IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24, NO. 4, NOVEMBER 2009
1731
Maximum Frequency Deviation Calculation
in Small Isolated Power Systems
Ignacio Egido, Fidel Fernández-Bernal, Pablo Centeno, and Luis Rouco, Member, IEEE
Abstract—Large frequency deviations due to a number of disturbances are frequent in small isolated power systems. The maximum frequency deviation in the system is limited to prevent other
generator tripping. It is important to have an accurate model to calculate it, both for system planning and operation. A new simplified
model to calculate the maximum frequency deviation when either
a generator or load-related disturbance occurs in these systems
is presented. This model takes into account the response of governor-prime mover even when different technologies are present in
the power system. Model parameters can be easily obtained from
either more complex models or from test records. Simulation results for an actual power system aimed at checking the model accuracy are presented. High accuracy is obtained while computation
time is reduced due to the simplicity of the model.
Index Terms—Frequency deviation, isolated power systems, load
shedding, minimum frequency, spinning reserve.
NOMENCLATURE
Ramp gain of generator (
in
).
Ramp gain of generator (
in
).
Base frequency (Hz).
Minimum frequency (Hz).
Pre-disturbance system frequency (Hz).
Equivalent inertia of the power system (s in
).
Inverse of the droop of generator (
in
).
Inverse of the droop of generator (
in
).
Inverted sign frequency derivative
.
Number of generating units in the power system.
System base power after disturbance (MVA).
Rated power of generator (MVA).
Time from disturbance (s).
Time instant of minimum frequency occurrence
(s).
Time constant of governor (s).
Frequency deviation
.
Manuscript received October 16, 2008; revised March 18, 2009. First published September 09, 2009; current version published October 21, 2009. Paper
no. TPWRS-00837-2008.
I. Egido, F. Fernández-Bernal, and L. Rouco are with Escuela Técnica Superior de Ingeniería (ICAI), Universidad Pontificia Comillas de Madrid, Madrid,
Spain (e-mail: egido@iit.upcomillas.es).
P. Centeno is with Alstom Power Systems, Baden, Switzerland.
Digital Object Identifier 10.1109/TPWRS.2009.2030399
Maximum frequency deviation
.
Disturbance: increment of load power
System frequency
.
.
Pre-disturbance system frequency
.
I. INTRODUCTION
S
MALL isolated power systems exhibit special characteristics which make them particularly vulnerable to the occurrence of large frequency deviations affecting the security of the
system. The main characteristic is the low value of the inertia
constant due to the reduced number of generators connected to
the system and the fact that most of the generators in isolated
systems are driven by diesel engines. When a generator trips,
these power systems may exhibit high initial rates of frequency
decay. When a generator trips, not only a large amount of generation can be lost, but inertia also, both factors contributing
to a high value of the initial slope of the frequency. This fact
makes the existing spinning reserve useless in some cases, since
it cannot be fast enough to arrest the frequency decay [1]. To address these issues, usual actions and limitations are: load shedding (by under-frequency relays and rate of frequency relays),
minimum spinning reserve, load reconnection limitation, and
wind power generation limitation (sometimes restricted during
operation in isolated power systems for fear of a sudden trip of
wind generation which can lead to load shedding) as shown in
[2]–[4].
Adjusting frequency relays in load shedding schemes, estimation of the amount of necessary spinning reserve, limitation
of the amount of load reconnection, or calculation of the wind
power generation limit to preserve security in the power system
are key examples where a model to estimate frequency deviation
is essential. If these assessments are to be calculated in normal
operation to provide adaptive adjustments or limitations, then a
simple model is necessary. Model proposed in [5] has been used
for this purpose in [6]–[8], although it has the drawback that all
governors and prime movers in the system have to be very similar in speed and, in principle, all generation have to be of the reheat steam turbine type. On the other hand, the model proposed
in this paper is a linear model that admits a variety of generation
technologies with very different governors. This is the case in
small isolated power systems, where the generation mix usually
comprises diesel, gas, fossil-fueled steam, and sometimes combined cycle gas turbines. A method to obtain model parameters
from a more complex model or from dedicated tests is detailed.
Simulation results are also presented to verify the accuracy of
the model.
0885-8950/$26.00 © 2009 IEEE
1732
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24, NO. 4, NOVEMBER 2009
Fig. 3. IEEE recommended general model for steam turbine speed governing
systems: IEEEG1 PSS/E model.
Fig. 1. Equivalent model in Laplace transform of a power system to analyze
the frequency behavior.
Fig. 2. Typical diesel governor and prime mover: DEGOV1 PSS/E model.
II. FREQUENCY DEVIATION MODELS IN THE LITERATURE
Fig. 4. Typical gas turbine governor and prime mover: GASTWD PSS/E
model.
A. Classical System Model
A typical model used to represent the frequency deviations in
a power system is shown in Fig. 1 [2]. Governor represents the
model of the speed governor and the prime mover of generating
unit and will be detailed in Section II-B.
This general model, and therefore all simplified models derived from it, assumes the following typical simplifications:
1) Frequency value is uniform throughout the system. Therefore,
the inter-machine oscillations are neglected; 2) The inertias
of the different generating units are joined in one equivalent
, taking into account the different rated power of the
inertia,
generating units; 3) The overall damping of loads due to their
frequency dependency is modeled by a single damping factor .
B. Complex Speed Governor and Prime Mover Models
Dynamic models to adequately represent the behavior of
the speed governors and prime movers have been widely
presented and used in the literature. Models of steam turbines
are presented in [9]–[11]. Models of hydraulic turbines are
detailed [12] whereas models of gas turbines are provided in
[13]. Models of combined cycle plants are provided in [14] and
[15]. Models for different technologies are also presented in
[16]–[19].
Examples of typical models of governor-prime movers are
shown in Figs. 2–4. A typical diesel model (DEGOV1 PSS/E
model [20]) is shown in Fig. 2, IEEE recommended general
model for steam turbine speed governing systems (IEEEG1
PSS/E model [20]) in Fig. 3, and a typical gas turbine model
(GASTWD PSS/E model [20]) in Fig. 4.
C. Simplified Speed Governor and Prime Mover Models
As shown before, dynamic models to adequately represent the
behavior of the speed governors and prime movers are usually
complex, which makes it difficult to perform simplified calculations regarding the behavior of the frequency deviation. In technical literature, two levels of simplification have been applied to
the model of Fig. 1:
1) Governors and prime movers are considered to keep con, and
stant the output power after a step disturbance
damping of the loads is neglected. In that case, frequency
deviation is calculated by the well-known expression (1)
[21]. Time response is shown in Fig. 5:
(1)
This assumption can be considered correct in the very first
instants after the disturbance, and it is a very good approximation to the initial value of the frequency derivative. Note
that (1) cannot be used to calculate the minimum value of
frequency (frequency correction is due to governors actions modifying the output power in order to correct frequency deviation).
2) All the governors and prime movers in the system are considered to be identical, and therefore, all of them can be
substituted by one equivalent governor. In [5], the proposed
model assumes that all governor-prime movers are of the
reheat steam turbine type, as shown in Fig. 6. This model
has proved to be useful in frequency deviation prediction
EGIDO et al.: MAXIMUM FREQUENCY DEVIATION CALCULATION IN SMALL ISOLATED POWER SYSTEMS
1733
Fig. 5. Evolution of frequency under the assumption of constant output power
after a step disturbance.
Fig. 7. Transient response for a 1% frequency step in DEGOV1 PSS/E model
for an actual diesel unit (solid line). Comparison with a first-order model fitted
to 25 s of data (dashed line) and fitted using only the first 4 s of data (dotted
line).
Fig. 6. Equivalent model of the power system to analyze the frequency behavior in [5].
and power imbalance estimation for adaptive load shedding methods as shown in [6]–[8].
III. PROPOSED MODEL TO OBTAIN THE MINIMUM FREQUENCY
Model of Fig. 6 is limited to units of the reheat steam turbine
type and all unit governor-prime movers should be identical. If
other type of unit is present (e.g., diesel or gas), or unit governors
are very different, the model of Fig. 6 cannot be used. Hence, a
more general model is needed.
The model proposed in this paper takes into account the response of the governors of all units in the system and produces
sensible results where the aforementioned simple models cannot
be used.
A. First-Order Model for Speed Governor and Prime Mover
In Figs. 7 and 8, the open-loop transient responses for a 1%
frequency step for the models of Figs. 2 and 4 are shown in
the solid line, respectively. Parameters used for simulation come
from real generators in the Spanish isolated power systems.
If the open-loop (governor-prime mover) is assumed to be
similar to a linear first-order system [see (2)], the estimation
of gain,
(modeling the inverse of the permanent droop,
), and constant time, (modeling speed response), is
necessary:
(2)
Fig. 8. Transient response for a 1% frequency step in GASTWD PSS/E model
for a real gas turbine group (solid line). Comparison with a first-order model
fitted to 25 s of data (dashed line) and fitted using only the first 4 s of data
(dotted line).
In Figs. 7 and 8, the response of a first-order system (dashed
line) is shown. The value of parameter is calculated from the
detailed model to obtain the same droop. Then, the value of
parameter is obtained by fitting the response of the first-order
model in (2) to the response of the detailed model in Figs. 2 and 4
using minimum-squared-error technique for 25 s of data. If data
from a step test for a generator are available, the same technique
can be used to fit model parameters to the real response of the
group. In both cases (Figs. 7 and 8), this long-term first-order
model obtained might be considered accurate enough. However,
in isolated power systems, minimum frequency usually appears
in the first seconds after the disturbance. To improve the fitting
in these first seconds of the response, parameters are computed
taking into account only the first seconds of data. Moreover, as
the steady-state value is not important in this case, both and
can be calculated using minimum squared-error techniques.
As minimum frequency occurs in the range of 1 to 4 s after the
disturbance in all the Canary Islands and Balearic Islands power
systems, fitting results obtained using only the first 4 s after the
1734
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24, NO. 4, NOVEMBER 2009
Fig. 9. Simplified equivalent model of the power system to analyze the frequency behavior using short-term first-order model for governors and prime
movers.
disturbances are shown as an example in Figs. 7 and 8 with a
dotted line (a different time range might be necessary in other
isolated systems). As can be seen in the figures, the short-term
fitting in the first 4 s is improved although the long-term fitting
is worsened.
Then, as a first simplification, let us model governors and
prime movers in the first seconds by a short-term first-order
model as shown in Fig. 9. Note that in Fig. 9, steady state has
instead of . Usually, droop is given in per
been noted by
unit of the generator [and then in (2)] and in the diagram of
:
Fig. 9 droop has to be in per unit of the system
where
is the base power of group and
of the system with N generating units:
Fig. 10. Simplified open-loop equivalent model of the power system to analyze
the frequency behavior.
C. Constant Ramp Model for Speed Governor
and Prime Mover
In Fig. 10, the output of governor is given by
(5)
Output begins with zero slope and in steady-state becomes a
, being
the slope of the frequency
ramp of slope
decay in (1). Since we are just interested in the evolution of
frequency deviation down to its minimum value, let us assume
given by (5) could
the additional simplification that output
as
be approximated by an averaged constant ramp
shown in (6):
(3)
(6)
is the base power
would be a value between 0 and a value lower than
where
. Note that
is a kind of ramp gain of the group . The
final simplified equivalent model proposed is shown in Fig. 11,
where load variation with frequency, , is also neglected. With
typical values of (5 s) and (1 ), the exponential response
and the ramp response of
are very
of
similar during the first seconds where minimum frequency usually takes place.
is that
A maximum value, and likely a good choice, for
in (6) to be the same as the value given by
which makes
:
(5) at the instant in which frequency is minimum
(4)
B. Breaking the Closed-Loop
In Fig. 9, the sum of governor outputs is the input for the
inertia integration that produces a lag in the frequency evolution.
Therefore, to compute the maximum frequency deviation, the
important effect is the output of governors some seconds before,
when they where excited by the quasi-linear decay of frequency
(see Fig. 14 as an example). Then, the input to the governors
during the transient could be modeled by (1) (see Fig. 5). In that
case, closed-loop can be broken yielding to the model in Fig. 10.
(7)
Then, from (7),
is obtained
(8)
EGIDO et al.: MAXIMUM FREQUENCY DEVIATION CALCULATION IN SMALL ISOLATED POWER SYSTEMS
1735
Minimum is found solving the following:
(11)
and, taking into account (1),
is obtained:
(12)
Substituting (12) in (10), maximum frequency deviation is
found:
(13)
and minimum frequency in Hz is
(14)
Fig. 11. Proposed equivalent model of the power system to analyze the frequency behavior.
where is the pre-disturbance system frequency and
system base frequency.
Had all the units the same averaged ramp gain,
, minimum frequency in Hz would be
is the
(15)
are preferred
If the ramp gains in per unit of the group
instead of the ramp gains in per unit of the system, , then
(16)
Substituting (16) in (14) and (15):
(17)
Fig. 12.
C
=K
ratio as a function of t
=T .
and, if
In Fig. 12, the
ratio in function of a sensible range of
using (8) is shown. Therefore,
.
IV. CALCULATION OF THE MINIMUM FREQUENCY
From the model in Fig. 11, frequency deviation evolution is
given by the following:
(9)
(18)
from (8) is chosen as the value for , all
ramp
If
gains can be calculated substituting (12) into (8) and solving
equation system ( and
are
the resulting nonlinear
known):
..
.
Solving (9) in time yields
(19)
(10)
1736
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24, NO. 4, NOVEMBER 2009
TABLE I
GENERATING UNIT DATA
Fig. 13. Single line diagram of the power system.
Solution of (19) can be found easily and fast enough using
any iterative numerical method. However, an additional simplification can be made with some loss of accuracy. Assuming a
for the system analyzed from expertise, a
typical value of
sensible value of
can be obtained from (8) or Fig. 12. Thus,
the minimum frequency for a specific perturbation can be obin
tained directly from (14). For example, a typical value for
. For the diesel unit
the Spanish isolated systems is
of 9.4 MVA of Fig. 7,
,
. If
in the system, then
and
. Using
could be a good tradeoff.
Surprisingly, from (13), maximum frequency deviation seems
. Mainly, this fact comes
not to depend on system inertia,
from (6), where it is assumed that the rate of change of output
power is proportional to the rate of change of frequency. This
is true to a certain extent, but it is really a simplification. Note
are calculated from (19), then
depends on
that if and
and
depends on
.
is necesTo calculate maximum deviation from (13),
is
sary. If this calculation is to be performed offline, then
directly the tripped power to be analyzed. If this calculation is to
be performed online (for an adaptive shedding scheme, for exhas to be estimated from (1) by an estimation
ample), then
algorithm using frequency measurements—for instance, using
the procedure and the Newton-type algorithm proposed in [8].
TABLE II
VALLEY AND PEAK DEMAND SCENARIOS
V. SIMULATION RESULTS
Simulations have been carried out on several Spanish isolated power systems. This paper shows the results for an isolated
system within the Canary Islands power system. This system
consists of 19 diesel units and four gas turbine units adding up
to 480 MVA. A simplified power system model for a peak scenario is depicted in Fig. 13. PSS/E model, inertia, rated power,
and first-order model associated for 4 s fitting are presented in
Table I.
Simulations have been carried out on valley demand (three
contingences) and peak (four contingences) scenarios, where no
limits are reached. These scenarios are summarized in Table II.
As an example, simulation results of the detailed model for a
18-MW unit trip in the valley scenario are shown in Fig. 14,
, generator output
, and dewhere system frequency
are presented. Maximum frequency deviation yields
mand
is 2.98 s. On the other hand,
1110 mHz (0.0222 ) and
using the simplified proposed model [using (19)], maximum freand
is 2.45 s.
quency deviation yields 970 mHz (0.0194
Note that generator tripping is equivalent to consider
and
that
and
have to be the ones after the disturbance.
These results and others obtained for the seven contingences
analyzed are summarized in Table III. Although load variation
with voltage and frequency is not taken into account in the proposed model, the accuracy achieved in calculated system minimum frequency is very sensible for all cases.
Results depend on the values of and of each generator.
and
are obtained using
As explained in Section III-A,
a minimum squared-error fitting for a given fitting time (in
EGIDO et al.: MAXIMUM FREQUENCY DEVIATION CALCULATION IN SMALL ISOLATED POWER SYSTEMS
1737
Programming and computational effort to calculate maximum frequency deviation using this model is affordable using
a simple PC with very short calculation time, which allows
them to be performed even online if needed.
The computational speed and accuracy obtained will help to
provide adaptive adjustments or limitations in normal operation to frequency relays, spinning reserve, load reconnection, or
wind generation, among others, in isolated power systems. This
model has been used by the Spanish System Operator to assess
all these issues in some of the Spanish islands power systems.
Based on this model, new adaptive load shedding methods can
be developed.
REFERENCES
Fig. 14. Simulation results for valley demand scenario in case a 18-MW unit
tripping.
TABLE III
SIMULATION RESULTS FOR VALLEY AND PEAK DEMAND SCENARIOS
TABLE IV
MINIMUM FREQUENCY CALCULATED FOR DIFFERENT MODELS
Section III-A, 4 s was used). In order to analyze the sensitiveness of minimum frequency calculation with the selection of
the fitting time, minimum frequency calculated for parameters
and
estimated with different fitting times are presented
in Table IV. Simulation times of 3 s, 4 s, and 5 s have been
selected as reasonable values. As can be seen in the table,
minimum frequency is nearly the same in the three cases.
VI. CONCLUSION
In this paper, a novel simple model to estimate maximum frequency deviations in small isolated power systems when generation or load related perturbations take place is presented. This
model takes into account the regulation speed of spinning reserve even when different generation technologies with very
different governors are present in the power system. Regulator
closed-loops are opened and substituted by simple ramp gains,
making the model simpler and calculations faster. The simple
procedure to obtain model parameters from a more complex
model or from real data has been presented.
Detailed simulations have been conducted proving that the
accuracy of the proposed model is very sensible.
[1] R. M. Maliszewski, R. D. Dunlop, and G. L. Wilson, “Frequency actuated load shedding and restoration. I. Philosophy,” IEEE Trans. Power
App. Syst., vol. PAS-90, no. 4, pp. 1452–1459, Jul.–Aug. 1971.
[2] P. Kundur, Power System Stability and Control. New York: McGrawHill, 1994.
[3] C. Concordia, L. H. Fink, and G. Poullikkas, “Load shedding on an
isolated system,” IEEE Trans. Power Syst., vol. 10, pp. 1467–1472,
1995.
[4] H. You, V. Vittal, and Z. Yang, “Self-healing in power systems: An approach using islanding and rate of frequency decline-based load shedding,” IEEE Trans. Power Syst., vol. 18, pp. 174–181, 2003.
[5] P. M. Anderson and M. Mirheydar, “A low-order system frequency
response model,” IEEE Trans. Power Syst., vol. 5, pp. 720–729, 1990.
[6] P. M. Anderson and M. Mirheydar, “An adaptive method for setting
underfrequency load shedding relays,” IEEE Trans. Power Syst., vol.
7, pp. 647–655, 1992.
[7] S. J. Huang and C. C. Huang, “An adaptive load shedding method with
time-based design for isolated power systems,” Int. J. Elect. Power Energy Syst., vol. 22, pp. 51–58, 2000.
[8] V. Terzija, “Adaptive underfrequency load shedding based on the
magnitude of the disturbance,” IEEE Trans. Power Syst., vol. 21, pp.
1260–1266, 2006.
[9] F. P. DeMello, “Boiler models for system dynamic performance
studies,” IEEE Trans. Power Syst., vol. 6, pp. 66–74, 1991.
[10] F. P. DeMello, “Dynamic models for fossil fueled steam units in power
system studies,” IEEE Trans. Power Syst., vol. 6, pp. 753–761, 1991.
[11] T. Inoue, H. Taniguchi, and Y. Ikeguchi, “A model of fossil fueled
plant with once-through boiler for power system frequency simulation
studies,” IEEE Trans. Power Syst., vol. 15, pp. 1322–1328, 2000.
[12] IEEE Working Group on Prime Mover and Energy Supply Models for
System Dynamic Performance Studies, “Hydraulic turbine and turbine
control models for system dynamic studies,” IEEE Trans. Power Syst.,
vol. 7, pp. 167–179, 1992.
[13] W. I. Rowen, “Simplified mathematical representations of heavy duty
gas turbines,” Trans. ASME, J. Eng. Power, vol. 105, pp. 865–870,
1983.
[14] F. P. DeMello and D. J. Ahner, “Dynamic models for combined cycle
plants in power system studies,” IEEE Trans. Power Syst., vol. 9, pp.
1698–1708, 1994.
[15] Q. Zhang and P. L. So, “Dynamic modelling of a combined cycle plant
for power system stability studies,” in Proc. 2000 IEEE Power Engineering Society Winter Meeting, Singapore, 2000, pp. 1538–1543.
[16] R. T. Byerly, O. Aanstad, D. H. Berry, R. D. Dunlop, D. N. Ewart, B. M.
Fox, L. H. Johnson, and D. W. Tschappat, “Dynamic models for steam
and hydro turbines in power system studies,” IEEE Trans. Power App.
Syst., vol. PAS-92, pp. 1904–1915, 1973.
[17] V. Kola, A. Bose, and P. M. Anderson, “Power plant models for operator training simulators,” IEEE Trans. Power Syst., vol. 4, pp. 559–565,
1989.
[18] L. Pereira, D. Kosterev, D. Davies, and S. Patterson, “New thermal
governor model selection and validation in the WECC,” IEEE Trans.
Power Syst., vol. 19, no. 1, pp. 517–523, Feb. 2004.
[19] L. Pereira, J. Undrill, D. Kosterev, D. Davies, and S. Patterson, “A
new thermal governor modeling approach in the WECC,” IEEE Trans.
Power Syst., vol. 18, no. 2, pp. 819–829, May 2003.
[20] PSS/E-29 Online Documentation, Power Technologies, Inc., Oct.
2002.
[21] M. S. Baldwin and H. S. Schenkel, “Determination of frequency decay
rates during periods of generation deficiency,” IEEE Trans. Power App.
Syst., vol. PAS-95, pp. 26–36, 1976.
1738
Ignacio Egido was born in Arévalo (Ávila), Spain,
in 1976. He received the M.S. and Ph.D. degrees
in electrical engineering from the Universidad Pontificia Comillas, Madrid, Spain, in 2000 and 2005,
respectively.
He is currently an Assistant Professor in the Department of Electrical Engineering of the School of
Engineering of Universidad Pontificia Comillas. He
develops his research activities at the Instituto de Investigación Tecnológica (IIT) of the same university,
where he has been involved in a number of research
projects related to AGC and power system stability. His interests include control
system design and power systems stability and control.
Fidel Fernández-Bernal was born in Madrid,
Spain, in 1968. He received the B.S., M.S., and
Ph.D. degrees in electrical engineering from the
Universidad Pontificia Comillas de Madrid in 1990,
1994, and 2000, respectively.
From 1988 to 1992, he was working as a Programmer and System Manager in a number of
software companies. From 1990 to 2000, he was
a Lecturer at the Universidad Pontificia Comillas
de Madrid. He is an Associate Professor in the
Department of Electrical Engineering of the School
of Engineering of Universidad Pontificia Comillas. He develops his research
activities at Instituto de Investigación Tecnologica (IIT) of the same university.
Dr. Fernández-Bernal was the recipient of the “Best B.S. degree in Electrical
Engineering in Spain in 1990” Award and the “Honorable Mention” for his M.S.
degree.
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24, NO. 4, NOVEMBER 2009
Pablo Centeno was born in Madrid, Spain, in 1973.
He received the M.S. degree in electrical engineering
from Universidad Pontificia Comillas de Madrid in
1998.
From 2000 to 2005, he worked as an Assistant Researcher at the Instituto de Investigación Tecnológica
(IIT), Universidad Pontificia Comillas, where he carried out several projects involving steady-state and
stability studies including design of load shedding
schemes of the Spanish isolated power systems. From
2005 to 2007, he was with Red Eléctrica de España
(REE), the Spanish TSO. During this period, he also worked as a part-time Lecturer at Universidad Pontifica Comillas. In 2007, he joined Alstom Power Systems, Baden, Switzerland, as a Senior Electrical Engineer. His areas of interest
are modeling, analysis, and simulation of power systems.
Luis Rouco (S’89–M’91) received the Ingeniero Industrial and Doctor Ingeniero Industrial degrees from
Universidad Politécnica de Madrid, Madrid, Spain, in
1985 and 1990, respectively.
He is a Professor of electrical engineering in the
School of Engineering of Universidad Pontificia
Comillas, Madrid, attached to the Department of
Electrical Engineering. He served as Director of the
Department of Electrical Engineering from 1999 to
2005. He develops his research activities at Instituto
de Investigación Tecnologica (IIT) of the same
university, where he has supervised more than 100 research and consultancy
projects for Spanish and foreign companies. He has published more than 70
papers in conferences and journals. He has been a visiting researcher at Ontario
Hydro, Toronto, ON, Canada; the Massachusetts Institute of Technology,
Cambridge; and ABB Power Systems, Vasteras, Sweden. His areas of interest
are modeling, analysis, simulation, and identification of electric power systems.
Prof. Rouco is a member of Cigré, the Vice-President of the Spanish Chapter
of the IEEE Power Engineering Society, and a member of the Executive Committee of Spanish National Committee of Cigré.