A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

Table 2

Training data base structure.

	1	2	3	4	5	6	7	8	Output				Samples
Fa_n
1° window	−0.000641867	−3.89083E − 05	−0.001196415	−0.000115795	0.001838282	0.000154703	−1.28535E − 16	2.94942E − 19	0	0	0	0	1
	0.973501524	−0.076403259	−0.341666999	0.038533053	−0.631834526	0.037870206	−6.91105E − 14	1.37198E − 15	0	0	0	0	2
	0.156566273	0.002870909	−0.922627797	0.062640947	0.766061524	−0.065511856	3.39279E − 14	8.94746E − 17	0	0	0	0	3
	−0.977512891	0.075485554	0.364054716	−0.043150787	0.613458175	−0.032334767	6.83136E − 14	−1.3745E − 15	0	0	0	0	4
	−0.130876088	−0.005686783	0.913497316	−0.064549946	−0.782621228	0.070236728	−3.57852E − 14	−5.31128E − 17	0	0	0	0	5
	0.980884519	−0.076176694	−0.388121653	0.041806383	−0.592762866	0.034370311	−6.73465E − 14	1.37569E − 15	0	0	0	0	6
	0.105208833	0.006836513	−0.903372829	0.06041348	0.798163996	−0.067249992	3.75514E − 14	1.70883E − 17	0	0	0	0	7
	−0.983615903	0.075157898	0.4118215	−0.046419404	0.571794403	−0.028738494	6.63519E − 14	−1.37607E − 15	0	0	0	0	8
2° window	−0.085390516	−0.009294317	0.893416538	−0.062237336	−0.812232671	0.071861669	−3.93077E − 14	0.000694649	1	0	0	1	9
	0.794482606	0.244448121	−0.41313357	0.051890314	−0.528477208	0.037597989	−6.87857E − 14	0.435579785	1	0	0	1	10
	0.07857321	0.608565667	−0.883929869	0.070899477	0.824708251	−0.055939163	4.14844E − 14	0.812910497	1	0	0	1	11
	−0.780159859	0.298755448	0.434207722	−0.044746113	0.504871283	−0.020256067	6.79103E − 14	0.304993572	1	0	0	1	12
	−0.043255913	−0.097347222	0.870970136	−0.061615551	−0.839513012	0.071455966	−4.29452E − 14	−0.113640457	1	0	0	1	13
	0.795320502	0.185590131	−0.458668486	0.053684383	−0.484590465	0.032579109	−6.65731E − 14	0.354537359	1	0	0	1	14
	0.035436192	0.539746239	−0.860491626	0.066775976	0.850674399	−0.058884817	4.49033E − 14	0.713893895	1	0	0	1	15
	−0.784244924	0.2154572	0.479820124	−0.049547695	0.460982222	−0.018303638	6.56191E − 14	0.192631481	1	0	0	1	16
	−0.003700022	−0.159990825	0.846601051	−0.060247995	−0.864070832	0.071439863	−4.64465E − 14	−0.193629071	1	0	0	1	17
	0.794671736	0.146470088	−0.503122432	0.055762758	−0.439532497	0.027911125	−6.42076E − 14	0.300094087	1	0	0	1	18
	−0.007413499	0.488236623	−0.834598405	0.062873078	0.874412279	−0.061256088	4.82739E − 14	0.638497717	1	0	0	1	19
	−0.785563286	0.149009345	0.523946543	−0.053838314	0.415652817	−0.015916624	6.31095E − 14	0.103483911	1	0	0	1	20
3° window	0.036283926	−0.206004065	0.819911852	−0.05837136	−0.886263953	0.071582108	−4.979E − 14	−0.251041408	0	0	0	0	21
	0.989358754	−0.071865898	−0.570113161	0.053793186	−0.417184944	0.019281513	−5.86319E − 14	0.002047061	0	0	0	0	22
	−0.100636605	0.022449661	−0.801635165	0.04975652	0.900541685	−0.072491386	5.0136E − 14	1.41371E − 06	0	0	0	0	23
	−0.982484998	0.071828401	0.589385182	−0.058290945	0.39193272	−0.013785476	5.68487E − 14	−2.15631E − 07	0	0	0	0	24