APPLICATION OF ARTIFICIAL NEURAL NETWORK I N PROTECTIVE RELAYING OF TRANSMISSION LINES
S.A. Khaparde,
P.B. Kale
Electrical Engineeri'ng Department
I n d i a n I n s t i t u t e of Technology
Bombay-400076, I N D I A
S.H. Agarwal
T e s t i n g Department
T a t a Electric Company
Trombay, Bombay, I N D I A
That the A r t i f i c i a l Neutel Netwcork (Awl) can pi3o.m
the pattern classification i n excellent f a s h i m is
already established i n the literature. Here, we
envisage the relay as a pattern classifying device.
ThFsopensan~~dimensioninrelayphFlosophywhich
d wi& investigatiaw. Keeping themirelay framKx3c intact, this peper repzts the
findings about the feasibility of using Awl in
pratection of tranmlission lins. "
I
moddl is
explored f o r the application and is fand to yield
-aging
results.
The inprt variables are
quantified over the opera*
range which eases the
arithmetics of the
.-im
The traiaing is
performed i n off-line mode and the camrged weight
matrix is stceced f o r on-line use.
-,
this
huable heginning has cope f a a lot of further
refinemnts which are rrnder investigatia.
1. IFmCUXmm
The techniques available f o r transmission line
pmtection as inp1i n the
analog
and static relays include, directlcnal UlverSBctkne
over current, d i r e c t i d distance, p i l o t -on
w i t h a ammdcaticm channel betuem te"lS.
-,
in the area of d i g i t a l protech'on of
transmission lines, distance relaying principle has
received m n s i i k a b l e attentim. DigiW distance
relaysmeasuretheinpedancebatvreenarelaylocatim
and the fault point, and thus determFne i f a fault is
internalorBRernaltoap"
zone.
The
hpedmce is defined a t -tal
frequency.
Qnrent and voltage signals used as inplts d
l y
txntain the f " e n t a l culpn@nts of interrst and
other signals of a wide f " c y
qy=t" which may
be terraed as noise "
l
'a).Durllrgeachstmpling
period, the signal sargles i n the data w i d o w are
-by
filter and then Befinetheimpedance
to arrive at the t r i p signal. several algorithm are
t r i p decision f r u a
available to extract the
signals minimizing the decision time [l,21.
the -le
These algorithm include mier algorithn with full
or partial cycle, Kahm filter etc.
trim
The-im
distance relays claim some
new and unlqlm capabllitiea 121.
cunnunication
circuits provide local and "te access to settings,
fault recards and other infarmation. Data prucessing
f o r plrposes ather than p"ztim8
Like f a u l t
locating, event
*
etc.c%nbeincl~.
This paper is an atto explore haw A r t i f i c i a l
m a l Network (ANN) can be m
i
e
d to realize a
distance relay in its sinplest form.
framecnrk of
themi-relay is retamed here. we
envisage a relay to be a 'pattern c l a s s i f e r ' and
pmvideaA"lrPdelwh.i.chcanperf'ormthistkskinan
excellent manner. = s e c t u n
describes H i c i a l
Neural "As.
sincethemotivaticnofprpsentwmkisbesedonthe
f a c t that I\Ewmodel can be used as a pattan classifier
andarelaycanbeviewedtobepatterncLassifk, it
d d be wmthrtu'le to review the Awl mdel s h i c h
caters to this task. The sinplemodel-byB.
Widrav [SI, A[lALIEIE, is describ~~I
here.
o"E) -tially
of
adaptive
threshold
bgiC d
m which can be
used in trainable pattern recogniti.cn systen. Aaaption
is effected byIMsorrewardand-algtxithn.
Awptive LmEX
91TH0374-9/91/0000-0122$01.~1991
IEEE
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In its sinplest form AwLINE can be shorm to r e a l i z e
linearly separable functicns.
figure 1 shavs basic building block of
weighted
are
t
Wak
I
WUC,
...
by
set
a
of
4.
Since a relay can be looked upon as a pattern
classifier, the
which essentially does the
sane task can replace the cmnentl'aral relay.
The
input quantities are voltage and current values.
Since the aim of the present work is to establish.the
f e a s i b i l i t y of the pmpcsed ccmcept we have e
the fra"rk of the micxqmcessor relay and lwdware
aspects to their SLrplest farm. Figure 2 shows the
schematic diaqran of t h e micmyxessar based neural
relay. The m w set of wights which are worked
i n off-line rmde are ttxs stored in the micruprocessor
for on-line awlication.
m.
coefficient
$
.
mc4TIcN
=
T
,Wml , the elaaent produces an analog
~,theinnerprcductYk~<y(.Thebiasweight
Wok is cavlected to a oo~lstentinput xo = +1 and it
c"1s
t h e threshold level. Decisicns are made by a
2-level quantizer.
The quantizaticn of a l l the i n p t vector is prcpxed
which is suitable far micmprocessar framwork
described i n next section.
Input
patt-
ern
+
yk
*lk0
Analog
output
quantize each inplt almg its axis fran "
I
to
Itlilxi" value and the n m b r of quanta w i l l depend
upm the resolution of the A/D m e . This
facilitates the redwt.~
'on of the input vector to be
descritized i n finite nunber of points.
such
discretized input can be readily processed nunerically
by the mimqrocessor. we assign a binary string to
each value of quantun. The input vedar is formed by
a x l c a t i n g t h e c o d * e s ~on each axis.
All
quanta form a code m a t r i x which if f u l l rank has
'2k0
'3k0
d e f i n i t e omvergeme. Chosen ccde shculd be linearly
independent. (xrtofthe codes dis~ussedin ref. [6],
we have chcsen 01 contrast here for our a@icatim.
I l l u s t r a t i o n of the coding is given i n next section.
Illustratim: Cansider a relay w i t h 2 inplts, voltage and current with their ranges 0-5 V and 0-1 A
respectively.
xet both of t h e n be quantized i n 5
parts each. Then the inpxt space would look like as
si"
i n Figure 3a.
Fig. 1
Desired response input
(training signal)
I& us use 01 contml coding for the case, then the
coding matrix is as sham i n Figure 3b. For input v =
2.5 volts and I = 0.3 A ,
we have input vedar
[ O O ~ U O I . U ~ J ~ . In this case, we have 10weights, one
to each b i t i n the input vector. Let
the d g h t vector be [+1 -1 2 3 0 2-3 1 0 -11.
We get
the following e a c t e r i s t i c s for givencase (fig. 4).
response' is a special input signal used
to train t h e neuron. An e o n algorithm adjusts
ueights so that the outplt
to the
gatterns w i l l be as clase as posslble to ttmr
msFective desired "sp0"Ses.
In other wxds the
mdel can realize a l m e a r l y separable logic functicn
d c h is a separating plane between the output
patterns which divides t h e output space having
different patterns.
Ihe 'desired
4.2
Aproposed schenatic of Neural Relay is as shown i n
figure 2. As shown in the Figure, the relay takes the
inputs fran the pmer system, a c h are passed thrcugh
surge f i l t e r s to zaaove high frecperq sut-gq. he
wider, for two-dunens
*
ionalcase,
Y = x1 w1 + % W 2 + W o = 0
(1)
w1
%=-3
- i;s;
*l
B g a a t i m ( 2 ) r e p r e s e n t s a line in
*
i d
plane. 'ihe r e p r e s s t a t i o n w i l l produce outplts w i t h
qposite signs on either si& of the line. This
sqgests that a caabina+ion
of
or netwark of
muons may be used to realize &inear
separating
PLaneS r71.
-Aspects
sigmls are then caditimed to make measuranents.
quantities are sanpled using -le
and hold
circuit and corresponiing d i g i t a l values are abtained
by using analog to d i g i t a l cm-.
lhese d i g i t a l
values are fed to a miQ9ccrrFpltet. A b i t pattern
coarespading to every input value is f
d by
pattern enccdfr, using pmper coding schele.
The
input vector thus formed is fed to the AIXUJNE and
weighted sun is obtained, which is passed througfi hard
limiter functim. "?E "p"t of t h i s pmduces the
binary-to t r i p (+1) or no t r i p
(-1) signal. m i s signal is fed back to power system
The
123
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.
-------
ANALOG INPUTS(V, I -,PHASE
- - - - -etc.)
-
---T-
--RELAY
--l- SETTINGS
OUTPUT
- - - - - -DIGITAL
--
f-----
FILTER
SIH
CIRCUIT
5
-
'
-L, PATTERN ----c WEIGHTED'
AID
CONVERTER
I
ENCODER
+ Tripping zone
1.0
- No trip zone
I
0.8
4
SUMMER
0.6
I 3 0.4
2
1
t
v
0.2
2
(0,O)1
Quntum no. -PI
2
4
3
3
v
Fig. 4
5
4
Fig. 3a
1
1
1
1
1
1
0
1
1
0
0
1
0
0
0
4.3
~ A s p e c t 8
Fig.3b
124
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= presentweight
= presentoutplt
d
= desiredoutplt
x(k) = present inplt
W(k)
y
carparable w i t h canventional relays even though the
wallel processing is not implmted. The decision
space is V-I plane rather than R-X plane. T h e d a y
setting can be changed by changing weights and time
&lay settings can also be achieved.
softwamaagani zatial:
sanplenet : To generate the training and sanple
Set
rain net
Toolnet
4.4
:
+-
train the V using above
generated training sanple s e t
%e a w l i c a t i o n is yet i n the develqing stages leaving
many refinanents. The adaptive relaying [8] can be
a&eved
using layered d e l where additicnal inpa
l i k e direction of current and approximate value of
fault resistance w x l d change the operating characteristics of the relay.
-
: To experiment on the weight matrix,
and observe effect of a transformation
of weight matrix on the - a m i S t i c s realized.
phadke and J.S. n'lorp, canplter m Y
Fmer Systems, John Wiley and Sons, 1988.
A.G.
Observations
IE
"rial
b fa
~aurseon "Microprocessor Relays and
protection Systems," NO. 88, M 0269-1-RiR.
~Ulcui.ng factors were studied and sane osrsenmtions
were made as f o l l m :
P.D. Wasserman,
CaFPlling, 'Iheory and
Practice," Van Nostrand Reinhold, 1989.
Placewnt of training sample: This is a critical
fador i n training since it f a r m basis f o r the
training.
R. x , i v ,
Introductim to C m ~ ~ i nw igt h
Neural ~ e t s , " Assp Magazine, April 1987.
order of presentation of training samples: I f t h e
sanre training set is presented to a ADAtINE i n
different order of training sanples, then they may
B. Widrcw, R.G.
Winter and R.A. Baxter, ''h-
-
realize s l i g h t l y different characteristics.
order of quantization:
As the quantization is
immased, m ~ l r e91130th surfaces can be realised.
s a . y a m d , R. subramaniani "Optimal codbes far
Leanung Control~systenS",Fifth N a t i d SystanS
COnferenoe,
India, 1971.
Copingschenes:
Anunberofcodingschemeswere
Identity coding and 21 contrast code did
not prcduce encowaging results, while 01 contrast
gave satisfadary cutputs.
tried.
F.W. anith,"A Trainable Nonlinear Function Generatnr",
IEm, Trans. of Autanatic ccntrol, vol. 11, No. 2,
pp 212-218, A p r i l ~ 1 6 6 .
superposition of regions: A new weight vector can
be farmed by adding 2 weight m a t r i c e s i n different
proport5ms. mis may be useful i n cutting daun
undesirable part of a regial etc.
A.G.
Phadke,. S.H.
U t Z ,
"pdaptive e y i n g , "
IEEE ccnprter Agplications i n Paver, Vol. 3, No. 3
PP 47-51, J d Y 1990.
Using a different concept of sun of r u m (SRI and
of colurns (sc), we can explain n?latialship of
sane of the above factors on the characbxistics
realized. W - S R and sc o~~cepts
imrolves basically
contrikrtion of SUR of weights along that axis f o r
t h a t axis for that quantun. Fbr any cell, the analog
output is fcnmd by adding this sua for the c*aresponding quanta along every axis. Hence the dichotany alarg
any quantun i s depedent only ~1 the sun a t that
quantun. l h i s can be used far shifting the characteristic etc. Also a non-closed curve can be gtaerated
using this cc(Icept [Ref. 71.
SUR
i
As expcted t h e pmposed AaAcINE model is able to
.locate the cperating paint correctly i n the &cision
space. This ccnfirms feasible use of W as relay.
"he pmposed schaae considers two inputs viz. voltage
andcurrentvalueS. Theschemecanbeextendedto
three inplt variables to include the phase angle,
it can be realized for
of cross-cheduq.
The suggested quantificatmn of mputs is suitable for
micraprocessor f r a " k
W weight matrix,
calculated i n off-line mDde is stared in "y. ltle
on-line time tfor arriving a t decision is
-.
125
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