An Improved Power Quality Disturbance Detection Using Deep Learning Approach

Table 10

Comparison with the existing methods.

Methods	Number of features selected	Number of PQDs	Accuracy in %
			Pure	40 dB	30 dB	20 dB
WPT and GA [18]	15	8	98.33	-	-	-
WT and PSO [19]	11	9	98	96.87	93.625	–
ST and RF [30]	4	15	99.7	99.9	99.7	95.9
FFT and ANNs [31]	–	8	–	93.95	95.65	–
ST and PNN [32]	4	11	97.4	-	-	-
FDST and DT [33]	20	13	99.28	98.8	97.49	–
Deep CNN [24]	AUTO	9	99.67	-	-	-
Deep CNN [25]	AUTO	16	99.96	99.95	99.66	98.13
Deep CNN [26]	AUTO	24	-	-	-	99.26
The proposed CNN + LSTM	AUTO	16	100	100	99.69	99.31