A Novel Approach to Adaptive Single Phase
Autoreclosure Scheme for EHV Power
Transmission Lines Based on Learning Error
Function of ADALINE
Behrooz Vahidi
Mohsen Jannati
Seyed Hossein Hosseinian
Department of Electrical Engineering
Amirkabir University of Technology
No. 424, Hafez Avenue
Tehran 158754413, Iran
vahidi@aut.ac.ir
In modern interconnected power systems, almost 70–90% of faults in high voltage Power Trans-
mission Lines (PTLs) are intrinsically transient. The necessity of rapid fault clearing results in fast
developing of protection equipments. Moreover, need for reliable supplying of loads, lead to improve-
ments in single-phase autoreclosure (SPAR) equipments. An ADAptive LInear NEuron (ADALINE)
is suitable for important applications such as protection of power systems and digital relays. In this
paper, a novel simple adaptive SPAR algorithm is introduced. This algorithm is based on learning
error function of an ADALINE. It can be distinguished by fault type (transient fault or a permanent
fault), and if the fault is permanent, autoreclosure should be blocked. This leads to improve the
performance and efficiency of SPAR. Electromagnetic transients program-based simulation results
show that the autoreclosure scheme based on learning error function of ADALINE on a typical 400
kV circuit for various system and fault conditions improves the reliability of fault discrimination.
Keywords: power transmission line, transient and permanent fault, dead time, ADALINE
1. Introduction pecially in PTLs located near generators [3–6]. Therefore,
over the last decade, further research into SPAR equip-
Around 70–90% of faults on most overhead Power Trans- ment has been conducted.
mission Lines (PTLs) are transient. The most prevalent This paper presents an Adaptive Single-Phase Autore-
potential of transient faults is lightning, PTLs switching closure (ASPAR) algorithm for high voltage PTL which
and jiff contact by external objects. Transient faults can be proposes the opportunity of controlling autoreclosure us-
cleared by a temporary opening of the faulted phase. So, ing a computer in the station. Correct discrimination be-
these faults are allowed to be self-cleared. For this type tween transient and permanent faults is necessary to solve
of fault, transmission systems can be energized by Single- this problem. Different solutions are proposed for this
Phase Autoreclosure (SPAR). This strategy improves reli- problem [7–10]. Most of them are based on analysis of
ability and stability of power systems [1, 2]. However, per- voltage waveform at the sending or receiving end of the
manent faults, for example scrapping wires, are not clear- PTL in the ‘Dead Time’ (the period when the fault hap-
able by SPAR and can incur system and utility damage. pens). In [7], an on-line SPAR is presented. Type of fault
The conventional autoreclosure is not recommended es- (transient or permanent) is detected by wavelet transform
and by processing transient voltage waveform. In [8], a
SPAR algorithm is introduced. The proposed scheme is
based on monitoring the fundamental component of the
SIMULATION, Vol. 84, Issue 12, December 2008 601–610 zero sequence instantaneous power to detect the instant
1
c 2008 The Society for Modeling and Simulation International of secondary arc quenching. Design of a reclosing relay
DOI: 10.1177/0037549708097293 based on the proposed method in [8] is sophisticated be-
Volume 84, Number 12 SIMULATION 601
, Vahidi, Jannati, and Hosseinian
1
N
34t5 2 Adc e36t 4 Vn sin4n7t 4 8 n 59 (1)
n21
In Equation (1), Adc e36t is the transient DC component,
6 is decaying coefficient, Vn and 8 n respectively is ampli-
tude and the phase of the nth harmonic, N is total number
Figure 1. The under study power transmission line of harmonics and 7 is the fundamental frequency and as-
sumed to be constant. So, signal 34t5 can be expressed in
discrete form as below [11]:
cause of the complexity of this approach. Another algo- 1
N
rithm is presented in [9] to lock the reclosing equipment 34k5 2 Adc 41 3 6kTs 5 4 An sin n7t 4k5
on permanent fault. This algorithm is essentially based on n21
fundamental and third harmonic components of voltage
and current waveforms. Detection of the location of the 1
N
fault in a 110 kV power system is also considered. In [10], 4 Bn cos n7t 4k59 (2)
by processing the input terminal voltage and Total Har- n21
monic Distortion (THD) criterion, proper protective ac-
tion for transient or permanent faults is completed. In Equation (2), the term Adc 41 3 6kTs 5 represents the
To optimize the ASPAR of PTLs, a new simple ap- first two terms of the Taylor series expansion of the decay-
proach is presented where, at first, voltage waveform at ing DC component, Ts is 2 7Ns , Ns is sampling period,
the sending end of the PTL is analyzed during the fault. An is Vn cos 8 n , Bn is Vn sin 8 n and t 4k5 is kth sampling
Then, ADALINE trained on-line. After that a new yard- time. To extract the fundamental and harmonic compo-
stick based on learning error function of ADALINE is nents from34k5, the ADALINE input vector,X 4k5, is cho-
applied to discriminate between transient and permanent sen to be:
faults, and if the fault is transient, to detect the time of 2 5
quenching of the secondary arc. This approach is simple, sin 7t 4k5 cos 7t 4k5 sin 27t 4k5 T
3 6
accurate and reliable. Simulation performed in EMTP en- X 4k5 2 4 cos 27t 4k5 999 sin n7t 4k5 7 9 (3)
vironment validates the effectiveness and accuracy of the
presented algorithm. cos n7t 4k5 1 3 kTs
In the training process, desire output yd 4k5 is assumed
2. Simulated Power Transmission System to be equal to the actual signal, therefore, the W 4k5 vec-
tor, weight vector of ADALINE, is Fourier transform co-
Figure 1 demonstrates the single line diagram of a 400 kV efficients of input signal. Weighting factors are selected to
and 300 km PTL simulated in the ATP/EMTP program. minimize the difference between output of ADALINE and
The PTL parameters are given as: positive and zero se- the reference signal. The Widrow–Hoff learning rule [12],
quence resistance, inductance and capacitance are R1 = an equation based on Least Square Error (LSE) minimiza-
0.01526 1/km, L1 = 0.8838 mH/km, C1 = 0.0126 2F/km, tion, is used for training. Weighting factors correction is
R0 = 0.04624 1/km, L0 = 2.6563 mH/km, and C0 = 0.0043 based on Equation (4):
2F/km, respectively. Thévenin’s equivalent impedances at e4k5X 4k5
buses A and B are described using mutual coupled R–L W 4k 4 15 2 W 4k5 4 9 (4)
circuit as: the positive sequence is R1 = 0.06 1 and L1 = X T 4k5X 4k5
40.03 mH, and the zero sequence is R0 = 0.127 1 and L0 = In this equation, W 4k5 is weighting factor’s vector in kth
23.56 mH. The distributed line parameter model of EMTP sampling time, W 4k 4 15 is weighting factor’s vector in
is intentionally selected to account for the unsymmetrical (k 4 1)th sampling time, X 4k5 is the input vector in kth
faults. sampling time, e4k5 is training rate and is error cor-
rection coefficient. Perfect tracking is attained when the
3. ADALINE Architecture tracking error e4k5 is brought to zero. So, we have:
y4k5 2 yd 4k5 2 34k5 2 W0T X 4k5 (5)
An ADALINE as shown in Figure 2 is an n-input single-
output neural network whose output is a linear combina- where W0 is the weighting factor vector after the conver-
tion of its inputs. An ADALINE can be used for on-line gence of error to zero and equals to:
following of harmonic content of a signal. To illustrate the
problem, a signal which has some harmonics is considered
below: W0 2 [A1 B1 A2 B2 999 An Bn Adc 6 Adc ] 9 (6)
602 SIMULATION Volume 84, Number 12
