Download International Electrical Engineering Journal (IEEJ) Vol. 7 (2016) No.3, pp. 2173-2181

International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
Torsional Oscillations Mitigation via Novel Fuzzy
Control Based Braking Resistor Model
1
M.Fayez Ahmed, 2 M.A. Ebrahim, 3M.A. El-Hadidy, 4W. M. Mansour
1
Cairo Electricity Production Company, Egypt
3
Egyptian Electricity Holding Company (EEHC), Egypt
2,4
Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
1
[email protected], [email protected], [email protected]
4
[email protected],

Abstract— Turbine generator shaft torsional oscillations is an
interdisciplinary problem since it involves mechanical and
electrical engineering. Torsional oscillations occur in the
mechanical systems for electrical reasons. There is a great
incentive to mitigate the shaft torsional oscillations especially
when unrestricted high speed reclosure (HSR) is utilized on the
overhead transmission lines emanating from a power plant.
Torsional oscillation damping compromises between the use of
HSR and preserving shaft fatigue life of the involved turbine
generator. In this paper a novel dynamic braking resistor model
is utilized for the purpose of damping torsional oscillations. The
novel model is the rectifier controlled braking resistor (RCBR).
It is connected at the generator terminals and controlled by
fuzzy logic controller to damp the turbine generator shaft
torsional oscillations after unsuccessful reclosure. Comparative
simulation study between the unsuccessful reclosure with and
without RCBR proves the effectiveness of the scheme for
damping torsional oscillations.
Index Terms— damping turbine-generator shaft torsional
oscillations, unsuccessful reclosure, rectifier controlled braking
resistor
(RCBR),
fuzzy
logic
controller
(FLC),
MATLAB/SIMULINK.
I. INTRODUCTION
In the analysis of power system dynamic performance, the
rotor of a turbine-generator (T-G) is assumed to be made of a
single mass. But, in fact, a T-G rotor is a very sophisticated
mechanical system. It consists of series of massive rotor
elements coupled in tandem at their slender shaft extensions
which ride with minimum friction in a well lubricated journal
bearings. The rotor of a steam turbine generator unit may
exceed 50 meters in total length and weight several hundred
tons [1, 2]. Under steady state conditions a constant torque in
the shaft extensions transfer kinetic energy between the
relatively rigid rotating masses. The mechanical shaft system
as a whole is coupled with the electrical network by the
electromagnetic torque (Te) in the generator air gap [3]. Any
sudden impact in the electromagnetic torque causes each rotor
in shaftline to oscillate about its rotational axis, resulting in
twisting in the different shaft sections and to a lesser extent in
Fayez
et. al.,
the large-diameter rotor masses [4].The magnitude of the
inertias of the individual masses together with magnitude of
the spring constants of the connecting shafts determine the
frequency of theses oscillations according to Newton’s Law
for torsional mechanics [5].
Torsional oscillations (vibrations) are so difficult to
recognize because of the following reasons [6]:
1. Most units in operation do not possess torsional
oscillations monitor.
2. Damaging magnitude torsional oscillations have very
small duration and unless the unit trips during the
event, most operators could not notice the
phenomenon making the corrective action is
impossible.
3. Torsional vibration, unlike lateral vibration, cannot be
felt on the turbine deck thus significantly torsional
events may go unobserved.
The main torsional oscillation stimulation mechanism falls
under abrupt changes in generator normal armature and field
currents.
These abrupt changes may happen due to the following
reasons [7]:
1. Network faults.
2. Fault clearings.
3. Automatic reclosing.
4. Transmission line switching.
5. Generator malsynchronization.
Since fault statistics indicate that over 80% of overhead
transmission line (OHTL) faults are temporary in nature it has
become a consensus operating feature to automatically
reclose the tripped circuit breaker after some dead time. This
dead time should be long enough for the secondary arc
extinction to guarantee the success of the autoreclosure [8].
Shaft torsional oscillations created by the faults and the
subsequent fault clearing still relatively sustained in the time
frame of high speed reclosure (HSR) because the torsional
oscillations have very light damping . In the case of
unsuccessful HSR two additional transients would occur i.e.
the second fault and the subsequent fault clearing. The two
2173
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
additional transients cause double torsional shock imposed to
the shafts of nearby turbine generators [9].
Depending on the timing of the chocks shaft torsional
stressing originating from the initial disturbance can be
ameliorated or exacerbated. As a consequence of the
exacerbation case the shaft fatigue life is jeopardized and the
mechanical integrity of the turbine generator unit is severely
affected [9].
The HSR has a lot of advantages such as [10]:
1. Overcoming the influences of OHTL sympathy trips
which occur due to undesirable relay operation for
faults on adjacent lines thus it helps to maintain
system stability.
2. Restoring electrical system integrity (i.e. restoring
system capacity and reliability) as soon as possible
after temporary transient faults which represent a
high percentage of overall system faults.
3. HSR is beneficial on transmission lines near
generating units due to the limited number of areal
transmission lines emanating from the generation
station.
The following factors limit the number of OHTL emanating
from a generation station [10]:
1. Extra high voltage transmission with inherent high
power transferring capability.
2. Considerations about economic issues.
3. Considerations about land usage and plant location.
Torsional oscillations may cause fatigue damage of T-G
shaft so it is imperative to mitigate the shaft segments
torsional oscillations resulting from faults and fault clearing
operation as fast as possible especially when unrestricted
HSR is utilized on the overhead transmission lines emanating
from a generation station .The mitigation of torsional
oscillations allows to compromise between the use of HSR
and preserving shaft fatigue life of the involved T-G [5].
The braking resistor (BR) was utilized for the enhancement
of system transient stability in the first place. It serves as an
extra resistive dummy load capable of dissipating surplus
generation in case of system severe faults occurring near
power plant. The dissipation of system surplus power
prevents generator loss of synchronism condition and enhance
system transient stability [11]. The BR utilizing for shaft
torsional oscillation mitigation was first introduced by
Wasynczuk in 1981. Wasynczuk proved that three phase
resistor bank connected to the generator terminals via full
wave ac voltage controller could damp T-G shaft torsional
oscillations [12]. A lot of researches found in the literature
addressing the use of thyristor controlled braking resistor
(TCBR) for turbine generator shaft torsional oscillations
mitigation [13-16]. TCBR per phase model is constructed as
back-to back connected thyristors with BR in series which
means that three BRs were utilized for torsional oscillations
mitigation.
In this paper, a new BR model, namely rectifier controlled
braking resistor (RCBR) model [17, 18] is proposed for
torsional oscillations mitigation .The use of only one BR
might reduce the overall size as well as cost of the overall BR
unit.
II. SYSTEM MODEL
Single machine infinite bus model is used for studying the
effect of RCBR on shaft torsional oscillations mitigation
which is shown in figure1. The system consists of four masses
steam turbine (not shown in the figure) driving a synchronous
generator and feeding an infinite bus via a step up transformer
and double circuit transmission lines. The model of RCBR
consists of one BR connected to the generator terminals via
six pulse full wave Rectifier Bridge. Figure 2 depicts the
Rectifier Controlled Braking Resistor system model.
Fuzzy Logic
Controller
Control Inputs
Line 2
Δω
G
Bridge State
CB1
Tr.
20/500 KV
P/V=0.9/1 Pu
Firing
Circuit
α
Line 1
CB2
Fault
Infinite Bus
Rectifier Controlled
Braking Resistor
Figure 1 SMIB Power System Model
T1
T3
T5
Phase A
Phase B
BR
Phase C
T4
T6
T2
Figure 2 Rectifier Controlled Braking Resistor System Model.
The steam turbine configuration under study is shown in
figure 3. It consists of high pressure (HP) section,
intermediate pressure (IP) section and two low pressure (LP)
sections [1]. The turbine contributing torque fractions for the
2174
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
Table 1 turbine-generator rotor detailed parameters
turbine sections HP, IP, LPA and LPB are 30% ,26%,22%
and 22% respectively [19].
Reheater
Cross Over
Mass
Shaft
Control Valve
HP
Intertia
Constant
H(second)
0.092897
Spring Constant K pu
Torque/rad.
HP-IP
Intercept
Valve
HP
Steam chest
19.303
0.155589
IP
IP-LPA
LPB
LPA
IP
34.929
0.858670
LPA
LPA-LPB
Shaft
52.038
0.884215
LPB
LPB-Gen.
To Condenser
Figure 3 Four Mass Steam Turbine Configuration [1]
The dynamic behavior of the turbine generator shaft system
is determined by following three parameters [1]:
1. Intertia constant H of each singular mass.
2. Torsional stiffness K of the shaft segments connecting
the neighboring rigid masses.
3. Mutual damping and self-damping coefficients.
The mutual damping (shaft damping) coefficient represents
the energy lost in the shaft metal as it oscillates and goes
through a stress/strain hysteresis cycles. The self-damping
coefficient represents the damping originating from the steam
flow into each turbine buckets [1].
Figure 4 shows the structure of five mass steam turbine
generator shaft system lumped mass-spring model for the case
study.
THP
TIP
K45
TLPA
K34
HP
H5
IP
H4
D45
LPA
H3
LPB
H2
D23
D33
Te
K12
K23
D34
D44
D55
TLPB
Gen.
H1
D12
D22
D11
Figure 4 Structure Of Lumped Mass Shaft System Model [2].
The Steam Turbine and Governor MATLAB/SIMULINK
block is used to implement a four mass steam turbine with two
time constants a speed governing system. The T-G shaft
detailed data are tabulated in table 1 [19].
70.858
0.868495
Gen.
The torsional damping is very small and it does not affect
the amplitude of torsional oscillations in the time frame of the
simulation study. So the shaft masses mutual damping is
neglected and the shaft masses self-damping is assumed to be
the same for all masses and equals 0.05 Pu Torque/rad due to
the lack of practical damping values in the IEEE first
benchmark model of subsynchronous resonance [1, 19].
The MATLAB/SIMULINK synchronous generator block
is used in our study to represent the turbine driven
synchronous generator in the simulations study and the
synchronous generator electrical parameters are tabulated in
table 2 [19].
Table 2 Synchronous Generator Electrical Parameters
G
Xd
X` d
X``d
Xl
Xq
X` q
1.79
0.169
0.135
0.13
1.71
0.228
892.4 MVA, 2 pole ,50Hz, 20KV
pu
X``q
0.2
pu
T`do
4.3
pu
T``do
0.03
`
pu
T qo
0.85
``
pu
T qo
0.05
pu
R
0
pu
s
s
s
s
pu
Static excitation system of IEEE ST1A type is used in the
simulation
study
and
it
is
implemented
by
MATLAB/SIMULINK ST1A excitation system block with
parameters tabulated in table 3 [1, 20].
Table 3 Excitation System Parameters
Parameter
Voltage regulator gain
Voltage regulator time constant
Voltage regulator output upper
limit
Voltage regulator output lower
limit
Rectifier loading factor
Exciter output current limiter
gain
Exciter output current limiter
reference
Transient gain reduction lag
time constants
Transient gain reduction lead
time constants
Damping filter gain
Damping filter time constant
Symbol
KA
TA
Value
200
0
VRMAX
7
VRMIN
-6.4
KC
0.04
KLR
4.54
ILR
4.4
TB,TB1
0
TC,TC1
0
KF
TF
1
0.001
2175
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
The generator step up (GSU) transformer is represented by
three phase MATLAB/SIMULINK transformer block with an
impedance of j0.14 per unit based on the generator apparent
power. The primary winding connection is delta and the
secondary winding connection is star.
Fuzzy logic controllers are proved to be more robust and
their performances have a lesser sensitivity to the parametric
variations than the conventional controllers [22]. Figure 6
depicts the schematic diagram of the fuzzy logic controller
system.
The transmission system is a double circuit system with
nominal voltage of 500 kV. It is represented using distributed
line parameters MATLAB/SIMULINK block. The line length
is 200 Km for each line. The parameters of each line are:
1. The resistance per unit length is 0.01165 Ω/km,
2. The inductance per unit length is 0.8679e-3 H/km
3. The capacitance per unit length is 13.41e-9 F/km.
The overall MATLAB/SIMULINK model including the
model of RCBR is shown in figure 5.
Figure 6 Fuzzy Logic Controller Basic Structure
The basic configuration of fuzzy-logic controller consists
of four parts as depicted in figure 6:
1. the fuzzification
2. the knowledge base
3. the inference engine
4. the defuzzification
Figure 5 SMIB MATLAB/ SIMULINK Model
The BR is chosen to be able to dissipate 40 % of the
generator apparent power (0.4 pu considering the machine
apparent power as a base power for the system).
III. FUZZY LOGIC CONTROLLER DESIGN
The fuzzy logic is dissimilar from the crispy logic in
boolean theory which uses only two logic levels (0 or 1) in
that it uses infinite logic levels (from 0 to 1) to solve a
problem that has uncertainties or imprecise situations [21].
In recent years, fuzzy-logic control has been proposed for
power system problems. Fuzzy logic controllers are nonlinear
controllers based on the use of expert knowledge in other
words they are rule-based systems. This knowledge is usually
obtained by performing extensive mathematical modelling,
analysis, and development of control algorithms for power
systems. A set of fuzzy rules usually characterized by
“IF-THEN” rules represent a control decision mechanism for
adjusting the effects of certain system stimuli [21-23].
The fuzzification is the process of mapping the input crisp
values into fuzzy variables using normalized membership
functions and input gains. The fuzzy-logic inference engine
deduces the proper control action based on the available rule
base. The fuzzy control action is transferred to the proper
crisp value through the defuzzification process using
normalized membership functions and output gains [24].
It has proven that the damping level obtained from using the
generator mass speed deviation as the controller input is better
than the damping level obtained from using other turbine
sections speed deviation [5, 14].
Practically the generator mass speed deviation is not
difficult to measure like HP,IP,LPA and LPB turbine masses
speed deviations since the entire steam turbine is rigidly
sealed with cases and thermal insulation [14].
So for the design of the fuzzy controller, the generator mass
speed deviation Δɷ (pu) is selected to be the fuzzy input and
the bridge state (either fully or fully off) as the fuzzy
controller output which is then sent to the firing circuit to
determine the appropriate firing angle.
The triangular membership functions are more convenient for
expressing the concept because it is easier to intercept
membership degrees from a triangle [25].
2176
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
NE
PO
DB
6
Electromagnetic Torque (pu)
Triangular membership functions are selected for the fuzzy
input as depicted in figure 7 in which the linguistic variables
NE, DB and PO stand for Negative, Dead Band and Positive
respectively.
Δɷ (pu)
-0.01
-0.005
0 0.002 0.005
4
2
0
-2
-4
0
0.01
Takagi –Sugeno inference mechanism is utilized in the
simulation study. The Center-of-Area is implemented as a
defuzzification method to determine the output crispy value
(the rectifier state). The rectifier state is then sent to the
rectifier bridge firing angle unit to determine if the bridge is
either fully on or fully off depending on the acceleration or
deceleration state of the generator mass.
4
5
HP-IP Torque (pu)
1.5
1
0.5
0
-0.5
-1
0
1
2
3
Time (Sec)
(b) HP-IP Shaft Torque in pu
4
5
4
5
4
5
3
IP-LPA Torque (pu)
Where zero indicates that the rectifier should be fully off
and one indicates that the rectifier should be fully off.
2
3
Time (Sec)
A-Generator Electromagnetic Torque in pu
Figure 7 Input membership Function of Generator Δɷ
The suggested control scheme is very straight forward and
simple since it has only three control rules where the BR is
inserted if the generator speed deviation exceeds the dead
band (the acceleration state) and removed elsewhere (steady
state and deceleration state).
There are three premise membership functions in figure 7, one
for each rule, and the conclusions are singletons so the fuzzy
control rules are:
1. If the input (Δɷ) is NE then the output is zero.
2. If the input (Δɷ) is DB then the output is zero.
3. If the input (Δɷ) is PO then the output is one.
1
2
1
0
-1
-2
0
1
2
3
Time (Sec)
(c) IP-LPA Shaft Torque in pu
IV. SIMULATION RESULTS
LPA-LPB Torque (pu)
For the purpose of testing the effectiveness and the
authenticity of the proposed scheme simulation study using
MATLAB/SIMULINK model is carried out considering the
worst case scenario. Three phase permanent bolted short
circuit is applied at transmission line number 1 very close to
the generator high voltage bus at 0.3 second from the
simulation time .The circuit breaker timings are assumed to
be:
1. 3 cycles (0.06 s) to clear the fault.
2. 30 cycles (0.5 s) to reclose.
3. 3 cycles (0.06 s) to reclear the fault.
4
3
2
1
0
-1
-2
0
1
2
3
Time (Sec)
(d) LPA-LPB Shaft Torque in pu
These timings are chosen in such a way that the torsional
responses are exacerbated to presume the most pessimistic
conditions .The system responses in pu without RCBR are
shown in figures 6 and 7.
2177
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
LPB-Gen. Torque (pu)
6
4
2
0
-2
-4
0
1
2
3
Time (Sec)
4
5
LPA Mass Speed Deviation (pu)
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
0.01
0
-0.01
-0.02
0.02
0.01
0
1
2
3
Time (Sec)
4
5
(c) LPA Turbine Mass Speed Deviation in pu
0.04
IP Mass Speed Deviation (pu)
The simulation results in figure 6 clarify that the
unsuccessful reclosure imposes devastating and poorly
damped shaft transient torques which consume
significant amount of shaft fatigue life and makes the T-G
mechanical integrity vulnerable.
-0.01
0.02
0
-0.02
-0.04
0
-0.02
1
2
3
Time (Sec)
4
5
(d) IP Turbine Mass Speed Deviation in pu
0.06
-0.03
0
1
2
3
Time (Sec)
4
5
(a) Generator Mass Speed Deviation in pu
0.04
0.02
0
HP Mass Speed Deviation (pu)
Gen. Mass Speed Deviation (pu)
0.02
-0.03
0
(e) LPB-Gen. Shaft Torque in pu
Figure 6 System Torque Responses without RCBR
LPB Mass Speed Deviation (pu)
0.03
0.04
0.02
0
-0.02
-0.04
-0.06
0
-0.02
1
2
3
4
5
Time (Sec)
(e) HP Turbine Mass Speed Deviation in pu
Figure 7 Individual Masses Speed Deviation Responses without RCBR
-0.04
0
1
2
3
Time (Sec)
4
(b) LPB Turbine Mass Speed Deviation in pu
5
The simulation results depicted in figure 7 show that
the individual masses of the drive shaft train experience a
severe changes in their speeds arising from the transients
in the generator air gap torque.
2178
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
4
2
0
-1
1
-2
1
2
3
4
Time (Sec)
(a) Generator Electromagnetic Torque in pu
0.5
0
-0.5
0
2
3
Time (Sec)
(b) HP-IP Shaft Torque in pu
4
0
-1
0
-0.5
4
5
Gen. Mass Speed Deviation (pu)
0.5
(c) IP-LPA Shaft Torque in pu
2
3
Time (Sec)
4
The simulation results in figure 8 show that T-G shaft torque
responses experienced a significant damping with a RCBR in
service.
1
2
3
Time (Sec)
5
1
(e) LPB-Gen. Shaft Torque in pu
Figure 8 System Torque Responses with RCBR
1.5
1
1
2
5
2
-1
0
5
3
-2
0
1
2
3
4
Time (Sec)
(d) LPA-LPB Shaft Torque in pu
4
5
1
HP-IP Torque (pu)
1
-2
0
-4
0
IP-LPA Torque (pu)
2
0
LPB - Gen. Toque (pu)
Electromagnetic Torque (pu)
6
3
LPA-LPB Torque (pu)
The simulation is done again but with RCBR in service
and the system responses in pu with RCBR are shown in
figure 8 and 9.
0.015
0.01
0.005
0
-0.005
-0.01
-0.015
0
1
2
3
Time (Sec)
4
5
(a) Generator Mass Speed Deviation in pu
2179
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
LPB mass Speed Deviation (pu)
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
It is clear that from the simulation result shown in figure 9
that the turbine generator drive train individual masses exhibit
a significant damping.
0.02
0.01
V. CONCLUSION
0
-0.01
-0.02
0
1
2
3
Time (Sec)
4
5
LPA Mass Speed Deviation (pu)
(b) LPB Turbine Mass Speed Deviation in pu
0.02
0.01
0
-0.01
REFERENCES
IP Mass Speed Deviation (pu)
-0.02
0
2
3
4
Time (Sec)
(c) LPA Turbine Mass Speed Deviation in pu
1
5
0.02
0.01
0
-0.01
-0.02
0
1
2
3
4
5
Time (Sec)
(d) IP Turbine Mass Speed Deviation in pu
0.06
HP Mass Speed Deviation (pu)
This paper utilizes novel dynamic BR model which is
rectifier controlled braking resistor (RCBR) and fuzzy logic
controller to damp turbine generator shaft torsional
oscillations arising from the unsuccessful reclosure . From the
simulation results the system exhibit a significantly good
damping which allows the torsional oscillations and masses
speed deviations to die out quickly after the severest kind of
torsional duty .This supplementary damping prevents
jeopardizing the mechanical integrity of the T-G shaft system
which consequently reduces the T-G rotor fatigue life
vulnerability. Unlike other similar works found in the
literature with regard to using BR as a torsional oscillations
mitigation method, this work utilizes only one BR unit which
might lead to a reduced system size and cost. The scheme
advantages may encourage the utility to use it for damping
T-G shaft torsional oscillations arising from unrestricted
HSR.
0.04
0.02
0
-0.02
-0.04
0
1
2
3
4
5
Time (Sec)
(e) HP Turbine Mass Speed Deviation in pu
Figure 9 Individual Masses Speed Deviation Responses with RCBR
P. Kundur “Power System Stability and Control”, McGraw-Hill, Inc.,
1994.
[2] IEEE Working Group Interim Report “Effects of Switching Network
Disturbances on Turbine-Generator Shaft Systems”, IEEE
Transactions on Power Apparatus and Systems, Vol. PAS-101, No. 9
September 1982.
[3] P. A. E. Rusche, “Network Alternative to Reduce Turbine-Generator
Shaft Stresses” IEEE Transactions on Power Apparatus and System,
Vol.PAS-98, No.2 March/April 1979. .
[4] Duncan N. Walker, “Torsional Vibration of Turbomachinery”, the
McGraw-Hill, 2003, Ch.1.
[5] EPRI Technical Report 103902, “Dynamic Brake Control to Reduce
To Reduce Turbine Shaft Transient Torque”, September 1994.
[6] Geoff Klempner, Isidor Kerszenbaum, “Handbook of Large
Turbo-Generator Operation and Maintenance”, A John Wiley & Sons,
Inc., 2008, Ch.4.
[7] IEEE Working Group Interim Report, “Effects of Switching Network
Disturbances on Turbine-Generator Shaft Systems”, IEEE
Transactions on Power Apparatus and Systems, Vol. PAS-101, No. 9
September 1982.
[8] C.E.J. Bowler, P.G. Brown, D.N. Walker, “Evaluation of the Effect of
Power Circuit Breaker Reclosing Practices on Turbine-Generator
Shafts IEEE Transactions on Power Apparatus and Systems, Vol.
PAS-99, No. 5 Sept/Oct 1980.
[9] John S. Joyce, Tadeusz Kulig, Dietrich Lambrecht, “The Impact of
High-Speed Reclosure of Single and Multi-Phase System Faults on
Turbine-Generator Shaft Torsional Fatigue”, IEEE Transactions on
Power Apparatus and Systems, Vol. PAS-99, No. 1 Jan. /Feb. 1980.
[10] R.D. Dunlop, S.H. Horowitz, A.C. Parikh, M.C. Jackson, “Turbine
Generator Shaft Torques and Fatigue: Part II - Impact of System
Disturbances and High Speed Reclosure” IEEE Transactions on Power
Apparatus and Systems, Vol. PAS-98, No.6 Nov. /Dec. 1979.
[11] Jan Machowski, Janusz W. Biaek, James R. Bumby, “Power System
Dynamics - Stability and Control”, second edition, John Wiley & Sons,
Ltd, 2008, Ch. 10.
[1]
2180
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model
International Electrical Engineering Journal (IEEJ)
Vol. 7 (2016) No.3, pp. 2173-2181
ISSN 2078-2365
http://www.ieejournal.com/
[12] Li Wang, Ching-Huei Lee, “Application of dynamic resistance braking
on stabilizing torsional oscillations”, IEEE TENCON '93 / Beijing.
[13] O. Wasynczuk, “Damping Shaft Torsional Oscillations Using a
Dynamically Controlled Resistor Bank” IEEE Transactions on Power
Apparatus and Systems, Vol. PAS-100, No. 7 July 1981.
[14] Mohd. Hasan Ali,Takumi Mikami,Toshiaki Murata,Junji Tamura, “A
Fuzzy Logic Controlled Braking Resistor Scheme For Damping Shaft
Torsional Oscillations”, IEEJ Transactions on Power and Energy, Vol.
124, No. 2, pp. 207214, February 2004.
[15] Mohd. Hasan Ali, Minwon Park, In-Keun Yu, Toshiaki Murata, Junji
Tamura, “Coordination of Fuzzy Controlled Braking Resistor and
Optimal Reclosing for Damping Shaft-Torsional Oscillations of
Synchronous Generator”, Proceeding of International Conference on
Electrical Machines and Systems 2007, Oct. 8-11, Seoul, Korea..
[16] Mohd. Hasan Ali, Minwon Park, In-Keun Yu, “Minimization of Shaft
Torsional Oscillations by Fuzzy Controlled Braking Resistor
Considering Communication Delay”, WSEAS Transactions on Power
Systems, Issue 3, Volume 3, March 2008.
[17] Riya Saluja, Mohd. Hasan Ali, “Novel Braking Resistor Models for
Transient Stability Enhancement in Power Grid System”, Innovative
Smart Grid Technologies (ISGT), 2013 IEEE PES.
[18] Riya Saluja, Sagnika Ghosh, Mohd. Hasan Ali, “Transient Stability
Enhancement of Multi-Machine Power System by Novel Braking
Resistor Models”, Southeastcon, 2013 Proceedings of IEEE.
[19] IEEE Subsynchronous Resonance Task Force of the Dynamic System
Performance Working Group Power System Engineering Committee,
“First Benchmark Model for Computer Simulation of Subsynchronous
Resonance” IEEE Transactions on Power Apparatus and Systems, Vol.
PAS-96, No. 5, September/October 1977.
[20] IEEE Power Engineering Society, “IEEE Recommended Practice for
Excitation System Models for Power System Stability Studies”, IEEE
Std 421.5-2005.
[21] Mohd. Hasan Ali,Toshiaki Murata,Junji Tamura, “Effect of
Coordination of Optimal Reclosing and Fuzzy Controlled Braking
Resistor on Transient Stability During Unsuccessful Reclosing” ,IEEE
Transactions On Power Systems, Vol. 21, No. 3, August 2006.
[22] Kapil Dev Sharma, Shailika Sharma, M.Ayyub, “Design of Different
Fuzzy Controllers for Delayed Systems”, International Electrical
Engineering Journal (IEEJ) ,Vol. 6 (2015) No.12, pp. 2103-2108.
[23] M. A. Ebrahim, K. A. El-Metwally, F. M. Bendary , W. M. Mansour,
“Fuzzy Stabilizer Design For Renewable Energy Based Distribution
Networks”, 22nd International Conference on Electricity Distribution
Stockholm, 10-13 June 2013
[24] M. A. Ebrahim, K. A. El-Metwally, F. M. Bendary , W. M. Mansour,
“Transient Stability Enhancement of a Wind Energy Distributed
Generation System by Using Fuzzy Logic Stabilizers”, WIND
ENGINEERING VOLUME 36, NO. 6, 2012 PP687-700
[25] I.H. Altas and A.M. Sharaf, “A Generalized Direct Approach for
Designing Fuzzy Logic Controllers in Matlab/Simulink GUI
Environment”, ”, International Journal of Information Technology and
Intelligent Computing, Int. J. IT&IC no.4 vol.1, 2007.
2181
Fayez
et. al.,
Torsional Oscillations Mitigation via Novel Fuzzy Control Based Braking Resistor Model