windfarm/main.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.spatial import distance_matrix
from scipy.sparse.csgraph import minimum_spanning_tree
from sklearn.cluster import KMeans
from collections import defaultdict
import networkx as nx
import math
import argparse

# 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):
    """
    生成模拟风电场数据
    :param layout: 'random' (随机) 或 'grid' (规则行列)
    :param spacing: 规则布局时的风机间距(m)
    """
    np.random.seed(seed)
    
    if layout == 'grid':
        # 计算行列数 (尽量接近方形)
        n_cols = int(np.ceil(np.sqrt(n_turbines)))
        n_rows = int(np.ceil(n_turbines / n_cols))
        
        x_coords = []
        y_coords = []
        
        for i in range(n_turbines):
            row = i // n_cols
            col = i % n_cols
            # 添加微小抖动模拟海上定位误差(±1%)
            jitter = spacing * 0.01
            x = col * spacing + np.random.uniform(-jitter, jitter)
            y = row * spacing + np.random.uniform(-jitter, jitter)
            x_coords.append(x)
            y_coords.append(y)
            
        x_coords = np.array(x_coords)
        y_coords = np.array(y_coords)
        
        # 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
        substation = np.array([[np.mean(x_coords), -spacing]])
        
    else:
        # 随机生成风机位置（扩大范围以适应更大容量）
        x_coords = np.random.uniform(0, 2000, n_turbines)
        y_coords = np.random.uniform(0, 2000, n_turbines)
        # 升压站位置
        substation = np.array([[0, 0]])
    
    # 随机生成风机额定功率（海上风机通常更大，6-10MW）
    power_ratings = np.random.uniform(6.0, 10.0, n_turbines) if layout == 'grid' else np.random.uniform(2.0, 5.0, n_turbines)
    
    # 创建DataFrame
    turbines = pd.DataFrame({
        'id': range(n_turbines),
        'x': x_coords,
        'y': y_coords,
        'power': power_ratings,
        'cumulative_power': np.zeros(n_turbines)  # 用于后续计算
    })
    
    return turbines, substation

# 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，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:
        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]
        
        required_cols = {'Type', 'X', 'Y', 'Power'}
        if not required_cols.issubset(df.columns):
            raise ValueError(f"Excel文件缺少必要列: {required_cols - set(df.columns)}")
            
        # 提取升压站数据
        substation_df = df[df['Type'].astype(str).str.lower() == 'substation']
        if len(substation_df) == 0:
            raise ValueError("未在文件中找到升压站(Substation)数据")
        
        substation = substation_df[['X', 'Y']].values
        
        # 提取风机数据
        turbines_df = df[df['Type'].astype(str).str.lower() == 'turbine'].copy()
        if len(turbines_df) == 0:
            raise ValueError("未在文件中找到风机(Turbine)数据")
            
        # 重置索引并整理格式
        turbines = pd.DataFrame({
            'id': range(len(turbines_df)),
            'original_id': (turbines_df['Id'].values if 'Id' in turbines_df.columns else range(len(turbines_df))),
            'x': turbines_df['X'].values,
            'y': turbines_df['Y'].values,
            'power': turbines_df['Power'].values,
            '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)
            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))
                
                if section > 0 and capacity > 0:
                    specs.append((section, capacity, resistance, cost))
            
            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")
        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)}")
        raise

# 2. 基于最小生成树(MST)的集电线路设计
def design_with_mst(turbines, substation):
    """使用最小生成树算法设计集电线路拓扑"""
    # 合并风机和升压站数据
    all_points = np.vstack([substation, turbines[['x', 'y']].values])
    n_points = len(all_points)
    
    # 计算距离矩阵
    dist_matrix = distance_matrix(all_points, all_points)
    
    # 计算最小生成树
    mst = minimum_spanning_tree(dist_matrix).toarray()
    
    # 提取连接关系
    connections = []
    for i in range(n_points):
        for j in range(n_points):
            if mst[i, j] > 0:
                # 确定节点类型：0是升压站，1+是风机
                source = 'substation' if i == 0 else f'turbine_{i-1}'
                target = 'substation' if j == 0 else f'turbine_{j-1}'
                connections.append((source, target, mst[i, j]))
    
    return connections

# 3. 基于扇区聚类（改进版K-means）的集电线路设计
def design_with_kmeans(turbines, substation, n_clusters=3):
    """
    使用基于角度的K-means聚类设计集电线路拓扑 (避免交叉)
    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
    """
    # 准备风机坐标数据
    turbine_coords = turbines[['x', 'y']].values
    substation_coord = substation[0]
    
    # 计算每台风机相对于升压站的角度和单位向量
    # 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
    dx = turbine_coords[:, 0] - substation_coord[0]
    dy = turbine_coords[:, 1] - substation_coord[1]
    
    # 归一化为单位向量 (对应角度特征)
    magnitudes = np.sqrt(dx**2 + dy**2)
    # 处理重合点避免除零
    with np.errstate(divide='ignore', invalid='ignore'):
        unit_x = dx / magnitudes
        unit_y = dy / magnitudes
    unit_x[magnitudes == 0] = 0
    unit_y[magnitudes == 0] = 0
    
    # 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
    # 如果纯粹按角度，设为单位向量即可
    features = np.column_stack([unit_x, unit_y])
    
    # 执行K-means聚类
    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
    turbines['cluster'] = kmeans.fit_predict(features)
    
    # 为每个簇找到最佳连接点（离升压站最近的风机）
    cluster_connection_points = {}
    
    for cluster_id in range(n_clusters):
        cluster_turbines = turbines[turbines['cluster'] == cluster_id]
        if len(cluster_turbines) == 0:
            continue
            
        distances_to_substation = np.sqrt(
            (cluster_turbines['x'] - substation_coord[0])**2 + 
            (cluster_turbines['y'] - substation_coord[1])**2
        )
        closest_idx = distances_to_substation.idxmin()
        cluster_connection_points[cluster_id] = closest_idx
    
    # 为每个簇内构建MST
    cluster_connections = []
    for cluster_id in range(n_clusters):
        # 获取当前簇的风机
        cluster_turbines = turbines[turbines['cluster'] == cluster_id]
        if len(cluster_turbines) == 0:
            continue
            
        cluster_indices = cluster_turbines.index.tolist()
        
        # 计算簇内距离矩阵
        coords = cluster_turbines[['x', 'y']].values
        dist_matrix = distance_matrix(coords, coords)
        
        # 计算簇内MST
        mst = minimum_spanning_tree(dist_matrix).toarray()
        
        # 添加簇内连接
        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]))
    
    # 添加簇到升压站的连接
    substation_connections = []
    for cluster_id, turbine_idx in cluster_connection_points.items():
        turbine_coord = turbines.loc[turbine_idx, ['x', 'y']].values
        distance = np.sqrt(
            (turbine_coord[0] - substation_coord[0])**2 + 
            (turbine_coord[1] - substation_coord[1])**2
        )
        substation_connections.append((f'turbine_{turbine_idx}', 'substation', distance))
    
    return cluster_connections + substation_connections, turbines

# 3.5 带容量约束的扇区扫描算法 (Capacitated Angular Sweep)
def design_with_capacitated_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
    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。
    """
    # 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)
    # 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 (该索引对应元素的 *后面* 是间隙)
        max_gap_idx = np.argmax(diffs)
        
        # 如果最大间隙不是在队尾，则需要旋转数组，使最大间隙成为新的起点（即队尾）
        # 实际上我们希望扫描的起点是最大间隙的“右侧”
        # 例如: [0, 10, 100, 110]. Gaps: 10, 90, 10, 250. Max gap is wrap (250). Start at 0 is fine.
        # 例如: [0, 10, 200, 210]. Gaps: 10, 190, 10, 150. Max gap is 190 (idx 1). 
        # 我们应该从 idx 2 (200) 开始扫描。
        
        start_idx = (max_gap_idx + 1) % n
        
        # 重新排序风机列表
        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']
        
        # 检查是否超载
        # 注意：如果是空簇，必须加入至少一个（即使单个风机超载也得加，但在风电中单机不会超载）
        if len(current_indices) > 0 and (current_power + p > max_mw):
            # 当前簇已满，结束当前簇，开启新簇
            work_df.loc[current_indices, 'cluster'] = cluster_id
            cluster_id += 1
            current_power = 0
            current_indices = []
            
        current_indices.append(i)
        current_power += p
        
    # 处理最后一个簇
    if current_indices:
        work_df.loc[current_indices, 'cluster'] = cluster_id
        cluster_id += 1 # 计数用
        
    print(f"DEBUG: 生成了 {cluster_id} 个回路 (簇)")
    
    # 将 cluster 标记映射回原始 turbines DataFrame (通过 original_id 或 索引匹配)
    # 这里我们简单地重建 turbines，因为 work_df 包含了所有信息且顺序变了
    # 为了保持外部一致性，我们把 cluster 列 map 回去
    
    # 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 (复用现有逻辑)
    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
            
        # --- 簇内 MST ---
        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 ---
        # 找到簇内离升压站最近的风机
        dists = np.sqrt((cluster_turbines['x'] - substation_coord[0])**2 + 
                        (cluster_turbines['y'] - substation_coord[1])**2)
        closest_id = dists.idxmin() # 返回的是原始DataFrame的index
        
        # 添加升压站连接
        min_dist = dists.min()
        substation_connections.append((f'turbine_{closest_id}', 'substation', min_dist))
    
    return cluster_connections + substation_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:
        # 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)
    else:
        # 如果没有传入电缆规格，通常意味着已尝试过所有规格但仍不满足需求
        raise Exception("没有提供电缆参数")
        
    # 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
    
    # 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, 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
    
    # 创建连接图
    graph = nx.Graph()
    graph.add_node('substation')
    
    for i, row in turbines.iterrows():
        graph.add_node(f'turbine_{i}')
    
    # 添加边
    for source, target, length in connections:
        graph.add_edge(source, target, weight=length)
    
    # 计算每台风机的累积功率（从叶到根）
    power_flow = defaultdict(float)
    
    # 按照后序遍历（从叶到根）
    nodes = list(nx.dfs_postorder_nodes(graph, 'substation'))
    
    for node in nodes:
        if node == 'substation':
            continue
            
        # 1. 如果节点是风机，先加上自身的功率
        if node.startswith('turbine_'):
            turbine_id = int(node.split('_')[1])
            power_flow[node] += turbines.loc[turbine_id, 'power']
    
        # 2. 找到上游节点（父节点）并将累积功率传递上去
        try:
            # 找到通往升压站的最短路径上的下一个节点
            path = nx.shortest_path(graph, source=node, target='substation')
            if len(path) > 1:
                parent = path[1] # path[0]是node自己，path[1]是父节点
                power_flow[parent] += power_flow[node]
        except nx.NetworkXNoPath:
            pass 
            
    # 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:
        # 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
            if nx.shortest_path_length(graph, 'substation', source) < nx.shortest_path_length(graph, 'substation', target):
                power = power_flow[target]
            else:
                power = power_flow[source]
        elif source == 'substation':
            # 从升压站出发的连接
            power = power_flow[target]
        elif target == 'substation':
            # 连接到升压站
            power = power_flow[source]
        else:
            power = 0
        
        # 电缆选型
        # 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
        
        # 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] * length / 1000  # 电阻(Ω)
        cost = selected_spec[3] * 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,
            'power': power,
            'cable': cable
        })
        
        # 累计成本
        total_cost += cable['cost']
        
        # 计算I²R损耗 (简化版)
        loss = (cable['current'] ** 2) * cable['resistance'] * 3  # 三相
        total_loss += loss
    
    return {
        'total_cost': total_cost,
        'total_loss': total_loss,
        'num_connections': len(connections),
        'details': detailed_connections
    }

# 6.5 导出CAD图纸 (DXF格式)
def export_to_dxf(turbines, substation, connections_details, filename):
    """
    将设计方案导出为DXF格式 (CAD通用格式)
    :param connections_details: evaluate_design返回的'details'列表
    """
    import ezdxf
    from ezdxf.enums import TextEntityAlignment
    
    doc = ezdxf.new(dxfversion='R2010')
    msp = doc.modelspace()
    
    # 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
    
    # 2. 绘制升压站
    sx, sy = substation[0, 0], substation[0, 1]
    # 绘制一个矩形代表升压站
    size = 50
    msp.add_lwpolyline([(sx-size, sy-size), (sx+size, sy-size), 
                        (sx+size, sy+size), (sx-size, sy+size), (sx-size, sy-size)], 
                       close=True, dxfattribs={'layer': 'Substation'})
    msp.add_text("Substation", dxfattribs={'layer': 'Substation', 'height': 20}).set_placement((sx, sy+size+10), align=TextEntityAlignment.CENTER)
    
    # 3. 绘制风机
    for i, row in turbines.iterrows():
        tx, ty = row['x'], row['y']
        # 绘制圆形代表风机
        msp.add_circle((tx, ty), radius=15, dxfattribs={'layer': 'Turbines'})
        # 添加文字标签
        label_id = str(int(row['original_id']) if 'original_id' in row else int(row['id']))
        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
        # 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, 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
    :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, 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, 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
    mst_connections = design_with_mst(turbines, substation)
    mst_evaluation = evaluate_design(turbines, mst_connections, substation, cable_specs=cable_specs, 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(cable_specs=cable_specs)
    
    # 允许指定簇的数量，如果设置为 None 则自动计算
    if n_clusters_override is not None:
        n_clusters = n_clusters_override
        min_clusters_needed = int(np.ceil(total_power / max_cable_mw))
        print(f"使用手动指定的回路数(簇数): {n_clusters} (理论最小需求 {min_clusters_needed})")
    else:
        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")
    
    # 替换为带容量约束的扫描算法
    kmeans_connections, clustered_turbines = design_with_capacitated_sweep(turbines.copy(), substation, cable_specs=cable_specs)
    kmeans_evaluation = evaluate_design(turbines, kmeans_connections, substation, cable_specs=cable_specs, is_offshore=is_offshore, method_name="Capacitated Sweep")
    
    # 创建结果比较
    results = {
        'MST Method': mst_evaluation,
        'Capacitated Sweep': 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 (现在是 Capacitated Sweep)
    # 获取实际生成的簇数
    n_actual_clusters = clustered_turbines['cluster'].nunique()
    visualize_design(clustered_turbines, substation, kmeans_evaluation['details'], 
                    f"Capacitated Sweep ({n_actual_clusters} circuits) - {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']}")

    # Calculate additional metrics for K-means (Capacitated Sweep)
    print("\n===== K-Means (Capacitated Sweep) 详细统计 =====")
    
    # 1. 总回路数
    # The 'cluster' column in clustered_turbines holds the circuit ID.
    num_circuits = clustered_turbines['cluster'].nunique()
    print(f"总回路数: {num_circuits}")
    
    # 2. 每种型号电缆长度
    cable_lengths = defaultdict(float)
    max_current = 0.0
    
    for conn in kmeans_evaluation['details']:
        # Length
        section = conn['cable']['cross_section']
        length = conn['length']
        cable_lengths[section] += length
        
        # Max Current
        current = conn['cable']['current']
        if current > max_current:
            max_current = current
            
    print("每种型号电缆的长度:")
    for section in sorted(cable_lengths.keys()):
        print(f"  {section}mm²: {cable_lengths[section]:.2f} m")
        
    # 3. 最大支路电流
    print(f"最大支路电流: {max_current:.2f} A")
    
    return results

# 8. 执行比较
if __name__ == "__main__":
    # 解析命令行参数
    parser = argparse.ArgumentParser(description='Wind Farm Collector System Design')
    parser.add_argument('--clusters', type=int, help='Specify the number of clusters (circuits) manually', default=None)
    args = parser.parse_args()

    # 2. 读取 Excel 坐标数据 (如果存在)
    excel_path = 'coordinates2.xlsx'
    # 尝试从命令行参数获取文件路径 (可选扩展)
    # if len(sys.argv) > 1: excel_path = sys.argv[1]
    
    # 3. 运行比较
    # 如果本地没有excel文件，将自动回退到生成数据模式
    compare_design_methods(excel_path, n_clusters_override=args.clusters)