Abstract: This paper presents a systematic approach for the
design of power system stabilizer using genetic algorithm and
investigates the robustness of the GA based PSS. The proposed
approach employs GA search for optimal setting of PSS parameters.
The performance of the proposed GPSS under small and large
disturbances, loading conditions and system parameters is tested.
The eigenvalue analysis and nonlinear simulation results show the
effectiveness of the GPSS to damp out the system oscillations. It is
found tat the dynamic performance with the GPSS shows improved
results, over conventionally tuned PSS over a wide range of
operating conditions.
Abstract: Low frequency power oscillations may be triggered
by many events in the system. Most oscillations are damped by the
system, but undamped oscillations can lead to system collapse.
Oscillations develop as a result of rotor acceleration/deceleration
following a change in active power transfer from a generator. Like
the operations limits, the monitoring of power system oscillating
modes is a relevant aspect of power system operation and control.
Unprevented low-frequency power swings can be cause of cascading
outages that can rapidly extend effect on wide region. On this regard,
a Wide Area Monitoring, Protection and Control Systems
(WAMPCS) help in detecting such phenomena and assess power
system dynamics security. The monitoring of power system
electromechanical oscillations is very important in the frame of
modern power system management and control. In first part, this
paper compares the different technique for identification of power
system oscillations. Second part analyzes possible identification
some power system dynamics behaviors Using Wide Area
Monitoring Systems (WAMS) based on Phasor Measurement Units
(PMUs) and wavelet technique.
Abstract: The development of the power electronics has allowed
increasing the precision and reliability of the electrical devices, thanks
to the adjustable inverters, as the Pulse Wide Modulation (PWM)
applied to the three level inverters, which is the object of this study.
The authors treat the relation between the law order adopted for a
given system and the oscillations of the electrical and mechanical
parameters of which the tolerance depends on the process with which
they are integrated (paper factory, lifting of the heavy loads,
etc.).Thus, the best choice of the regulation indexes allows us to
achieve stability and safety training without investment (management
of existing equipment). The optimal behavior of any electric device
can be achieved by the minimization of the stored electrical and
mechanical energy.
Abstract: Saturated two-phase fluid flows are often subject to
pressure induced oscillations. Due to compressibility the vapor
bubbles act as a spring with an asymmetric non-linear characteristic.
The volume of the vapor bubbles increases or decreases differently if
the pressure fluctuations are compressing or expanding;
consequently, compressing pressure fluctuations in a two-phase pipe
flow cause less displacement in the direction of the pipe flow than
expanding pressure fluctuations. The displacement depends on the
ratio of liquid to vapor, the ratio of pressure fluctuations over average
pressure and on the exciting frequency of the pressure fluctuations.
In addition, pressure fluctuations in saturated vapor bubbles cause
condensation and evaporation within the bubbles and change
periodically the ratio between liquid to vapor, and influence the
dynamical parameters for the oscillation. The oscillations are
conforming to an isenthalpic process at constant enthalpy with no
heat transfer and no exchange of work.
The paper describes the governing non-linear equation for twophase
fluid oscillations with condensation and evaporation, and
presents steady state approximate solutions for free and for pressure
induced oscillations. Resonance criteria and stability are discussed.
Abstract: This paper aims to select the optimal location and
setting parameters of TCSC (Thyristor Controlled Series
Compensator) controller using Particle Swarm Optimization (PSO)
and Genetic Algorithm (GA) to mitigate small signal oscillations in a
multimachine power system. Though Power System Stabilizers
(PSSs) are prime choice in this issue, installation of FACTS device
has been suggested here in order to achieve appreciable damping of
system oscillations. However, performance of any FACTS devices
highly depends upon its parameters and suitable location in the
power network. In this paper PSO as well as GA based techniques are
used separately and compared their performances to investigate this
problem. The results of small signal stability analysis have been
represented employing eigenvalue as well as time domain response in
face of two common power system disturbances e.g., varying load
and transmission line outage. It has been revealed that the PSO based
TCSC controller is more effective than GA based controller even
during critical loading condition.
Abstract: Small signal stability causes small perturbations in the
generator that can cause instability in the power network. It is
generally known that small signal stability are directly related to the
generator and load properties. This paper examines the effects of
generator input variations on power system oscillations for a small
signal stability study. Eigenvaules and eigenvectors are used to
examine the stability of the power system. The dynamic power
system's mathematical model is constructed and thus calculated using
load flow and small signal stability toolbox on MATLAB. The power
system model is based on a 3-machine 9-bus system that was
modified to suit this study. In this paper, Participation Factors are a
means to gauge the effects of variation in generation with other
parameters on the network are also incorporated.
Abstract: Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, particle swarm optimization (PSO) technique is applied to design a robust power system stabilizer (PSS). The design problem of the proposed controller is formulated as an optimization problem and PSO is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions; disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations. Further, all the simulations results are compared with a conventionally designed power system stabilizer to show the superiority of the proposed design approach.
Abstract: The MFCAV Riemann solver is practically used in many Lagrangian or ALE methods due to its merit of sharp shock profiles and rarefaction corners, though very often with numerical oscillations. By viewing it as a modification of the WWAM Riemann solver, we apply the MFCAV Riemann solver to the Lagrangian method recently developed by Maire. P. H et. al.. The numerical experiments show that the application is successful in that the shock profiles and rarefaction corners are sharpened compared with results obtained using other Riemann solvers. Though there are still numerical oscillations, they are within the range of the MFCAV applied in onther Lagrangian methods.
Abstract: The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.
Abstract: The incidences of dengue hemorrhagic disease (DHF)
over the long term exhibit a seasonal behavior. It has been
hypothesized that these behaviors are due to the seasonal climate
changes which in turn induce a seasonal variation in the incubation
period of the virus while it is developing the mosquito. The standard
dynamic analysis is applied for analysis the Susceptible-Exposed-
Infectious-Recovered (SEIR) model which includes an annual
variation in the length of the extrinsic incubation period (EIP). The
presence of both asymptomatic and symptomatic infections is
allowed in the present model. We found that dynamic behavior of the
endemic state changes as the influence of the seasonal variation of
the EIP becomes stronger. As the influence is further increased, the
trajectory exhibits sustained oscillations when it leaves the chaotic
region.
Abstract: The statistical distributions are modeled in explaining
nature of various types of data sets. Although these distributions are
mostly uni-modal, it is quite common to see multiple modes in the
observed distribution of the underlying variables, which make the
precise modeling unrealistic. The observed data do not exhibit
smoothness not necessarily due to randomness, but could also be due
to non-randomness resulting in zigzag curves, oscillations, humps
etc. The present paper argues that trigonometric functions, which
have not been used in probability functions of distributions so far,
have the potential to take care of this, if incorporated in the
distribution appropriately. A simple distribution (named as, Sinoform
Distribution), involving trigonometric functions, is illustrated in the
paper with a data set. The importance of trigonometric functions is
demonstrated in the paper, which have the characteristics to make
statistical distributions exotic. It is possible to have multiple modes,
oscillations and zigzag curves in the density, which could be suitable
to explain the underlying nature of select data set.
Abstract: This paper presents a novel approach for tuning unified power flow controller (UPFC) based damping controller in order to enhance the damping of power system low frequency oscillations. The design problem of damping controller is formulated as an optimization problem according to the eigenvalue-based objective function which is solved using iteration particle swarm optimization (IPSO). The effectiveness of the proposed controller is demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies under a wide range of loading conditions. The simulation study shows that the designed controller by IPSO performs better than CPSO in finding the solution. Moreover, the system performance analysis under different operating conditions show that the δE based controller is superior to the mB based controller.
Abstract: It is well known that a linear dynamic system including
a delay will exhibit limit cycle oscillations when a bang-bang sensor
is used in the feedback loop of a PID controller. A similar behaviour
occurs when a delayed feedback signal is used to train a neural
network. This paper develops a method of predicting this behaviour
by linearizing the system, which can be shown to behave in a manner
similar to an integral controller. Using this procedure, it is possible
to predict the characteristics of the neural network driven limit cycle
to varying degrees of accuracy, depending on the information known
about the system. An application is also presented: the intelligent
control of a spark ignition engine.
Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: The present study concentrates on solving the along wind oscillation problem of a tall square building from first principles and across wind oscillation problem of the same from empirical relations obtained by experiments. The criterion for human comfort at the worst condition at the top floor of the building is being considered and a limiting value of height of a building for a given cross section is predicted. Numerical integrations are carried out as and when required. The results show severeness of across wind oscillations in comparison to along wind oscillation. The comfort criterion is combined with across wind oscillation results to determine the maximum allowable height of a building for a given square cross-section.
Abstract: Flows over a harmonically oscillating NACA 0012
airfoil are simulated here using a two-dimensional, unsteady,
incompressibleNavier-Stokes solver.Both pure-plunging and
pitching-plunging combined oscillations are considered at a Reynolds
number of 5000. Special attention is paid to the vortex shedding and
interaction mechanism of the motions. For all the simulations
presented here, the reduced frequency (k) is fixed at a value of 2.5
and plunging amplitude (h) is selected to be in the range of 0.2-0.5.
The simulation results show that the interaction mechanism between
the leading and trailing edge vortices has a decisive effect on the
values of the resulting thrust and propulsive efficiency.
Abstract: The main objective of this paper is a comparative
investigate in enhancement of damping power system oscillation via
coordinated design of the power system stabilizer (PSS) and static
synchronous series compensator (SSSC) and static synchronous
compensator (STATCOM). The design problem of FACTS-based
stabilizers is formulated as a GA based optimization problem. In this
paper eigenvalue analysis method is used on small signal stability of
single machine infinite bus (SMIB) system installed with SSSC and
STATCOM. The generator is equipped with a PSS. The proposed
stabilizers are tested on a weakly connected power system with
different disturbances and loading conditions. This aim is to enhance
both rotor angle and power system stability. The eigenvalue analysis
and non-linear simulation results are presented to show the effects of
these FACTS-based stabilizers and reveal that SSSC exhibits the best
effectiveness on damping power system oscillation.
Abstract: The three-dimensional incompressible flow past a
rectangular open cavity is investigated, where the aspect ratio of the
cavity is considered as 4. The principle objective is to use large-eddy
simulation to resolve and control the large-scale structures, which are
largely responsible for flow oscillations in a cavity. The flow past an
open cavity is very common in aerospace applications and can be a
cause of acoustic source due to hydrodynamic instability of the shear
layer and its interactions with the downstream edge. The unsteady
Navier-stokes equations have been solved on a staggered mesh using
a symmetry-preserving central difference scheme. Synthetic jet has
been used as an active control to suppress the cavity oscillations in
wake mode for a Reynolds number of ReD = 3360. The effect of
synthetic jet has been studied by varying the jet amplitude and
frequency, which is placed at the upstream wall of the cavity. The
study indicates that there exits a frequency band, which is larger than
a critical value, is effective in attenuating cavity oscillations when
blowing ratio is more than 1.0.
Abstract: Static Var Compensator (SVC) is a shunt type FACTS
device which is used in power system primarily for the purpose of
voltage and reactive power control. In this paper, a fuzzy logic based
supplementary controller for Static Var Compensator (SVC) is
developed which is used for damping the rotor angle oscillations and
to improve the transient stability of the power system. Generator
speed and the electrical power are chosen as input signals for the
Fuzzy Logic Controller (FLC). The effectiveness and feasibility of
the proposed control is demonstrated with Single Machine Infinite
Bus (SMIB) system and multimachine system (WSCC System)
which show improvement over the use of a fixed parameter
controller.
Abstract: Many studies have focused on the nonlinear analysis
of electroencephalography (EEG) mainly for the characterization of
epileptic brain states. It is assumed that at least two states of the
epileptic brain are possible: the interictal state characterized by a
normal apparently random, steady-state EEG ongoing activity; and
the ictal state that is characterized by paroxysmal occurrence of
synchronous oscillations and is generally called in neurology, a
seizure.
The spatial and temporal dynamics of the epileptogenic process is
still not clear completely especially the most challenging aspects of
epileptology which is the anticipation of the seizure. Despite all the
efforts we still don-t know how and when and why the seizure
occurs. However actual studies bring strong evidence that the
interictal-ictal state transition is not an abrupt phenomena. Findings
also indicate that it is possible to detect a preseizure phase.
Our approach is to use the neural network tool to detect interictal
states and to predict from those states the upcoming seizure ( ictal
state). Analysis of the EEG signal based on neural networks is used
for the classification of EEG as either seizure or non-seizure. By
applying prediction methods it will be possible to predict the
upcoming seizure from non-seizure EEG.
We will study the patients admitted to the epilepsy monitoring
unit for the purpose of recording their seizures. Preictal, ictal, and
post ictal EEG recordings are available on such patients for analysis
The system will be induced by taking a body of samples then
validate it using another. Distinct from the two first ones a third body
of samples is taken to test the network for the achievement of
optimum prediction. Several methods will be tried 'Backpropagation
ANN' and 'RBF'.