Abstract: This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.
Abstract: Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.
Abstract: This paper presents an approach for the design of
fuzzy logic power system stabilizers using genetic algorithms. In the
proposed fuzzy expert system, speed deviation and its derivative
have been selected as fuzzy inputs. In this approach the parameters of
the fuzzy logic controllers have been tuned using genetic algorithm.
Incorporation of GA in the design of fuzzy logic power system
stabilizer will add an intelligent dimension to the stabilizer and
significantly reduces computational time in the design process. It is
shown in this paper that the system dynamic performance can be
improved significantly by incorporating a genetic-based searching
mechanism. To demonstrate the robustness of the genetic based
fuzzy logic power system stabilizer (GFLPSS), simulation studies on
multimachine system subjected to small perturbation and three-phase
fault have been carried out. Simulation results show the superiority
and robustness of GA based power system stabilizer as compare to
conventionally tuned controller to enhance system dynamic
performance over a wide range of operating conditions.
Abstract: This paper presents a procedure for modeling and tuning the parameters of Thyristor Controlled Series Compensation (TCSC) controller in a multi-machine power system to improve transient stability. First a simple transfer function model of TCSC controller for stability improvement is developed and the parameters of the proposed controller are optimally tuned. Genetic algorithm (GA) is employed for the optimization of the parameter-constrained nonlinear optimization problem implemented in a simulation environment. By minimizing an objective function in which the oscillatory rotor angle deviations of the generators are involved, transient stability performance of the system is improved. The proposed TCSC controller is tested on a multi-machine system and the simulation results are presented. The nonlinear simulation results validate the effectiveness of proposed approach for transient stability improvement in a multimachine power system installed with a TCSC. The simulation results also show that the proposed TCSC controller is also effective in damping low frequency oscillations.
Abstract: Cow milk, is a product of the mammary gland and
soymilk is a beverage made from soybeans; it is the liquid that
remains after soybeans are soaked. In this research effort, we
compared nutritional parameters of this two kind milk such as total
fat, fiber, protein, minerals (Ca, Fe and P), fatty acids, carbohydrate,
lactose, water, total solids, ash, pH, acidity and calories content in
one cup (245 g). Results showed soymilk contains 4.67 grams of fat,
0.52 of fatty acids, 3.18 of fiber, 6.73 of protein, 4.43 of
carbohydrate, 0.00 of lactose, 228.51 of water, 10.40 of total solids
and 0.66 of ash, also 9.80 milligrams of Ca, 1.42 of Fe, and 120.05 of
P, 79 Kcal of calories, pH=6.74 and acidity was 0.24%. Cow milk
contains 8.15 grams of fat, 5.07 of fatty acids, 0.00 of fiber, 8.02 of
protein, 11.37 of carbohydrate, ´Çá4.27 of lactose, 214.69 of water,
12.90 of total solids, 1.75 of ash, 290.36 milligrams of Ca, 0.12 of
Fe, and 226.92 of P, 150 Kcal of calories, pH=6.90 and acidity was
0.21% . Soy milk is one of plant-based complete proteins and cow
milk is a rich source of nutrients as well. Cow milk is containing near
twice as much fat as and ten times more fatty acids do soymilk. Cow
milk contains greater amounts of mineral (except Fe) it contain more
than three hundred times the amount of Ca and nearly twice the
amount of P as does soymilk but soymilk contains more Fe (ten time
more) than does cow milk. Cow milk and soy milk contain nearly
identical amounts of protein and water and fiber is a big plus, dairy
has none. Although what we choose to drink is really a mater of
personal preference and our health objectives but looking at the
comparison, soy looks like healthier choices.
Abstract: Toxic and bloom-forming cyanobacterium Microcystis
aeruginosa was exposed to antialgal allelochemical gramine (0, 0.5, 1,
2, 4, 8 mg·L-1), The effects of gramine on photosynthetic pigments
(lipid soluble: chlorophyll a and β-carotene; water soluble:
phycocyanin, allophycocyanin, phycoerythrin, and total phycobilins)
and absorption spectra were studied in order to identify the most
sensitive pigment probe implicating the crucial suppression site on
photosynthetic apparatus. The results obtained indicated that all
pigment parameters were decreased with gramine concentration
increasing and exposure time extending. The above serious bleaching
of pigments was also reflected on the scanning results of absorption
spectra. Phycoerytherin exhibited the highest sensitivity to gramine
added, following by the largest relative decrease. It was concluded that
gramine seriously influenced algal photosynthetic activity by
destroying photosynthetic pigments and phycoerythrin most sensitive
to gramine might be contributed to its placing the outside of
phycobilins.
Abstract: Power Factor (PF) is one of the most important parameters in the electrical systems, especially in the water pumping station. The low power factor value of the water pumping stations causes penalty for the electrical bill. There are many methods use for power factor improvement. Each one of them uses a capacitor on the electrical power network. The position of the capacitors is varied depends on many factors such as; voltage level and capacitors rating. Adding capacitors on the motor terminals increase the supply power factor from 0.8 to more than 0.9 but these capacitors cause some problems for the electrical grid network, such as increasing the harmonic contents of the grid line voltage. In this paper the effects of using capacitors in the water pumping stations to improve the power factor value on the harmonic contents of the electrical grid network are studied. One of large water pumping stations in Kafr El-Shikh Governorate in Egypt was used, as a case study. The effect of capacitors on the line voltage harmonic contents is measured. The station uses capacitors to improve the PF values at the 1 lkv grid network. The power supply harmonics values are measured by a power quality analyzer at different loading conditions. The results showed that; the capacitors improved the power factor value of the feeder and its value increased than 0.9. But the THD values are increased by adding these capacitors. The harmonic analysis showed that; the 13th, 17th, and 19th harmonics orders are increased also by adding the capacitors.
Abstract: A new Meta heuristic approach called "Randomized gravitational emulation search algorithm (RGES)" for solving vertex covering problems has been designed. This algorithm is found upon introducing randomization concept along with the two of the four primary parameters -velocity- and -gravity- in physics. A new heuristic operator is introduced in the domain of RGES to maintain feasibility specifically for the vertex covering problem to yield best solutions. The performance of this algorithm has been evaluated on a large set of benchmark problems from OR-library. Computational results showed that the randomized gravitational emulation search algorithm - based heuristic is capable of producing high quality solutions. The performance of this heuristic when compared with other existing heuristic algorithms is found to be excellent in terms of solution quality.
Abstract: The effect of variable chemical reaction on heat and mass transfer characteristics over unsteady stretching surface embedded in a porus medium is studied. The governing time dependent boundary layer equations are transformed into ordinary differential equations containing chemical reaction parameter, unsteadiness parameter, Prandtl number and Schmidt number. These equations have been transformed into a system of first order differential equations. MATHEMATICA has been used to solve this system after obtaining the missed initial conditions. The velocity gradient, temperature, and concentration profiles are computed and discussed in details for various values of the different parameters.
Abstract: In this paper, an automated system is presented for
identification and separation of plastic resins based on near infrared
(NIR) reflectance spectroscopy. For identification and separation
among resins, a "Two-Filter" identification method is proposed that
is capable to distinguish among polyethylene terephthalate (PET),
high density polyethylene (HDPE), polyvinyl chloride (PVC),
polypropylene (PP) and polystyrene (PS). Through surveying effects
of parameters such as surface contamination, sample thickness, label
and cap existence, it was obvious that the "Two-Filter" method has a
high efficiency in identification of resins. It is shown that accurate
identification and separation of five major resins can be obtained
through calculating the relative reflectance at two wavelengths in the
NIR region.
Abstract: Boron minerals are very useful for various industrial
activities, such as glass industry and detergent industry, due to its
mechanical and chemical properties. During the production of boron
compounds, many of these are introduced into the environment in the
form of waste. Boron is also an important micro nutrient for the
plants to vegetate but if it exists in high concentrations, it could have
toxic effects. The maximum boron level in drinking water for human
health is given as 0.3 mg/L in World Health Organization (WHO)
standards. The toxic effects of boron should be noted especially for
dry regions, thus, in recent years, increasing attention has been paid
to remove the boron from waste waters. In this study, boron removal
is implemented by ion exchange process using Amberlite IRA-743
resin. Amberlite IRA-743 resin is a boron specific resin and it
belongs to the polymerizate sorbent group within the aminopolyol
functional group. Batch studies were performed to investigate the
effects of various experimental parameters, such as adsorbent dose,
initial concentration and pH, on the removal of boron. It is found
that, when the adsorbent dose increases removal of boron from the
liquid phase increases. However, an increase in the initial
concentration decreases the removal of boron. The effective pH
values for removal of boron are determined between 8.5 and 9.
Equilibrium isotherms were also analyzed by Langmuir and
Freundlich isotherm models. The Langmuir isotherm is obeyed better
than the Freundlich isotherm.
Abstract: In the process of polyethylene extrusion polymer
material similar to powder or granule is under compression, melting
and transmission operation and on base of special form, extrudate has
been produced. Twin-screw extruders are applicable in industries
because of their high capacity. The powder mixing with chemical
additives and melting with thermal and mechanical energy in three
zones (feed, compression and metering zone) and because of gear
pump and screw's pressure, converting to final product in latest plate.
Extruders with twin-screw and short distance between screws are
better than other types because of their high capacity and good
thermal and mechanical stress.
In this paper, process of polyethylene extrusion and various tapes
of extruders are studied. It is necessary to have an exact control on
process to producing high quality products with safe operation and
optimum energy consumption.
The granule size is depending on granulator motor speed. Results
show at constant feed rate a decrease in granule size was found whit
Increase in motor speed. Relationships between HDPE feed rate and
speed of granulator motor, main motor and gear pump are calculated
following as:
x = HDPE feed flow rate, yM = Main motor speed
yM = (-3.6076e-3) x^4+ (0.24597) x^3+ (-5.49003) x^2+ (64.22092)
x+61.66786 (1)
x = HDPE feed flow rate, yG = Gear pump speed
yG = (-2.4996e-3) x^4+ (0.18018) x^3+ (-4.22794) x^2+ (48.45536)
x+18.78880 (2)
x = HDPE feed flow rate, y = Granulator motor speed
10th Degree Polynomial Fit: y = a+bx+cx^2+dx^3... (3)
a = 1.2751, b = 282.4655, c = -165.2098,
d = 48.3106, e = -8.18715, f = 0.84997
g = -0.056094, h = 0.002358, i = -6.11816e-5
j = 8.919726e-7, k = -5.59050e-9
Abstract: A modified Genetic Algorithm (GA) based optimal selection of parameters for Automatic Generation Control (AGC) of multi-area electric energy systems is proposed in this paper. Simulations on multi-area reheat thermal system with and without consideration of nonlinearity like governor dead band followed by 1% step load perturbation is performed to exemplify the optimum parameter search. In this proposed method, a modified Genetic Algorithm is proposed where one point crossover with modification is employed. Positional dependency in respect of crossing site helps to maintain diversity of search point as well as exploitation of already known optimum value. This makes a trade-off between exploration and exploitation of search space to find global optimum in less number of generations. The proposed GA along with decomposition technique as developed has been used to obtain the optimum megawatt frequency control of multi-area electric energy systems. Time-domain simulations are conducted with trapezoidal integration along with decomposition technique. The superiority of the proposed method over existing one is verified from simulations and comparisons.
Abstract: This paper emphasizes on the application of genetic algorithm (GA) to optimize the parameters of the TMD for achieving the best results in the reduction of the building response under earthquake excitations. The Integral of the Time multiplied Absolute value of the Error (ITAE) based on relative displacement of all floors in the building is taken as a performance index of the optimization criterion. The problem of robustly TMD controller design is formatted as an optimization problem based on the ITAE performance index to be solved using GA that has a story ability to find the most optimistic results. An 11–story realistic building, located in the city of Rasht, Iran is considered as a test system to demonstrate effectiveness of the proposed GA based TMD (GATMD) controller without specifying which mode should be controlled. The results of the proposed GATMD controller are compared with the uncontrolled structure through timedomain simulation and some performance indices. The results analysis reveals that the designed GA based TMD controller has an excellent capability in reduction of the seismically excited example building and the ITAE performance, that is so for remains as unknown, can be introduced a new criteria - method for structural dynamic design.
Abstract: The overall objective of this paper is to retrieve soil
surfaces parameters namely, roughness and soil moisture related to
the dielectric constant by inverting the radar backscattered signal
from natural soil surfaces.
Because the classical description of roughness using statistical
parameters like the correlation length doesn't lead to satisfactory
results to predict radar backscattering, we used a multi-scale
roughness description using the wavelet transform and the Mallat
algorithm. In this description, the surface is considered as a
superposition of a finite number of one-dimensional Gaussian
processes each having a spatial scale. A second step in this study
consisted in adapting a direct model simulating radar backscattering
namely the small perturbation model to this multi-scale surface
description. We investigated the impact of this description on radar
backscattering through a sensitivity analysis of backscattering
coefficient to the multi-scale roughness parameters.
To perform the inversion of the small perturbation multi-scale
scattering model (MLS SPM) we used a multi-layer neural network
architecture trained by backpropagation learning rule. The inversion
leads to satisfactory results with a relative uncertainty of 8%.
Abstract: Efficient and safe plant operation can only be
achieved if the operators are able to monitor all key process
parameters. Instrumentation is used to measure many process
variables, like temperatures, pressures, flow rates, compositions or
other product properties. Therefore Performance monitoring is a
suitable tool for operators. In this paper, we integrate rigorous
simulation model, data reconciliation and parameter estimation to
monitor process equipments and determine key performance
indicator (KPI) of them. The applied method here has been
implemented in two case studies.
Abstract: This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.
Abstract: We constructed a method of noise reduction for
JPEG-compressed image based on Bayesian inference using the
maximizer of the posterior marginal (MPM) estimate. In this method,
we tried the MPM estimate using two kinds of likelihood, both of
which enhance grayscale images converted into the JPEG-compressed
image through the lossy JPEG image compression. One is the
deterministic model of the likelihood and the other is the probabilistic
one expressed by the Gaussian distribution. Then, using the Monte
Carlo simulation for grayscale images, such as the 256-grayscale
standard image “Lena" with 256 × 256 pixels, we examined the
performance of the MPM estimate based on the performance measure
using the mean square error. We clarified that the MPM estimate via
the Gaussian probabilistic model of the likelihood is effective for
reducing noises, such as the blocking artifacts and the mosquito noise,
if we set parameters appropriately. On the other hand, we found that
the MPM estimate via the deterministic model of the likelihood is not
effective for noise reduction due to the low acceptance ratio of the
Metropolis algorithm.
Abstract: Most integrated inertial navigation systems (INS) and
global positioning systems (GPS) have been implemented using the
Kalman filtering technique with its drawbacks related to the need for
predefined INS error model and observability of at least four
satellites. Most recently, a method using a hybrid-adaptive network
based fuzzy inference system (ANFIS) has been proposed which is
trained during the availability of GPS signal to map the error
between the GPS and the INS. Then it will be used to predict the
error of the INS position components during GPS signal blockage.
This paper introduces a genetic optimization algorithm that is used to
update the ANFIS parameters with respect to the INS/GPS error
function used as the objective function to be minimized. The results
demonstrate the advantages of the genetically optimized ANFIS for
INS/GPS integration in comparison with conventional ANFIS
specially in the cases of satellites- outages. Coping with this problem
plays an important role in assessment of the fusion approach in land
navigation.
Abstract: Signature represents an individual characteristic of a
person which can be used for his / her validation. For such application
proper modeling is essential. Here we propose an offline signature
recognition and verification scheme which is based on extraction of
several features including one hybrid set from the input signature
and compare them with the already trained forms. Feature points
are classified using statistical parameters like mean and variance.
The scanned signature is normalized in slant using a very simple
algorithm with an intention to make the system robust which is
found to be very helpful. The slant correction is further aided by the
use of an Artificial Neural Network (ANN). The suggested scheme
discriminates between originals and forged signatures from simple
and random forgeries. The primary objective is to reduce the two
crucial parameters-False Acceptance Rate (FAR) and False Rejection
Rate (FRR) with lesser training time with an intension to make the
system dynamic using a cluster of ANNs forming a multiple classifier
system.