluate_design(
+            turbines, conns_base, substation, cable_specs=current_specs, 
+            is_offshore=is_offshore, method_name=base_name
+        )
+        
+        comparison_results.append({
+            'name': base_name,
+            'cost': eval_base['total_cost'],
+            'loss': eval_base['total_loss'],
+            'eval': eval_base,
+            'turbines': turbines_base,
+            'specs': current_specs
+        })
+        print(f"  [Base]       Cost: ¥{eval_base['total_cost']:,.2f} | Loss: {eval_base['total_loss']:.2f} kW")
+
+        # --- Run 2: Rotational Algorithm (Optimization) ---
+        rot_name = f"{name} (Rotational)"
+        conns_rot, turbines_rot = design_with_rotational_sweep(
+            turbines.copy(), substation, cable_specs=current_specs
+        )
+        eval_rot = evaluate_design(
+            turbines, conns_rot, substation, cable_specs=current_specs, 
+            is_offshore=is_offshore, method_name=rot_name
+        )
+        
+        comparison_results.append({
+            'name': rot_name,
+            'cost': eval_rot['total_cost'],
+            'loss': eval_rot['total_loss'],
+            'eval': eval_rot,
+            'turbines': turbines_rot,
+            'specs': current_specs
+        })
+        print(f"  [Rotational] Cost: ¥{eval_rot['total_cost']:,.2f} | Loss: {eval_rot['total_loss']:.2f} kW")
+        
+        # --- Run 3: Esau-Williams Algorithm ---
+        ew_name = f"{name} (Esau-Williams)"
+        conns_ew, turbines_ew = design_with_esau_williams(
+            turbines.copy(), substation, max_cable_mw
+        )
+        eval_ew = evaluate_design(
+            turbines, conns_ew, substation, cable_specs=current_specs, 
+            is_offshore=is_offshore, method_name=ew_name
+        )
+        
+        comparison_results.append({
+            'name': ew_name,
+            'cost': eval_ew['total_cost'],
+            'loss': eval_ew['total_loss'],
+            'eval': eval_ew,
+            'turbines': turbines_ew,
+            'specs': current_specs
+        })
+        print(f"  [Esau-Williams] Cost: ¥{eval_ew['total_cost']:,.2f} | Loss: {eval_ew['total_loss']:.2f} kW")
+        
+        # 记录最佳
+        if eval_rot['total_cost'] < best_cost:
+            best_cost = eval_rot['total_cost']
+            
+        if eval_ew['total_cost'] < best_cost:
+            best_cost = eval_ew['total_cost']
+            # best_result 不再需要单独维护，最后遍历 comparison_results 即可
+            
+        if eval_base['total_cost'] < best_cost:
+             best_cost = eval_base['total_cost']
+
+        # 可视化 (只画 Base 版本)
+        ax_idx = i + 1
+        if ax_idx < 4:
+            n_circuits = turbines_base['cluster'].nunique()
+            title = f"{base_name} ({n_circuits} circuits)\nCost: ¥{eval_base['total_cost']/10000:.2f}万"
+            visualize_design(turbines_base, substation, eval_base['details'], title, ax=axes[ax_idx])
 
     plt.tight_layout()
-    output_filename = 'wind_farm_design_imported.png' if excel_path else 'offshore_wind_farm_design.png'
+    output_filename = 'wind_farm_design_comparison.png'
     plt.savefig(output_filename, dpi=300)
+    print(f"\n比较图(Base版)已保存至: {output_filename}")
     
-    # 导出DXF
+    # 准备文件路径
+    if excel_path:
+        base_name = os.path.splitext(os.path.basename(excel_path))[0]
+        dir_name = os.path.dirname(excel_path)
+        dxf_filename = os.path.join(dir_name, f"{base_name}_design.dxf")
+        excel_out_filename = os.path.join(dir_name, f"{base_name}_design.xlsx")
+    else:
         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)
+        excel_out_filename = 'wind_farm_design.xlsx'
 
-    plt.show() 
+    # 导出所有方案到同一个 Excel
+    if comparison_results:
+        export_all_scenarios_to_excel(comparison_results, excel_out_filename)
         
-    # 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']}")
+        # 交互式选择导出 DXF
+        print("\n===== 方案选择 =====")
+        best_idx = 0
+        for i, res in enumerate(comparison_results):
+            if res['cost'] < comparison_results[best_idx]['cost']:
+                best_idx = i
+            print(f"  {i+1}. {res['name']} - Cost: ¥{res['cost']:,.2f}")
             
-    return results
+        print(f"推荐方案: {comparison_results[best_idx]['name']} (默认)")
+        
+        try:
+            choice_str = input(f"请输入要导出DXF的方案编号 (1-{len(comparison_results)})，或输入 'A' 导出全部: ").strip()
+            
+            if choice_str.upper() == 'A':
+                print("正在导出所有方案...")
+                base_dxf_name, ext = os.path.splitext(dxf_filename)
+                for res in comparison_results:
+                    # 生成文件名安全后缀
+                    safe_suffix = res['name'].replace(' ', '_').replace(':', '').replace('(', '').replace(')', '').replace('/', '-')
+                    current_filename = f"{base_dxf_name}_{safe_suffix}{ext}"
+                    print(f"  导出 '{res['name']}' -> {current_filename}")
+                    export_to_dxf(res['turbines'], substation, res['eval']['details'], current_filename)
+            else:
+                if not choice_str:
+                    choice = best_idx
+                else:
+                    choice = int(choice_str) - 1
+                    if choice < 0 or choice >= len(comparison_results):
+                        print("输入编号无效，将使用默认推荐方案。")
+                        choice = best_idx
+                
+                selected_res = comparison_results[choice]
+                print(f"正在导出 '{selected_res['name']}' 到 DXF: {dxf_filename} ...")
+                export_to_dxf(selected_res['turbines'], substation, selected_res['eval']['details'], dxf_filename)
+        except Exception as e:
+            print(f"输入处理出错: {e}，将使用默认推荐方案。")
+            selected_res = comparison_results[best_idx]
+            print(f"正在导出 '{selected_res['name']}' 到 DXF: {dxf_filename} ...")
+            export_to_dxf(selected_res['turbines'], substation, selected_res['eval']['details'], dxf_filename)
+            
+    return comparison_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()
+    # 解析命令行参数
+    parser = argparse.ArgumentParser(description='Wind Farm Collector System Design')
+    parser.add_argument('--excel', help='Path to the Excel coordinates file', default=None)
+    parser.add_argument('--clusters', type=int, help='Specify the number of clusters (circuits) manually', default=None)
+    args = parser.parse_args()
+
+    # 3. 运行比较
+    # 如果没有提供excel文件，将自动回退到生成数据模式
+    compare_design_methods(args.excel, n_clusters_override=args.clusters)

project_context.md

@@ -0,0 +1,61 @@
+# Project Context: Wind Farm Layout Optimization
+
+**Last Updated:** 2025-12-30
+**Project Path:** `D:\code\windfarm`
+**Current Goal:** Optimize offshore wind farm cable layout using MST and K-means algorithms, with realistic constraints and CAD export.
+
+## 1. System Overview
+The system simulates and designs the collection system (inter-array cables) for an offshore wind farm.
+It compares two main algorithms:
+1.  **MST (Minimum Spanning Tree)**: Global optimization of cable length. (Note: Often creates overloaded branches in large farms).
+2.  **Sector Clustering (K-means)**: Angular clustering to divide turbines into radial "strings" or "loops" feeding the substation. This is the preferred method for large offshore farms to ensure cable capacity constraints are met.
+
+## 2. Key Implementations
+
+### A. Data Handling
+- **Generation**: Can generate random or grid layouts.
+- **Import**: Supports reading coordinates from `coordinates.xlsx` (Columns: Type, ID, X, Y, Power).
+- **Units**: 
+    - Power in **MW** (input).
+    - Coordinates in **meters**.
+    - Voltage: **66 kV** (Code constant `VOLTAGE_LEVEL`).
+
+### B. Algorithms
+- **Angular K-means**: 
+    - Uses `(cosθ, sinθ)` of the angle relative to substation for clustering.
+    - Eliminates cable crossings between sectors.
+- **Dynamic Cluster Sizing**: 
+    - Automatically calculates the required number of clusters (feeders) based on: `Total_Power / Max_Cable_Capacity`.
+    - Ensures no string exceeds the thermal limit of the largest available cable.
+
+### C. Electrical Modeling
+- **Cable Sizing**: Selects from standard cross-sections (35mm² to 400mm²).
+- **Constraint**: Max cable capacity (400mm²) is approx. **50.4 MW** at 66kV/0.95PF.
+- **Loss Calc**: $I^2 R$ losses.
+
+### D. Visualization & Export
+- **Matplotlib**: Shows layout with color-coded cables (Green=Thin -> Red=Thick).
+- **DXF Export**: Uses `ezdxf` to generate `.dxf` files compatible with CAD.
+    - Layers: `Substation`, `Turbines`, `Cable_XXmm`.
+    - entities: Circles (Turbines), Polylines (Substation), Lines (Cables).
+
+## 3. Critical Logic & Constants
+- **Voltage**: 66,000 V
+- **Power Factor**: 0.95
+- **Max Current (400mm²)**: 580 A * 0.8 (derating) = 464 A.
+- **Unit Conversion**: Critical fix applied to convert MW to Watts for current calculation (`power * 1e6`).
+
+## 4. Current State & file Structure
+- `main.py`: Core logic.
+- `coordinates.xlsx`: Input data (if present).
+- `wind_farm_design_imported.png`: Latest visualization.
+- `wind_farm_design.dxf`: Latest CAD export.
+
+## 5. Known Behaviors
+- **MST Method**: Will report extremely high costs/losses for large farms because it creates a single tree structure that massively overloads the root cables. This is expected behavior (physically invalid but mathematically correct for unconstrained MST).
+- **K-means Method**: Produces realistic, valid designs with appropriate cable tapering (e.g., 400mm² at root, 35mm² at leaves).
+
+## 6. Future Improvements (Optional)
+- **Obstacle Avoidance**: Currently assumes open ocean.
+- **Loop Topology**: Current design is radial strings. Reliability could be improved with loop/ring structures.
+- **Substation Placement Optimization**: Currently fixed or calculated as centroid.