WTOAAM/wtoaamapi/apps/business/algorithm/cpWindSpeedAnalyst.py

import os
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import seaborn as sns
import matplotlib.pyplot as plt
from .analyst import Analyst
from .utils.directoryUtil import DirectoryUtil as dir
from confBusiness import ConfBusiness


class CpWindSpeedAnalyst(Analyst):
    """
    风电机组风能利用系数分析
    """

    def typeAnalyst(self):
        return "cp_windspeed"

    def turbineAnalysis(self,
                        dataFrame,
                        outputAnalysisDir,
                        outputFilePath,
                        confData: ConfBusiness,
                        turbineName):

        self.cp(dataFrame, outputFilePath,
                confData.field_wind_speed, confData.field_rotor_speed, confData.field_power, confData.field_pitch_angle1, confData.rotor_diameter, confData.density_air)

    def cp(self, dataFrame, output_path, wind_speed_col, field_rotor_speed, power_col, pitch_col, rotor_diameter, air_density):
        print('rotor_diameter={}  air_density={}'.format(
            rotor_diameter, air_density))

        dataFrame['power'] = dataFrame[power_col]  # Alias the power column
        # Floor division by 10 and then multiply by 10
        dataFrame['wind_speed_floor'] = (dataFrame[wind_speed_col] / 1).astype(int) + 0.5
        dataFrame['wind_speed'] = dataFrame[wind_speed_col].astype(float)
        dataFrame['rotor_speed'] = dataFrame[field_rotor_speed].astype(float)

        # Power coefficient calculation
        dataFrame['power'] = pd.to_numeric(dataFrame['power'], errors='coerce')
        dataFrame['wind_speed'] = pd.to_numeric(
            dataFrame['wind_speed'], errors='coerce')
        rotor_diameter = pd.to_numeric(rotor_diameter, errors='coerce')
        air_density = pd.to_numeric(air_density, errors='coerce')
        print('rotor_diameter={}  air_density={}'.format(
            rotor_diameter, air_density))
        # Calculate cp
        dataFrame['cp'] = dataFrame['power'] * 1000 / \
            (0.5 * np.pi * air_density *
             (rotor_diameter ** 2) / 4 * dataFrame['wind_speed'] ** 3)

        # Group by wind_speed_floor and calculate mean, max, and min of the specified columns
        grouped = dataFrame.groupby('wind_speed_floor').agg(
            power=('power', 'mean'),
            rotor_speed=('rotor_speed', 'mean'),
            cp=('cp', 'mean'),
            cp_max=('cp', 'max'),
            cp_min=('cp', 'min'),
        ).reset_index()

        # Rename columns post aggregation for clarity
        grouped.columns = ['wind_speed_floor', 'power',
                           'rotor_speed', 'cp', 'cp_max', 'cp_min']

        # Sort by wind_speed_floor
        grouped = grouped.sort_values('wind_speed_floor')

        # Write the dataframe to a CSV file
        grouped.to_csv(output_path, index=False)

    def turbinesAnalysis(self, dataFrameMerge, outputAnalysisDir, confData: ConfBusiness):
        self.generate_cp_distribution(outputAnalysisDir, confData.farm_name)

    def generate_cp_distribution(self, csvFileDirOfCp, farm_name, encoding='utf-8'):
        """
        Generates Cp distribution plots for turbines in a wind farm.

        Parameters:
        - csvFileDirOfCp: str, path to the directory containing input CSV files.
        - farm_name: str, name of the wind farm.
        - encoding: str, encoding of the input CSV files. Defaults to 'utf-8'.
        """

        output_path = csvFileDirOfCp

        field_Name_Turbine = "turbine_name"
        x_name = 'wind_speed_floor'
        y_name = 'cp'
        split_way = '_cp_windspeed.csv'

        sns.set_palette('deep')
        res = pd.DataFrame()

        for root, dir_names, file_names in dir.list_directory(csvFileDirOfCp):
            for file_name in file_names:

                if not file_name.endswith(".csv"):
                    continue

                file_path = os.path.join(root, file_name)
                print(file_path)
                frame = pd.read_csv(file_path, encoding=encoding)
                turbine_name = file_name.split(split_way)[0]
                frame[field_Name_Turbine] = turbine_name

                res = pd.concat(
                    [res, frame.loc[:, [field_Name_Turbine, x_name, y_name]]], axis=0)

        ress = res.reset_index()

        fig, ax = plt.subplots(figsize=(16, 8))
        ax = sns.lineplot(x=x_name, y=y_name, data=ress,
                          hue=field_Name_Turbine)
        ax.set_title('Cp-Distribution')
        plt.legend(ncol=4)
        plt.savefig(os.path.join(
            output_path, "{}-Cp-Distribution.png".format(farm_name)), bbox_inches='tight', dpi=300)
        plt.close()

        grouped = ress.groupby(field_Name_Turbine)
        for name, group in grouped:
            color = ["lightgrey"] * len(ress[field_Name_Turbine].unique())
            fig, ax = plt.subplots(figsize=(8, 8))
            ax = sns.lineplot(x=x_name, y=y_name, data=ress, hue=field_Name_Turbine,
                              palette=sns.color_palette(color), legend=False)
            ax = sns.lineplot(x=x_name, y=y_name, data=group,
                              color='darkblue', legend=False)
            ax.set_title('turbine name={}'.format(name))
            plt.savefig(os.path.join(output_path, "{}.png".format(
                name)), bbox_inches='tight', dpi=120)
            plt.close()

    def plot_cp_distribution(self, csvFileDir,  farm_name):
        field_Name_Turbine = "设备名"
        x_name = 'wind_speed_floor'
        y_name = 'cp'
        split_way = '_cp.csv'
        # Create the output path based on the farm name
        output_path = csvFileDir  # output_path_template.format(farm_name)
        # Ensure the output directory exists
        os.makedirs(output_path, exist_ok=True)
        print(csvFileDir)
        # Initialize a DataFrame to store results
        res = pd.DataFrame()

        # Walk through the input directory to process each file
        for root, _, file_names in dir.list_directory(csvFileDir):
            for file_name in file_names:
                full_path = os.path.join(root, file_name)
                frame = pd.read_csv(full_path, encoding='gbk')
                turbine_name = file_name.split(split_way)[0]
                print("turbine_name={}".format(turbine_name))
                frame[field_Name_Turbine] = turbine_name
                res = pd.concat(
                    [res, frame.loc[:, [field_Name_Turbine, x_name, y_name]]], axis=0)

        # Reset index for plotting
        ress = res.reset_index(drop=True)

        # Plot combined Cp distribution for all turbines
        fig = make_subplots(rows=1, cols=1)
        for name, group in ress.groupby(field_Name_Turbine):
            fig.add_trace(go.Scatter(
                x=group[x_name], y=group[y_name], mode='lines', name=name))

        fig.update_layout(title_text='{} Cp分布'.format(
            farm_name), xaxis_title=x_name, yaxis_title=y_name)
        fig.write_image(os.path.join(
            output_path, "{}Cp分布.png".format(farm_name)), scale=3)

        # Plot individual Cp distributions
        unique_turbines = ress[field_Name_Turbine].unique()
        for name in unique_turbines:
            individual_fig = make_subplots(rows=1, cols=1)
            # Add all turbines in grey
            for turbine in unique_turbines:
                group = ress[ress[field_Name_Turbine] == turbine]
                individual_fig.add_trace(go.Scatter(
                    x=group[x_name], y=group[y_name], mode='lines', name=turbine, line=dict(color='lightgrey')))

            # Highlight the current turbine in dark blue
            group = ress[ress[field_Name_Turbine] == name]
            individual_fig.add_trace(go.Scatter(
                x=group[x_name], y=group[y_name], mode='lines', name=name, line=dict(color='darkblue')))

            individual_fig.update_layout(title_text='设备名={}'.format(name))
            individual_fig.write_image(os.path.join(
                output_path, "all-{}.png".format(name)), scale=2)