Downtime categories¶

In [1]:

# import libraries
import pandas as pd
import matplotlib.pyplot as plt

In [2]:

# configure plot styles
plt.style.use("seaborn-colorblind")
plt.rcParams["font.family"] = "Source Sans Pro"
plt.rcParams["figure.dpi"] = 96
plt.rcParams["axes.grid"] = False
plt.rcParams["figure.titleweight"] = "semibold"
plt.rcParams["axes.titleweight"] = "semibold"
plt.rcParams["figure.titlesize"] = "13"
plt.rcParams["axes.titlesize"] = "12"
plt.rcParams["axes.labelsize"] = "10"

In [3]:

data_cat = pd.read_csv("data/processed/downtime_categories.csv")
data_ts = pd.read_csv(
    "data/processed/downtime_timeseries.csv",
    lineterminator="\n",
    parse_dates=["timestamp_start", "timestamp_end"]
)

In [4]:

data_ts["period"] = data_ts["timestamp_end"] - data_ts["timestamp_start"]
data_ts["period"] = data_ts["period"].dt.total_seconds() / (60 * 60)

In [5]:

data = pd.DataFrame(data_ts.groupby(["TurbineCategory_id"]).count()["id"])
data.rename(columns={"id": "frequency"}, inplace=True)
data["Category"] = data.index

In [6]:

data["period"] = data_ts.groupby(["TurbineCategory_id"]).sum()["period"]

In [7]:

data["frequency/t/y"] = data["frequency"] / (25 * 2.5)
data["period/t/y"] = data["period"] / (25 * 2.5)

In [8]:

data_cat = data_cat[data_cat["Type"] == "Turbine"]
data_cat = data_cat.drop(columns=["Type"])

In [9]:

data = pd.concat([data, data_cat], axis=1)
data.fillna(0, inplace=True)
data.sort_values(by="frequency", inplace=True)

In [10]:

data

Out[10]:

	frequency	Category	period	frequency/t/y	period/t/y	Category	Name
17	0.0	0.0	0.000000	0.000	0.000000	17	Retrofit
0	0.0	0.0	0.000000	0.000	0.000000	0	Unknown
14	0.0	0.0	0.000000	0.000	0.000000	14	Noise Constraints
21	1.0	21.0	3.560278	0.016	0.056964	21	Delayed Startup
4	9.0	4.0	5.441111	0.144	0.087058	4	Main Shaft
12	11.0	12.0	0.245278	0.176	0.003924	12	Unlogged manual stop
19	58.0	19.0	542.960833	0.928	8.687373	19	Hub
16	214.0	16.0	408.461667	3.424	6.535387	16	Tower
9	227.0	9.0	493.191667	3.632	7.891067	9	Hydraulics
3	256.0	3.0	596.369167	4.096	9.541907	3	Rotor Brake
22	329.0	22.0	810.376389	5.264	12.966022	22	Other
2	359.0	2.0	1030.276667	5.744	16.484427	2	Anemometry
18	478.0	18.0	178.107778	7.648	2.849724	18	Cable Unwind
13	553.0	13.0	9833.588056	8.848	157.337409	13	Customer Stop
7	614.0	7.0	980.310833	9.824	15.684973	7	Yaw System
20	686.0	20.0	5978.126389	10.976	95.650022	20	Rotor Blades
6	866.0	6.0	5249.156667	13.856	83.986507	6	Generator
5	1122.0	5.0	12545.892222	17.952	200.734276	5	Gearbox
15	1968.0	15.0	1561.361944	31.488	24.981791	15	Scheduled Maintenance
8	2042.0	8.0	8063.895000	32.672	129.022320	8	Electrical Controls
10	3182.0	10.0	8457.707500	50.912	135.323320	10	Electrical System
1	3560.0	1.0	8635.941111	56.960	138.175058	1	OK
11	5790.0	11.0	20375.991944	92.640	326.015871	11	Pitch Control

In [11]:

fig, ax = plt.subplots(ncols=2, sharey=True, figsize=(9, 5))
data.plot.barh("Name", "frequency", ax=ax[0], color="C0", width=.8)
data.plot.barh("Name", "period", ax=ax[1], color="C2", width=.8)
ax[0].set_ylabel("Turbine category")
ax[0].set_xlabel("Frequency")
ax[1].set_xlabel("Period (hours)")
ax[0].get_legend().remove()
ax[1].get_legend().remove()
fig.tight_layout()
plt.minorticks_off()
plt.show()

In [12]:

fig, ax = plt.subplots(ncols=2, sharey=True, figsize=(9, 5))
data.plot.barh("Name", "frequency/t/y", ax=ax[0], color="C1", width=.8)
data.plot.barh("Name", "period/t/y", ax=ax[1], color="C3", width=.8)
ax[0].set_ylabel("Turbine category")
ax[0].set_xlabel("Frequency per turbine per year")
ax[1].set_xlabel("Period (hours) per turbine per year")
ax[0].get_legend().remove()
ax[1].get_legend().remove()
fig.tight_layout()
plt.minorticks_off()
plt.show()