IJ-AI ISSN: 2252-8938
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection …. Adnan H. Tawafan 67
Table 1 The Conditions for These Simulations
Event Simulation conditions
Sending and receiving end capacitor bank operation
operation conditions: on and off Load levels: 30, 70 and 100 percent of the full load
Source voltage phase angle: 0, 180 Sending Capacitor operation: 2.1 and 4.2 MVar
Receiving Capacitor operation: 2.1 MVar Existing sending receiving capacitor: 0 and 2.1 MVar
Load operation on the feeder
operation conditions: three and single phase Load levels operation: 30-70, 70-100 percent of the full load
Source voltage phase angle: 0, 180 Existing sending receiving capacitor: 0, 2.1 and 4.2 MVar
Non-linear load operation on the feeder
operation conditions: three phase Load levels operation: 30-70, 70-100 percent of the full load
Source voltage phase angle: 0, 180 Existing sending receiving capacitor: 0, 2.1 and 4.2 MVar
Figure 4a shows that real measured HIF current waveform extracted from [18] and Figure 4b shows the simulated waveform current. The qualitative comparison indicates that there is a relatively good
correspondence between the real and simulation waveforms. 4.
THE PROPOSED ALGORITHM
The proposed algorithm includes three important parts: input data preparation, features generation and fault classification. Input data preparation part is described in simulation section. The rest parts are
detailed in the next section. Figure5 showed the Structure of the proposed algorithm
.
Figure. 5 Structure of the proposed algorithm
4.1 Features Generation
On the modeled distribution system, different operation conditions have been simulated by using PSCAD EMTDC. The simulated data then were transferred to MATLAB to complete the rest algorithm. The
main goal of algorithm is to discriminate between HIFs and other similar waveforms. In this algorithm, the current waveforms of distribution power are used only to extract the features
of HIFs, but not on voltage waveforms. The discrimination is based on the amplitude of fundamental and other harmonics current waveforms in the frequency domain. A Fast Fourier Transform FFT method is used
for feature extraction. The analysis is focused on current waveform which is obtained from the distribution power system feeder. In the frequency domain, odd harmonics, such as the 3rd, 5th, 7th and 9th, are
predominant also some even harmonics, such as 2nd, have significant amplitudes. However, the fundamental harmonic is decreased when the fault is occurred.
A normal current waveform, when a capacitor bank is present, appears no significant varies in phase current in the amplitude, the odd harmonics are predominant also even harmonics can be seen to some
extent. In this paper, various waveforms were obtained by changing different parameters. When all these waveforms were obtained, useful relevant data were used to find the features that were common to all and
can discriminate HIF from other signals. These investigations led us to define the following features.
The ratio of harmonics amplitude 2
nd
, 3
rd
, 5
th
, 7
th
, 9
th
and 11
th
to the fundamental harmonic amplitude.
Features Generation
HIF Yesno
Current Signals
6 cycle FFT window
Harmonic selector
ANFIS based
fault classificatio
n
Feeder
IJ-AI Vol. 1, No. 2, June 2012 : 68
a
c
e Figure 6. Signals of the Feeder:
capacitor bank; b, d: FFT for
1 2
1 I
I f
=
1 3
2 I
I f
=
1 5
3 I
I f
=
1 7
4 I
I f
=
1 9
5 I
I f
=
: 63–72
b
d
f : a, c: The typical signals of HIF fault current unde
for signals in a and b; and e, f : The typical signal of FFT
ISSN: 2252-8938
der linear load and with l of nonlinear load and its
4
5
6
7
8
IJ-AI ISSN: 2252-8938
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection …. Adnan H. Tawafan 69
1 11
6 I
I f
=
9 where I
2
,
3
,
5
,
7
,
9
and
11
represent the 2
nd
, 3
rd
, 5
th
, 7
th
, 9
th
and 11
th
harmonics amplitude of the signal respectively, I
1
represent the fundamental harmonic amplitude, and f
i
represent the extracted features. Gather all the extracted features to obtain one vector which represent the input data to training the
adaptive neural subtractive clustering fuzzy system. The typical signals of HIF fault current under linear load and with capacitor bank also signal of nonlinear current and their spectrum is shown in Figs. 6
a, b, c, d, e and f respectively.
4.2 Classification
