Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances

Table 10

Complete performance indices of all algorithms.

PQD class	Precision				Recall				F-score				Accuracy				Best algorithm
	KNN	SVM	DT	RF	KNN	SVM	DT	RF	KNN	SVM	DT	RF	KNN	SVM	DT	RF
C1	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	100	100	100	100	All
C2	0.93	0.86	0.98	1.00	0.92	0.95	0.98	1.00	0.92	0.90	0.98	1.00	92.0	95.3	97.9	100	RF
C3	0.98	1.00	1.00	1.00	0.98	1.00	1.00	1.00	0.98	1.00	1.00	1.00	98.1	100	100	100	DT and RF
C4	0.94	0.98	0.99	1.00	0.93	0.96	0.99	1.00	0.93	0.97	0.99	1.00	93.0	96.0	99.1	100	RF
C5	0.90	0.97	0.95	0.96	0.87	0.93	0.93	0.98	0.88	0.95	0.94	0.97	86.8	92.8	92.8	97.9	RF
C6	0.95	1.00	0.98	1.00	0.96	0.98	0.97	1.00	0.95	0.99	0.97	1.00	96.0	98.0	96.8	100	RF
C7	0.92	0.99	1.00	1.00	0.91	0.96	1.00	1.00	0.91	0.97	1.00	1.00	90.9	96.1	100	100	DT and RF
C8	0.87	1.00	1.00	1.00	0.92	1.00	1.00	1.00	0.89	1.00	1.00	1.00	91.9	100	100	100	SVM, DT, and RF
C9	0.96	0.88	0.96	0.98	0.95	0.92	0.96	0.96	0.95	0.90	0.96	0.97	94.9	92.0	96.0	96.0	DT and RF
C10	0.89	0.91	1.00	1.00	0.90	0.97	0.99	1.00	0.89	0.94	0.99	1.00	90.0	96.9	98.7	100	RF
C11	0.92	0.83	0.96	0.96	0.96	0.91	0.98	0.97	0.94	0.87	0.97	0.96	96.0	90.5	98.0	96.8	DT
C12	0.93	0.92	0.93	0.97	0.89	0.90	0.97	0.95	0.90	0.90	0.95	0.96	89.0	90.9	96.9	94.8	DT
C13	0.96	0.95	1.00	1.00	0.95	0.94	0.99	1.00	0.95	0.94	0.99	1.00	95.0	94.2	98.9	100	RF
C14	0.92	0.94	0.97	1.00	0.94	0.85	0.98	1.00	0.93	0.89	0.97	1.00	94.1	84.9	98.1	100	RF
C15	0.90	0.91	0.96	1.00	0.88	0.87	0.95	0.98	0.89	0.89	0.95	0.99	87.9	86.9	95.0	98.0	RF
C16	0.94	1.00	1.00	1.00	0.98	1.00	100	1.00	0.96	1.00	1.00	1.00	97.2	100	100	100	SVM, DT, and RF
C17	0.95	1.00	1.00	0.97	0.96	0.98	0.99	1.00	0.95	0.98	0.99	0.98	95.2	97.7	99.4	99.7	RF