# filter only data for turbine x
for x in list1:
dfx = df[(df["turbine_id"] == x)].copy()
# copying fault to new column (mins) (fault when turbine category id is y)
for y in list2:
def f(c):
if c["TurbineCategory_id"] == y:
return 0
else:
return 1
dfx["mins"] = dfx.apply(f, axis=1)
# sort values by timestamp in descending order
dfx = dfx.sort_values(by="timestamp", ascending=False)
# reset index
dfx.reset_index(drop=True, inplace=True)
# assigning value to first cell if it's not 0 with a large number
if dfx.loc[0, "mins"] == 0:
dfx.set_value(0, "mins", 0)
else:
# to allow the following loop to work
dfx.set_value(0, "mins", 999999999)
# using previous value's row to evaluate time
for i, e in enumerate(dfx["mins"]):
if e == 1:
dfx.at[i, "mins"] = dfx.at[i - 1, "mins"] + 10
# sort in ascending order
dfx = dfx.sort_values(by="timestamp")
# reset index
dfx.reset_index(drop=True, inplace=True)
# convert to hours, then round to nearest hour
dfx["hours"] = dfx["mins"].astype(np.int64)
dfx["hours"] = dfx["hours"] / 60
# round to integer
dfx["hours"] = round(dfx["hours"]).astype(np.int64)
# > 48 hours - label as normal (999)
def f1(c):
if c["hours"] > 48:
return 999
else:
return c["hours"]
dfx["hours"] = dfx.apply(f1, axis=1)
# filter out curtailment - curtailed when turbine is pitching outside
# 0deg <= normal <= 3.5deg
def f2(c):
if (
0 <= c["pitch"] <= 3.5
or c["hours"] != 999
or (
(c["pitch"] > 3.5 or c["pitch"] < 0)
and (
c["ap_av"] <= (0.1 * dfx["ap_av"].max())
or c["ap_av"] >= (0.9 * dfx["ap_av"].max())
)
)
):
return "normal"
else:
return "curtailed"
dfx["curtailment"] = dfx.apply(f2, axis=1)
# filter unusual readings, i.e., for normal operation, power <= 0 in
# operating wind speeds, power > 100 before cut-in, runtime < 600 and
# other downtime categories
def f3(c):
if c["hours"] == 999 and (
(
3 < c["ws_av"] < 25
and (
c["ap_av"] <= 0
or c["runtime"] < 600
or c["EnvironmentalCategory_id"] > 1
or c["GridCategory_id"] > 1
or c["InfrastructureCategory_id"] > 1
or c["AvailabilityCategory_id"] == 2
or 12 <= c["TurbineCategory_id"] <= 15
or 21 <= c["TurbineCategory_id"] <= 22
)
)
or (c["ws_av"] < 3 and c["ap_av"] > 100)
):
return "unusual"
else:
return "normal"
dfx["unusual"] = dfx.apply(f3, axis=1)
# round to 6 hour intervals to reduce number of classes
def f4(c):
if 1 <= c["hours"] <= 6:
return 6
elif 7 <= c["hours"] <= 12:
return 12
elif 13 <= c["hours"] <= 18:
return 18
elif 19 <= c["hours"] <= 24:
return 24
elif 25 <= c["hours"] <= 30:
return 30
elif 31 <= c["hours"] <= 36:
return 36
elif 37 <= c["hours"] <= 42:
return 42
elif 43 <= c["hours"] <= 48:
return 48
else:
return c["hours"]
dfx["hours6"] = dfx.apply(f4, axis=1)
# change label for unusual and curtailed data (9999), if originally
# labelled as normal
def f5(c):
if c["unusual"] == "unusual" or c["curtailment"] == "curtailed":
return 9999
else:
return c["hours6"]
# apply to new column specific to each fault
dfx["hours_%s" % y] = dfx.apply(f5, axis=1)
# drop unnecessary columns
dfx = dfx.drop("hours6", axis=1)
dfx = dfx.drop("hours", axis=1)
dfx = dfx.drop("mins", axis=1)
dfx = dfx.drop("curtailment", axis=1)
dfx = dfx.drop("unusual", axis=1)
features = [
"ap_av",
"ws_av",
"wd_av",
"pitch",
"ap_max",
"ap_dev",
"reactive_power",
"rs_av",
"gen_sp",
"nac_pos",
]
# separate features from classes for classification
classes = [col for col in dfx.columns if "hours" in col]
# list of columns to copy into new df
list3 = features + classes + ["timestamp"]
df2 = dfx[list3].copy()
# drop NaNs
df2 = df2.dropna()
X = df2[features]
# normalise features to values b/w 0 and 1
X = preprocessing.normalize(X)
Y = df2[classes]
# convert from pd dataframe to np array
Y = Y.as_matrix()
criterion = ["gini", "entropy"]
scores = []
# cross validation using time series split
tscv = TimeSeriesSplit(n_splits=5)
# looping for each value of c and defining random forest classifier
for c in criterion:
rf = RandomForestClassifier(criterion=c, n_jobs=-1)
# empty list to hold score for each cross validation fold
p1 = []
# looping for each cross validation fold
for train_index, test_index in tscv.split(X):
# split train and test sets
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = Y[train_index], Y[test_index]
# fit to classifier and predict
rf1 = rf.fit(X_train, Y_train)
pred = rf1.predict(X_test)
# accuracy score
p2 = np.sum(np.equal(Y_test, pred)) / Y_test.size
# add to list
p1.append(p2)
# average score across all cross validation folds
p = sum(p1) / len(p1)
scores.append(p)
# changing to misclassification error
MSE = [1 - x for x in scores]
# determining best n
optimal = criterion[MSE.index(min(MSE))]
num.append(optimal)
err.append(min(MSE))