Wind Turbine Performance Analysis

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(321)

# Wind turbine performance data
n_samples = 180

wind_speed = np.random.uniform(3, 25, n_samples)  # m/s
air_density = np.random.uniform(1.1, 1.3, n_samples)  # kg/m³
power_output = 0.5 * air_density * np.pi * 50**2 * wind_speed**3 / 1e6  # MW (simplified)
power_output *= np.random.uniform(0.3, 0.5, n_samples)  # Efficiency factor
power_output = np.clip(power_output, 0, 5)  # Rated capacity

# Color by efficiency
efficiency = power_output / (wind_speed / 10)
efficiency_norm = (efficiency - efficiency.min()) / (efficiency.max() - efficiency.min())

fig = plt.figure(figsize=(10, 8), facecolor='#ffffff')
ax = fig.add_subplot(111, projection='3d', facecolor='#ffffff')

# Custom colormap from coral to cyan
colors = []
for e in efficiency_norm:
    r = int(245 - e * 206)
    g = int(39 + e * 172)
    b = int(108 + e * 137)
    colors.append(f'#{r:02x}{g:02x}{b:02x}')

ax.scatter(wind_speed, air_density, power_output, c=colors, s=60, 
           alpha=0.7, edgecolors='#374151', linewidths=0.3)

ax.set_xlabel('Wind Speed (m/s)', color='#1f2937', fontsize=10)
ax.set_ylabel('Air Density (kg/m³)', color='#1f2937', fontsize=10)
ax.set_zlabel('Power Output (MW)', color='#1f2937', fontsize=10)
ax.set_title('Wind Turbine Performance Analysis', color='#1f2937', fontsize=14, fontweight='bold', pad=20)

ax.tick_params(colors='#6b7280', labelsize=8)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('#e5e7eb')
ax.yaxis.pane.set_edgecolor('#e5e7eb')
ax.zaxis.pane.set_edgecolor('#e5e7eb')

ax.view_init(elev=20, azim=45)
plt.tight_layout()
plt.show()