Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy

Protection and Control of Modern Power Systems

Table 5 Comparison of Data Mining Algorithms with 7 attributes for stratified 10-fold cross-validation

S. No.	Algorithm	Cases Tested	Correct Classification		Incorrect Classification		Training Time (s)
S. No.	Algorithm	Cases Tested	No. of Samples	Accuracy (%)	No. of Samples	Inaccuracy (%)	Training Time (s)
1	J48	Sags	31,424	99.9554	14	0.0445	3.68
		Swells	22,506	100	0	0
		Interruptions	5440	99.9816	1	0.0184
		Unbalances	14,252	99.8878	16	0.1122
		No PQ problems	326,348	100	0	0
		Overall	399,970	99.9923	31	0.0077
2	Random Tree	Sags	31,429	99.9713	9	0.0287	1.75
		Swells	22,506	100	0	0
		Interruptions	5441	100	0	0
		Unbalances	14,258	99.9299	10	0.0701
		No PQ problems	326,347	99.9997	1	0.0003
		Overall	399,981	99.995	20	0.005
3	Random Forest	Sags	31,434	99.9872	4	0.0128	21.25
		Swells	22,506	100	0	0
		Interruptions	5441	100	0	0
		Unbalances	14,262	99.9579	6	0.0421
		No PQ problems	326,347	99.9997	1	0.0003
		Overall	399,990	99.9973	11	0.0027