This library helps lower the entry barrier for domain experts in the wind energy industry to leverage advanced analytics and methodologies developed in related scientific research.
Modules are implemented for different routine and non-routine analysis of time series data collected from operating wind turbines. Practicing wind engineers and analysts can build on fundamental modules as part of a larger project development.
Library installation
- Clone the github repo to have access to example notebooks and public data.
- Next, pip install the library
# Clone github repo
git clone https://github.com/abbey2017/wind-energy-analytics.git
# Pip install library
pip install scada-data-analysis
Current modules
- Iterative power curve filter (returns normal and abnormal datapoints)
- Expected power estimator (returns expected power based on operational data)
Usage of power curve filter
import pandas as pd
from scada_data_analysis.modules.power_curve_preprocessing import PowerCurveFiltering
# Load turbine scada data
df = pd.read_csv('path\to\data')
# Instantiate power curve filtering class
pc_filter = PowerCurveFiltering(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
power_label='P_avg', data=df, cut_in_speed=3, bin_interval=0.5,
z_coeff=2.5, filter_cycle=5, return_fig=True, image_path='..\images')
# Process raw scada data
normal_df, abnormal_df = pc_filter.process()
Usage of expected power estimator
import pandas as pd
from scada_data_analysis.modules.expected_power import ExpectedPower
# Load turbine scada data
df = pd.read_csv('path\to\data')
# Instantiate expected power estimator
power_model = ExpectedPower(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
power_label='P_avg', method='binning', kind='linear')
# Fit estimator based on training data
power_model = power_model.fit(train_df)
# Estimate expected power based on operation power curve
pred_df = power_model.predict(test_df)
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