Table 3 Comparison of various ML algorithm under fault Cases.

From: Effectiveness of supervised machine learning models for electrical fault detection in solar PV systems

ML Algorithms

Classification

Precision

Recall

F1-score

Accuracy (%)

RF

GF

1

1

1

97.20

MF

0.90

0.96

0.93

Normal

0.96

0.90

0.93

OC

1

1

1

SC

1

1

1

SVM

GF

1

1

1

97.40

MF

0.90

0.97

0.93

Normal

0.97

0.90

0.93

OC

1

1

1

SC

1

1

1

DT

GF

1

1

1

97.20

MF

0.90

0.96

0.93

Normal

0.96

0.90

0.93

OC

1

1

1

SC

1

1

1

Naïve Bayes

GF

1

1

1

97.60

MF

0.91

0.97

0.94

Normal

0.97

0.91

0.94

OC

1

1

1

SC

1

1

1

XGBoost

GF

1

1

1

98.00

MF

0.93

0.97

0.95

Normal

0.97

0.93

0.95

OC

1

1

1

SC

1

1

1

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