Introduction
In past few
years, there has been a tremendous increase in the emphasis on power
consumption and cooling of computer systems from both the management and design
aspect. Managing electric power has significant consequences for system
performance and also draws the attention of the computer architecture and
system research communities. Benchmarks in power system are used to develop
models of system behavior and experimentally assess new ideas. A ‘benchmark
system’ consists of a model of the electric power system together with a set of
conventional Power System Stabilizers (PSSs) whose parameters get soundly tuned
according to one of the several techniques that are broadly applied in
practice. Therefore, the benchmark systems offer a basis for evaluating the damping
performance of novel damping controls and tuning approaches to the research
community. Installing PSSs so as to increase the damping element of the
electrical torque of a synchronous generator through the modulation of its
excitation voltage, has been a common practice in the industry for several
decades now, and aims at improving the small-signal stability of interconnected
power systems. There have been proposals of many variants of the phase
compensation designs of the 1970s so as to reduce possible adverse dynamic
effects of this controller at non-electromechanical frequencies. The
developments in robust control theory in the past few years have also resulted
in some of the alternative PSSs, all acting through the automatic voltage
regulator (AVR) as well as the exciter of the synchronous machines. Controllers
with structures apart from the lead-lag phase compensation and applied to FACTS
gadgets have been referred to as Power Oscillation Dampers (PODs) (Hanson, et.
al., 2007).
The performance
of the new controller get verified by digital simulations and compared to that
of a conventional PSS using a test system built primarily to pinpoint the
benefits of the new controller. In such kind of cases, there is a tendency to
over-emphasize the advantages of the new controller as compared to well applied
conventional solutions. Hence, a set of benchmark systems is necessary with
soundly tuned conventional PSSs so as to give a common basis for fairly
assessing the performance of novel damping controllers and PSS tuning
techniques in comparison with conventional methodologies.
One of the areas
where there has been increased attention in the electric power system includes
the focus on power and controlling of computer systems both from design and
management. Similarly, there is also an increasing interest in the
understanding of performance effects of power management actions. A majority of
the power management actions include dynamic voltage and frequency scaling have
performance implications and understating the trade-offs between performance
and power is significant all the system design levels. Power systems do not
always get run at complete capacity, and designers and users require having
knowledge of the power consumption and the performance that they can deliver
across the whole range of load intensity, from zero to peak capacity.
Simultaneously, the benchmarking tests for power and performance give a more
complex measurement problem that has been there for performance benchmarking
only. There must be a correlation between the power and performance data with
each other through time. Simple end-to-end numbers, t transactions consuming j
Joules over s seconds do not give adequate information concerning dynamic
runtime behavior.
The
Benchmark systems and their selection criteria
Single Machine
Infinite Bus (SMIB) test systems have been employed widely in the study of
electromechanical oscillations. The SMIB model considers many of the practical
components linked to the field commissioning and testing of stabilizers, in
which it is not possible to excite interarea modes (Choo, 2015). The
simplifications inherent to the SMIB model, however, limit its applicability to
the research of system-wide (inter-area) oscillations in massively
interconnected power systems. The other critical elements that need a detailed
multi-machine power system representation are 1) the coordination of multiple
controllers to simultaneously damp different modes, and 2) robust damping of
these electromechanical modes for a range of operating conditions.
The requirements
considered in the selection of a power system model as a benchmark system are;
1) The power system should have
multiple machines and demonstrate a combination of local and inter-area modes.
There can also be other kinds of modes including intra-plant modes so as to
reflect practical system conditions in a better way.
2) The power system must be
provided with at least one soundly executed conventional damping control method
whose action leads into a satisfactory damping performance.
3) The system must have got
validated through comparison of its eigenvalues and non-linear time-domain
responses from at least two different simulation software packages.
Real-time
simulation
Since the
mid-twentieth century, there has been a widespread usage of simulation tools
for the design and improvement of electrical systems. A simulation refers to a
representation of the operation or features of a power system through the
utilization or operation of another (Mahanta, et. al., 2015). Simulation tools
are significant in the successful development of a vast number of applications
that include in the layout of transmission lines in large scale power systems
and also in the optimization of motor drives in transportation. The rapid
evolution of simulation tools in the past few years is attributed to the rapid
evolution of computing technologies. As the developments in the computer
technologies have led to decreasing in cost and increase in performance, there
has been an improvement in the capability of simulation tools in addressing
complex challenges in less time. Various digital simulations can be performed
in power systems. In discrete time simulation, the time moves forward in steps
of equal duration commonly referred to as fixed time-step simulation. In
solving mathematical functions and equations at a provided time-step, all the
variables or system states are solved successively as a function of variables
and states at the end of the previous time-step. The amount of real time needed
during a discrete-time simulation in the computation of all equations and
functions representing a power system during a given time-step may be shorter
or longer as compared to the duration of the simulation time-step. Another kind
of simulation is the real-time simulation, in which the accuracy of
computations is dependent upon the precise dynamic representation of the system
as well as also on the length of time used to generate results. The real-time
simulator used for a real –time simulation to be considered valid must
accurately produce the internal variables and outputs of the simulation within
an equal length of time that its physical counterpart would take. The time
required for computation of a solution at given time-step must be shorter when
compared to the wall-clock duration of the time-step. It allows for the
real-time simulator to carry out all functions necessary to make a real-time
simulation relevant, including driving inputs and outputs (I/O) to and from
externally connected gadgets.
Description
of Benchmark System solved using Mat-lab to do solution
The benchmark
system comprises of 39 buses and 10 generators and the damping control issue is
the coordination of multiple stabilizers so as to damp electromechanical modes
within a control area.
All the
electromechanical modes in the system presented above have local or regional
nature, except for one that is observed as the oscillation of generators 2 to
10 against generator 1. The last mode has the lowest frequency. The system does
not present much of a problem from the small-signal stability stabilization
perspective and gets included herein mostly for historical reasons and the sake
of verification of the compatibility of outcomes from different software.
The applied
solution of this scenario was based on a classical tuning method, which
entailed the calculation of the GEP(s) function of all the system generators
and the determination of the PSS gains and phase compensation parameters.
In the modeling
and validation, the dynamic elements of the benchmark system were done
according to the guidelines from IEEE standards. The system model was done on
MATLAB and PacDyn. The eigenvalue analyses obtained from the approaches was
used for validation. The figure below displays the nine electromechanical modes
of the benchmark system.
The 39-bus
system was validated as a reliable matching and is obtained for the other
eigenvalues of the system as well.
Conclusion
The modern power
systems have evolved significantly hence necessitating to a continuous
assessment of new constraints. Simulation tools are essential in designing and
planning of electrical power systems. The use of MATLAB has been found to be a
necessary simulation tool for benchmark systems as it assists in analyzing and
designing the systems.
References
Choo, Y.
C. (2015). Small signal stability analysis for a turbine-generator unit
connected to an HVDC system.
Hanson,
H., Rajamani, K., Rubio, J., Ghiasi, S., & Rawson, F. (2007). Benchmarking
for power and performance. In 2007 SPEC Workshop.
Mahanta,
P. K., Debnath, K., & Rahman, M. H. (2015). Modeling and Simulation of a PV
Module Based Power System Using MATLAB/Simulink. Dhaka University Journal of
Science, 62(2), 127-132.
Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in write my essay online if you need a similar paper you can place your order from write my essay for me services.
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