In the paper the results of investigations on the application of Artificial Intelligence (AI) methods to the out-ofstep (OS) protection of synchronous machines are presented. The main efforts have been focused on development of new Artificial Neural Network (ANN) based and mixed protection techniques which should ensure better (faster and more secure) detection of generator loss of synchronism. The designed AI-based OS protection schemes have been thoroughly optimised and tested with EMTP-generated case signals. Wide robustness analysis as well as comparison with chosen standard OS detection method have also been performed

Feser, K

Rebizant, W

English

Repozytorium Eny Politechnika Wrocławska

OUT-OF-STEP PROTECTION WITH AI METHODS
W Rebizant, K Feser
Wroclaw University of Technology, Poland
University of Stuttgart, Germany
ABSTRACT
In the paper the results of investigations on the applica-
tion of Artificial Intelligence (AI) methods to the out-of-
step (OS) protection of synchronous machines are pre-
sented. The main efforts have been focused on devel-
opment of new Artificial Neural Network (ANN) based
and mixed protection techniques which should ensure
better (faster and more secure) detection of generator
loss of synchronism. The designed AI-based OS protec-
tion schemes have been thoroughly optimised and tested
with EMTP-generated case signals. Wide robustness
analysis as well as comparison with chosen standard OS
detection method have also been performed.
INTRODUCTION
The out-of-step conditions (loss of synchronism) of a
synchronous machine may occur as a result of loss of
excitation or during pole slipping. Both effects may on
the one hand threaten power system stability and on the
other hand cause severe mechanical and thermal stresses
to the generator itself. Thus appropriate protection
schemes are to be applied to avoid the threats men-
tioned.
The loss of excitation protection guards against the con-
sequences for a generator of a partial or complete failure
of the excitation. An underimpedance relay is used to
recognise this event. In case of small generating units
other protection schemes including power factor relays
or reverse power relays are recommended, Elmore (1).
The pole slipping protection also detects loss of syn-
chronism, but with the excitation intact. This condition
can arise after a long power system fault or when a tie
line between two systems is opened. The parameter su-
pervised to detect pole slipping is the impedance vector
measured at the machine terminals. Crossing of the im-
pedance vector trajectory with properly set characteris-
tic on the impedance plane is checked to detect the pole
slipping, (1). The other methods used for OS protection
are based on the equal area criterion, Centeno et al (2),
direct method of Liapunov, Roemish and Wall (3) or
rate of change of apparent resistance augmentation,
Taylor (4). With a communication channel available, an
OS protection system may use observations of phase
differences between substations, Ohura et al (5). It must
be said that all the above methods allow to detect the
OS conditions not before the first slip actually occurred.
Since the commonly used methods of loss of excitation
and pole slipping protection are not always fast and se-
cure enough, it is justified to search for new solutions
applying AI approach. Hence, appropriate relaying pro-
cedures have been considered and adequate ANN and
ANN-Fuzzy solutions have been developed and tested.
ANN-BASED OS PROTECTION SCHEME
Among various AI techniques the ANNs have become
most frequently used modern data processing / decision
algorithms to solve numerous complex engineering
problems. Thanks to their well-known advantages (e.g.
possibility of learning from examples, generalisation
ability, parallel computation etc.) the number of ANN
applications grows continuously covering also the field
of power system protection, Neibur (6).
Below an approach towards new neural OS protection is
described and discussed. The general scheme of the de-
veloped ANN-based scheme is shown in Fig. 1. The de-
cision part of the protection is realised with help of an
ANN performing typical pattern recognition with ap-
propriately chosen vector of criterion signal samples.
The decision (criterion) values have to be previously
calculated from available power system signals with use
of dedicated digital processing algorithms.
While designing the ANN classification unit for the OS
protection scheme the following important issues have
been taken into account:
Analog filtering
A/D Conversion
Digital filtering
Calculation of
criterion values
ANN
Comparison
with threshold
Power system signals
OS detection
Figure 1: Neural OS protection arrangement
• ANN structure choice (structure type, number of
layers and neurones, neurone activation functions),
• ANN training (training algorithm, initial values of
weights and biases),
• ANN inputs (signals and their measurement).
The choice of adequate (carrying highest amount of in-
formation) decision signals for the ANN feeding has
been made on basis of the EMTP/ATP simulations de-
scribed in the next section.
Simulation data
To investigate the proposed ANN-based OS protection
the following simple single machine power system has
been modelled (Fig. 2, parameters may be found in Re-
bizant (7)). The synchronous machine G1 is connected
to the infinite bus system S1 via the block transformer
T1 and a 200 km long double-circuit line L1 on which
different fault events are assumed to occur. The simula-
tion model included also generator voltage and speed
controllers as well as the elements of analogue pre-
processing (VTs, CTs, anti-aliasing filters etc.). Within
the transmission line L1 various three-phase faults on
one of the line circuits were applied. A total of 108
short-circuit cases were simulated with the following
parameters being varied:
• fault duration time (80, 100, 120 ms),
• fault termination (self-extinction, switching-off the
faulty circuit, switching-off + autoreclosing),
• machine loading (0.1 ... 1.1 times rated power).
Generator output voltages and currents as well as its an-
gular speed were registered in EMTP output files. Ad-
ditional feature signals like voltage/current amplitudes,
components of generator power, impedance vector (seen
from machine terminals) were obtained after digital
processing of phase voltages and currents. The meas-
urement algorithms utilised orthogonal components of
voltage and current signals obtained after their filtration
with use of pairs of full-cycle orthogonal Fourier filters.
Optimal selection of the decision signals for ANN
feeding has been done with help of statistical measures
of distance between calculated dynamic conditional
probability density functions (PDFs) of considered sig-
nals, (7). The mean and maximum values of chosen in-
dices (∆1 – difference of PDF expected values, ∆2 – dif-
ference of PDF standard deviations) taken over 1.44 sec
interval after fault clearance are gathered in Table 1.
F
L1S1 T1 (2,3) G1 (2,3)
Figure 2: Fragment of power system modelled
TABLE 1 - PDF distance indices
Decision signalPDF
distance ∆ω Arg(Z) P Q R X
mean (∆1) 0.31 0.20 0.14 0.24 0.07 0.04
max (∆1) 0.66 0.60 0.22 0.39 0.10 0.11
mean (∆2) 0.07 0.07 0.02 0.04 0.02 0.03
max (∆2) 0.10 0.10 0.03 0.06 0.07 0.08
Ranking 1 2 5a 3 5b 4
The analysis of obtained statistics allowed to order the
decision signals according to their relative recognition
strength (the greater distance coefficients, the higher
ranking position was assigned). In the investigated case
the most valuable recognition feature turned out to be
the angular frequency deviation, followed by the imped-
ance angle and reactive power measured at the machine
terminals. The statistics for the remaining signals do not
promise good classification abilities.
ANN design and scheme assessment
In the design phase of the OS protection scheme numer-
ous three-layer non-linear feed-forward neural networks
(Multilayer Perceptron type) have been examined. The
ANNs were trained to produce output equal to 0 for sta-
ble patterns and 1 for OS conditions. For classification
purpose a threshold value set to 0.5 was introduced. All
the cases for which the ANN output was lower than 0.5
were classified as stable and those for which the thresh-
old was exceeded were recognised as OS cases.
ANNs of various sizes (3-3-1 ... 30-30-1 neurones) and
12 different numbers of input signal samples (data win-
dow length, DWL, from 0.12 to 1.44 sec) have been
trained and tested with use of MATLAB programme.
Six different input features were considered: ∆ω,
Arg(Z), P, Q, R and X. The signals P and Q were stan-
dardised through their division by the machine rated
power in order to avoid convergence problems while
training and simultaneously to make the scheme inde-
pendent of the machine size. From a total of 720 net-
work configurations examined the optimal ANN struc-
ture having 9-9-1 neurones was found.
0 250 500 750 1000 1250 1500
0
5
10
15
20
t [ms]
Cl
as
. e
rro
rs
 [%
]
Arg(Z)
P
X
R
Q
∆ω
Figure 3: OS detection/prediction errors
Fig.3 shows the classification results (average over
various ANN sizes) for all considered ANN input sig-
nals. The results obtained confirmed our previous ex-
pectations as to the recognition abilities assessed with
use of statistical indices. The best classification effi-
ciency was achieved for ANNs fed with ∆ω signal - less
than 1% of errors for 0.48 sec long data window and no
false recognition at 0.8 sec after fault clearance.
The developed ANN based OS scheme has been sub-
jected to extensive tests and robustness checking with
patterns obtained from further EMTP simulations. First,
72 cases of single and double phase faults within line L1
(Fig. 1) were prepared, some of them being responsible
for OS conditions after short-circuit switching-off. It
turned out that the neural OS detector designed could
properly recognise all new unsymmetrical fault testing
cases. The other group of 72 testing cases was prepared
by simulating various symmetrical faults for two other
synchronous machines (G2, G3 – JG2,3=0.4, 0.8 JG1). It
was found that the robustness degree of trained ANNs is
inversely proportional to the discrepancy between con-
sidered generators - the higher difference of machine
inertia moments, the more misclassified OS cases ob-
served. However, also in this concern quite good ro-
bustness was achieved. In case of machine G2 (inertia
moment not far from that for machine G1) the classifi-
cation efficiency remained practically unchanged. In
case of machine G3 worse results (ca. 5% errors) were
obtained for some ANN input features, yet still perfect
recognition abilities (comparable to those for machine
G1) were maintained for ANNs fed with ∆ω signal.
The developed neural detection scheme (ANN with 9-9-
1 neurones, DWL=320 ms) has been compared with
chosen standard impedance-based OS protection
scheme. The scheme modelled consisted of two blinder
couples  - outer and inner, with assistance of the super-
visory MHO circle, Imhof (8). The out-of-step condi-
tions were detected by the scheme and tripping com-
mands issued when the left outer blinder was passed and
the supervisory MHO element was reset. The detection
parameters of both the ANN-based and standard OS
schemes as given in Table 2. The detection statistics
show that the standard OS scheme modelled was able to
recognise pole slipping conditions just before (or close
after) it actually took place. The ANN-based scheme, on
the contrary, was able to recognise all OS cases within
360 ms time. It means that for most of the cases a pre-
diction instead of detection action was really performed.
TABLE 2 - Operation parameters of the blinder-MHO
     and ANN-based OS detection schemes.
Time [ms]Pole slip
min mean max
a) observed 420 660 1300
b) detected (standard sch.) 345 610 1350
c) detected (ANN scheme) 320 322 360
It is the author’s belief that further improvement of pro-
posed neural OS detection scheme may be obtained with
additional careful analysis, considering:
• another (traditional or new) input patterns,
• optimisation of the threshold value,
• introduction of fuzzy concepts (hybrid solutions).
In the next section the results of research on the mixed
OS protection solutions are reported.
HYBRID ANN-FUZZY SOLUTIONS
Further optimisation of the protection scheme has been
done introducing some concepts of Fuzzy Logic (FL),
thus making the scheme an AI hybrid. Two cascade-
type neuro-fuzzy configurations have been examined.
The Fuzzy Inference System (FIS) module was used as
a pre-processor of natural power system signals (the FIS
output was further processed by an ANN) or as an inter-
preter of the ANN outputs (Fig. 4). Apart from the out-
put signal of the pre-processing unit (FIS or ANN, re-
spectively) some additional inputs may also be added.
Their character, number as well as time position are to
be examined in order to become the most optimal op-
eration of the scheme under investigation.
Both mixed ANN – FIS structures shown in Fig. 4 have
been thoroughly studied. The investigations described
here have been done for a Sugeno-type FIS structure
where the output of each rule is a linear combination of
input variables plus a constant term, and the final output
is the weighted average of each rule’s output. Initial FIS
structures were generated with data clustering and then
were represented as adaptive multi-layer feed-forward
networks (ANFIS), Jang (9). The ANFIS parameters
were tuned for the OS classification task with use of a
hybrid supervised learning procedure being a combina-
tion of the least-squares algorithm and the back-
propagation gradient descent method. The advantages of
the hybrid FIS-ANN signal processing can be analysed
in Fig. 5, where the scheme input signals (∆ω vector)
together with relevant intermediate and final outputs are
presented. The structure a) from Fig.4 was examined,
with current samples of ∆ω used as additional input for
the ANN.
OS
det.
ANN
output Fuzzy
Inference
System
b)
Other
input
signals
a)
Other
input
signals
FIS
output
ANN
OS
det.
Figure 4: Hybrid configurations studied
               a) FIS – ANN, b) ANN – FIS.
         
0 0.2 0.4 0.6 0.8 1 1.2-0.5
0
0.5
1
1.5
A
N
N
 o
ut
pu
t
0 0.2 0.4 0.6 0.8 1 1.2-0.5
0
0.5
1
1.5
Time [s]
D
ec
isi
on
3
3
1
1
2
2
0 0.2 0.4 0.6 0.8 1 1.2-0.5
0
0.5
1
1.5
FI
S 
ou
tp
ut
3
1
2
0 0.2 0.4 0.6 0.8 1 1.2-10
a)
b)
DWL
0
5
3
1 2
10
15
-5
DWL
∆ω
[r
ad
/s
]
Figure 5: Examples of FIS-ANN operation
for test signals (1, 2 – stable cases, 3 – OS case)
a) input signals, b) intermediate and final outputs
The testing cases from Fig. 5 were delivered by our in-
dustrial partner (ABB Power Automation) as a part of
the COMTRADE file libraries developed within the
Accurate Generator Model for Power System Applica-
tions project and intended for testing of ABB relaying
equipment. One OS and two stable cases owing to a
long lasting (300ms) symmetrical faults are presented.
For the sake of space shortage the primary signals
shown are limited to ∆ω calculated from relevant phase
voltages. It can be observed that during the short-circuit
interval the OS algorithm picks up for all three cases but
after the fault clearance and 320ms transient period
(data window length) its output stabilises around 0 or 1
giving correct state classification. Such a performance
was expected since the FIS-ANN scheme had been
trained with post-fault samples only. It is seen that ad-
ditional processing of FIS output by an ANN brought
about significant narrowing of the uncertainty area
around tripping threshold. The ANN outputs are closer
to 0 and 1 and thus more reliable decision can be issued.
As a result the hybrid system becomes more robust than
the other single-technique schemes developed.
CONCLUSIONS
The investigations on application of AI techniques to
out-of-step detection/prediction in power systems have
been presented in the paper. After thorough de-
sign/optimisation analysis promising results in shape of
high classification efficiency have been achieved. Rela-
tively compact structures (ANN with 9-9-1 neurones,
FIS with 3 rules) were achieved which implies that they
may be easily implemented on-line on traditional signal
processors without need for any expensive specialised
neural/fuzzy chips. The developed OS detectors proved
to be robust against changing power system conditions
(different fault types) and may be used for synchronous
machines of various ratings. The developed hybrid OS
detectors were able to recognise coming OS conditions
100-1000 ms before they actually took place (a predic-
tion action) thus providing a chance to undertake proper
preventive operations to maintain system stability and to
avoid presumable stresses to the protected machine.
ACKNOWLEDGEMENTS
The first author would like to extend his gratitude to the
Alexander-von-Humboldt-Foundation (Germany) for
financial supporting of this work. The authors thank
also Dr. J. Bertsch from ABB Power Automation, Ba-
den, Switzerland for delivering of testing signals.
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Out-of-Step Protection with AI Methods

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Out-of-Step Protection with AI Methods

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