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 4 Comparison of Data Mining Algorithms with 7 attributes for evaluation on training set

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	31,436	99.9936	2	0.0064	3.26
		Swells	22,506	100	0	0
		Interruptions	5441	100	0	0
		Unbalances	14,263	99.9650	5	0.0350
		No PQ problems	326,348	100	0	0
		Overall	399,994	99.9983	7	0.0017
2	Random Tree	Sags	31,438	100	0	0	1.88
		Swells	22,506	100	0	0
		Interruptions	5441	100	0	0
		Unbalances	14,268	100	0	0
		No PQ problems	326,348	100	0	0
		Overall	400,001	100	0	0
3	Random Forest	Sags	31,438	100	0	0	22.58
		Swells	22,506	100	0	0
		Interruptions	5441	100	0	0
		Unbalances	14,268	100	0	0
		No PQ problems	326,348	100	0	0
		Overall	400,001	100	0	0