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 # 设置matplotlib支持中文显示 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial'] plt.rcParams['axes.unicode_minus'] = False # 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 Excel格式要求包含列: Type (Turbine/Substation), ID, X, Y, Power """ try: df = pd.read_excel(file_path) # 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)) }) print(f"成功加载: {len(turbines)} 台风机, {len(substation)} 座升压站") return turbines, substation 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 # 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): """ 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） """ # 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 # 电缆规格库: (截面mm², 载流量A, 电阻Ω/km, 基准价格元/m) cable_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), # 增加大截面适应海上大功率 (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) # 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 max_power_w = np.sqrt(3) * VOLTAGE_LEVEL * max_current * POWER_FACTOR return max_power_w / 1e6 # MW # 5. 计算集电线路方案成本 def evaluate_design(turbines, connections, substation, is_offshore=False): """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 max_power = max(power_flow.values()) if power_flow else 0 print(f"DEBUG: 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 cable = select_cable(power, length, is_offshore=is_offshore) # 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()