Wind Turbine Anaylsis for whole year.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import os \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statistics\n",
    "import sort_dataframeby_monthorweek\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date/Time</th>\n",
       "      <th>LV ActivePower (kW)</th>\n",
       "      <th>Wind Speed (m/s)</th>\n",
       "      <th>Theoretical_Power_Curve (KWh)</th>\n",
       "      <th>Wind Direction (°)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01 01 2018 00:00</td>\n",
       "      <td>380.047791</td>\n",
       "      <td>5.311336</td>\n",
       "      <td>416.328908</td>\n",
       "      <td>259.994904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01 01 2018 00:10</td>\n",
       "      <td>453.769196</td>\n",
       "      <td>5.672167</td>\n",
       "      <td>519.917511</td>\n",
       "      <td>268.641113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01 01 2018 00:20</td>\n",
       "      <td>306.376587</td>\n",
       "      <td>5.216037</td>\n",
       "      <td>390.900016</td>\n",
       "      <td>272.564789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01 01 2018 00:30</td>\n",
       "      <td>419.645905</td>\n",
       "      <td>5.659674</td>\n",
       "      <td>516.127569</td>\n",
       "      <td>271.258087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01 01 2018 00:40</td>\n",
       "      <td>380.650696</td>\n",
       "      <td>5.577941</td>\n",
       "      <td>491.702972</td>\n",
       "      <td>265.674286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date/Time  LV ActivePower (kW)  Wind Speed (m/s)  \\\n",
       "0  01 01 2018 00:00           380.047791          5.311336   \n",
       "1  01 01 2018 00:10           453.769196          5.672167   \n",
       "2  01 01 2018 00:20           306.376587          5.216037   \n",
       "3  01 01 2018 00:30           419.645905          5.659674   \n",
       "4  01 01 2018 00:40           380.650696          5.577941   \n",
       "\n",
       "   Theoretical_Power_Curve (KWh)  Wind Direction (°)  \n",
       "0                     416.328908          259.994904  \n",
       "1                     519.917511          268.641113  \n",
       "2                     390.900016          272.564789  \n",
       "3                     516.127569          271.258087  \n",
       "4                     491.702972          265.674286  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turbine_df = pd.read_csv(\"T1.csv\")\n",
    "turbine_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "for i in range(4):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    sns.kdeplot(turbine_df.iloc[:,i+1], shade=True)\n",
    "    plt.title(turbine_df.columns[i+1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming the column name (can be easily used in data manipulation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50530 entries, 0 to 50529\n",
      "Data columns (total 6 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Time                           50530 non-null  object \n",
      " 1   LV ActivePower (kW)            50530 non-null  float64\n",
      " 2   Wind Speed (m/s)               50530 non-null  float64\n",
      " 3   Theoretical_Power_Curve (KWh)  50530 non-null  float64\n",
      " 4   Wind Direction (°)             50530 non-null  float64\n",
      " 5   Month                          50530 non-null  object \n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "turbine_df.info()\n",
    "turbine_df.rename(columns={'Date/Time':'Time'},inplace=True)\n",
    "turbine_df.rename(columns={'LV ActivePower (kW)':'Active_Power'},inplace=True)\n",
    "turbine_df.rename(columns={'Wind Speed (m/s)':'Wind_Speed'},inplace=True)\n",
    "turbine_df.rename(columns={'Theoretical_Power_Curve (KWh)':'Theoretical_Power'},inplace=True)\n",
    "turbine_df.rename(columns={'Wind Direction (°)':'Wind_Direction'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Correlation with Cumulative Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Active_Power  Theoretical_Power  Wind_Speed  Wind_Direction\n",
      "Active_Power           1.000000           0.949918    0.912774       -0.062702\n",
      "Theoretical_Power      0.949918           1.000000    0.944209       -0.099076\n",
      "Wind_Speed             0.912774           0.944209    1.000000       -0.077188\n",
      "Wind_Direction        -0.062702          -0.099076   -0.077188        1.000000\n",
      "Cumulative Variance :  [ 97.26561937  99.76275115  99.99994754 100.        ]\n"
     ]
    }
   ],
   "source": [
    "corr_df = turbine_df[['Active_Power','Theoretical_Power','Wind_Speed','Wind_Direction']]\n",
    "corr_matrix = corr_df.corr()\n",
    "print(corr_matrix)\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "my_model = PCA(n_components=4)\n",
    "my_model.fit_transform(corr_df.values)\n",
    "\n",
    "print (\"Cumulative Variance : \", my_model.explained_variance_ratio_.cumsum()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Month and adding into existing data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function for finding months\n",
    "def find_month(x):\n",
    "    if \" 01 \" in x:\n",
    "        return \"Jan\"\n",
    "    elif \" 02 \" in x:\n",
    "        return \"Feb\"\n",
    "    elif \" 03 \" in x:\n",
    "        return \"March\"    \n",
    "    elif \" 04 \" in x:\n",
    "        return \"April\"    \n",
    "    elif \" 05 \" in x:\n",
    "        return \"May\"    \n",
    "    elif \" 06 \" in x:\n",
    "        return \"June\"    \n",
    "    elif \" 07 \" in x:\n",
    "        return \"July\"    \n",
    "    elif \" 08 \" in x:\n",
    "        return \"August\"    \n",
    "    elif \" 09 \" in x:\n",
    "        return \"Sep\"    \n",
    "    elif \" 10 \" in x:\n",
    "        return \"Oct\"    \n",
    "    elif \" 11 \" in x:\n",
    "        return \"Nov\"    \n",
    "    else:\n",
    "        return \"Dec\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Active_Power</th>\n",
       "      <th>Wind_Speed</th>\n",
       "      <th>Theoretical_Power</th>\n",
       "      <th>Wind_Direction</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01 01 2018 00:00</td>\n",
       "      <td>380.047791</td>\n",
       "      <td>5.311336</td>\n",
       "      <td>416.328908</td>\n",
       "      <td>259.994904</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01 01 2018 00:10</td>\n",
       "      <td>453.769196</td>\n",
       "      <td>5.672167</td>\n",
       "      <td>519.917511</td>\n",
       "      <td>268.641113</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01 01 2018 00:20</td>\n",
       "      <td>306.376587</td>\n",
       "      <td>5.216037</td>\n",
       "      <td>390.900016</td>\n",
       "      <td>272.564789</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01 01 2018 00:30</td>\n",
       "      <td>419.645905</td>\n",
       "      <td>5.659674</td>\n",
       "      <td>516.127569</td>\n",
       "      <td>271.258087</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01 01 2018 00:40</td>\n",
       "      <td>380.650696</td>\n",
       "      <td>5.577941</td>\n",
       "      <td>491.702972</td>\n",
       "      <td>265.674286</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time  Active_Power  Wind_Speed  Theoretical_Power  \\\n",
       "0  01 01 2018 00:00    380.047791    5.311336         416.328908   \n",
       "1  01 01 2018 00:10    453.769196    5.672167         519.917511   \n",
       "2  01 01 2018 00:20    306.376587    5.216037         390.900016   \n",
       "3  01 01 2018 00:30    419.645905    5.659674         516.127569   \n",
       "4  01 01 2018 00:40    380.650696    5.577941         491.702972   \n",
       "\n",
       "   Wind_Direction Month  \n",
       "0      259.994904   Jan  \n",
       "1      268.641113   Jan  \n",
       "2      272.564789   Jan  \n",
       "3      271.258087   Jan  \n",
       "4      265.674286   Jan  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add months\n",
    "turbine_df['Month']=turbine_df.Time.apply(find_month)\n",
    "turbine_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning and Removing unncessary columns from Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Active_Power</th>\n",
       "      <th>Theoretical_Power</th>\n",
       "      <th>Month</th>\n",
       "      <th>Loss_Value</th>\n",
       "      <th>Loss(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>380.05</td>\n",
       "      <td>416.33</td>\n",
       "      <td>Jan</td>\n",
       "      <td>36.28</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453.77</td>\n",
       "      <td>519.92</td>\n",
       "      <td>Jan</td>\n",
       "      <td>66.15</td>\n",
       "      <td>12.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>306.38</td>\n",
       "      <td>390.90</td>\n",
       "      <td>Jan</td>\n",
       "      <td>84.52</td>\n",
       "      <td>21.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>419.65</td>\n",
       "      <td>516.13</td>\n",
       "      <td>Jan</td>\n",
       "      <td>96.48</td>\n",
       "      <td>18.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>380.65</td>\n",
       "      <td>491.70</td>\n",
       "      <td>Jan</td>\n",
       "      <td>111.05</td>\n",
       "      <td>22.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Active_Power  Theoretical_Power Month  Loss_Value  Loss(%)\n",
       "0        380.05             416.33   Jan       36.28     8.71\n",
       "1        453.77             519.92   Jan       66.15    12.72\n",
       "2        306.38             390.90   Jan       84.52    21.62\n",
       "3        419.65             516.13   Jan       96.48    18.69\n",
       "4        380.65             491.70   Jan      111.05    22.59"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del turbine_df['Wind_Speed']\n",
    "del turbine_df['Wind_Direction']\n",
    "\n",
    "df_clean = turbine_df.sort_values(\"Time\")\n",
    "df_clean[\"Loss_Value\"]=df_clean[\"Theoretical_Power\"]-df_clean[\"Active_Power\"]\n",
    "df_clean[\"Loss(%)\"]=df_clean[\"Loss_Value\"]/df_clean[\"Theoretical_Power\"]*100\n",
    "#round the values to 2 digit.\n",
    "df_clean=df_clean.round({'Active_Power': 2, 'Theoretical_Power': 2,'Loss_Value': 2, 'Loss(%)': 2})\n",
    "del df_clean['Time']\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregating the data on the basis of month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Active_Power              Theoretical_Power               Loss_Value  \\\n",
      "               mean          std              mean          std        mean   \n",
      "Month                                                                         \n",
      "April    824.358562  1193.657141        955.690151  1282.557024  131.331561   \n",
      "August  1978.188843  1183.497237       2220.579144  1229.762474  242.390325   \n",
      "Dec     1176.785858  1374.223195       1424.063515  1395.610556  247.277693   \n",
      "Feb     1503.354965  1427.411851       1712.709995  1412.944515  209.354998   \n",
      "Jan     1323.158004  1416.651750       1847.287949  1412.677834  524.130003   \n",
      "July     477.014243   623.399179        583.709290   751.687601  106.695018   \n",
      "June     995.490335  1090.078636       1139.291322  1205.494103  143.801008   \n",
      "March   1952.405772  1461.628665       2075.819816  1473.178956  123.414087   \n",
      "May      836.941964   967.624627        960.203574  1073.393726  123.261589   \n",
      "Nov     1886.693866  1269.787719       2030.027650  1293.609706  143.333934   \n",
      "Oct     1408.274933  1180.918575       1534.185256  1248.802183  125.910326   \n",
      "Sep     1429.484700  1343.052157       1553.319597  1401.183572  123.834978   \n",
      "\n",
      "                   Loss(%)      \n",
      "               std    mean std  \n",
      "Month                           \n",
      "April   331.352406    -inf NaN  \n",
      "August  316.269602    -inf NaN  \n",
      "Dec     578.523249     NaN NaN  \n",
      "Feb     571.027437     NaN NaN  \n",
      "Jan     978.977414     NaN NaN  \n",
      "July    150.476378     NaN NaN  \n",
      "June    187.714971     NaN NaN  \n",
      "March   293.865198     NaN NaN  \n",
      "May     171.033663     NaN NaN  \n",
      "Nov     286.595511    -inf NaN  \n",
      "Oct     156.754207     NaN NaN  \n",
      "Sep     158.297927     NaN NaN  \n"
     ]
    }
   ],
   "source": [
    "df_rank = df_clean.groupby('Month').agg(['mean', 'std'])\n",
    "print(df_rank)\n",
    "df_month = df_clean.groupby('Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Mean data of Active Power and Theoretical Power in separate DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_active = df_month['Active_Power'].mean()\n",
    "df_theo = df_month['Theoretical_Power'].mean()\n",
    "## Saving Data as Backup\n",
    "df_active.to_csv('active_mean.csv')\n",
    "df_theo.to_csv('theoretical_power_mean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construction of Control Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_active = pd.read_csv(\"active_mean.csv\")\n",
    "df_theo = pd.read_csv(\"theoretical_power_mean.csv\")\n",
    "## Sorting data on the basis of Months\n",
    "df_active = sort_dataframeby_monthorweek.Sort_Dataframeby_Month(df=df_active,monthcolumnname='Month')\n",
    "df_theo = sort_dataframeby_monthorweek.Sort_Dataframeby_Month(df=df_theo,monthcolumnname='Month')\n",
    "\n",
    "## X data Series for both Active and Theoretical\n",
    "x_active = pd.Series(df_active['Active_Power'])\n",
    "x_theo = pd.Series(df_theo['Theoretical_Power'])\n",
    "months = df_active['Month'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define list variable for moving ranges\n",
    "MR = [np.nan]\n",
    "\n",
    "# Get and append moving ranges\n",
    "i = 1\n",
    "for data in range(1, len(x_theo)):\n",
    "    MR.append(abs(x_theo[i] - x_theo[i-1]))\n",
    "    i += 1\n",
    "\n",
    "# Convert list to pandas Series objects    \n",
    "MR = pd.Series(MR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate mR Series with and rename columns\n",
    "data_active = pd.concat([x_active,MR], axis=1).rename(columns={'Active_Power':\"x\", 0:\"mR\"})\n",
    "data_theo = pd.concat([x_theo,MR], axis=1).rename(columns={'Theoretical_Power':\"x\", 0:\"mR\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Control Limits are calculated from Theoretical Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Control Limits : \n",
      "CL For individual chart : 1503.0739381568362\n",
      "UCL For individual chart: 2901.060135664016\n",
      "LCL For individual chart: 105.08774064965655\n",
      "---------------------------------------------------\n",
      "CL For mR chart : 1503.0739381568362\n",
      "UCL For mR chart: 2847.40542540369\n",
      "LCL For mR chart: 158.74245090998215\n"
     ]
    }
   ],
   "source": [
    "# Control Limits For individual chart \n",
    "print(\"Control Limits : \")\n",
    "CL = data_theo['x'].mean()\n",
    "UCL = data_theo['x'].mean() + 3*(data_theo['mR'].mean())/1.128\n",
    "LCL = data_theo['x'].mean() - 3*(data_theo['mR'].mean())/1.128\n",
    "print(f'CL For individual chart : {CL}')\n",
    "print(f'UCL For individual chart: {UCL}')\n",
    "print(f'LCL For individual chart: {LCL}')\n",
    "\n",
    "print(\"---------------------------------------------------\")\n",
    "# Control Limits For mR chart\n",
    "mR_CL = data_theo['x'].mean()\n",
    "mR_UCL = data_theo['x'].mean() + 3*(data_theo['mR'].mean())*0.8525\n",
    "mR_LCL = data_theo['x'].mean() - 3*(data_theo['mR'].mean())*0.8525\n",
    "print(f'CL For mR chart : {mR_CL}')\n",
    "print(f'UCL For mR chart: {mR_UCL}')\n",
    "print(f'LCL For mR chart: {mR_LCL}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Active power points are plotted with Theoretical Power Control Limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Months'), Text(0, 0.5, 'Active Power')]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot x and mR charts\n",
    "fig, axs = plt.subplots(2, figsize=(15,15))\n",
    "\n",
    "# x chart\n",
    "axs[0].plot(data_active['x'], linestyle='-', marker='o', color='black')\n",
    "axs[0].axhline(CL, color='blue')\n",
    "axs[0].axhline(UCL, color = 'red', linestyle = 'dashed')\n",
    "axs[0].axhline(LCL, color = 'red', linestyle = 'dashed')\n",
    "axs[0].set_title('Individual Chart')\n",
    "axs[0].set_xticks(range(0,len(months)))\n",
    "axs[0].set_xticklabels(months)\n",
    "axs[0].set(xlabel='Months', ylabel='Active Power')\n",
    "\n",
    "# mR chart\n",
    "axs[1].plot(data_active['mR'], linestyle='-', marker='o', color='black')\n",
    "axs[1].axhline(mR_CL), color='blue')\n",
    "axs[1].axhline(mR_UCL, color='red', linestyle ='dashed')\n",
    "axs[1].axhline(mR_LCL, color='red', linestyle ='dashed')\n",
    "axs[1].set_ylim(bottom=0)\n",
    "axs[1].set_title('mR Chart')\n",
    "axs[1].set_xticks(range(0,len(months)))\n",
    "axs[1].set_xticklabels(months)\n",
    "axs[1].set(xlabel='Months', ylabel='Active Power')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validating the points out of control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All points within Theoretical Power control limits.\n",
      "All points within Theoretical Power control limits.\n"
     ]
    }
   ],
   "source": [
    "# x chart\n",
    "i = 0\n",
    "control = True\n",
    "for unit in data_active['x']:\n",
    "    if unit > statistics.mean(data_theo['x'])+3*statistics.mean(data_theo['mR'][1:len(data_theo['mR'])])/1.128 or unit < statistics.mean(data_theo['x'])-3*statistics.mean(data_theo['mR'][1:len(data_theo['mR'])])/1.128:\n",
    "        print('Unit', i, 'out of cotrol limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within Theoretical Power control limits.')\n",
    "\n",
    "# mR chart\n",
    "i = 0\n",
    "control = True\n",
    "for unit in data_active['mR']:\n",
    "    if unit > statistics.mean(data_theo['mR'][1:len(data_theo['mR'])])+3*statistics.mean(data_theo['mR'][1:len(data_theo['mR'])])*0.8525 or unit < statistics.mean(data_theo['mR'][1:len(data_theo['mR'])])-3*statistics.mean(data_theo['mR'][1:len(data_theo['mR'])])*0.8525:\n",
    "        print('Unit', i, 'out of control limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within Theoretical Power control limits.')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}