Abstract: The deviation between the target state variable and the
practical state variable should be used to form the state tending factor
of complex systems, which can reflect the process for the complex
system to tend rationalization. Relating to the system of basic
equations of complete factor synergetics consisting of twenty
nonlinear stochastic differential equations, the two new models are
considered to set, which should be called respectively the
rationalizing tendency model and the non- rationalizing tendency
model. Therefore we can extend the theory of programming with the
objective function & constraint condition suitable only for the realm
of man-s activities into the new analysis with the tendency function &
constraint condition suitable for all the field of complex system.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.
Abstract: This paper deals with an on-line identification method
of continuous-time Hammerstein systems by using the radial basis
function (RBF) networks and immune algorithm (IA). An unknown
nonlinear static part to be estimated is approximately represented
by the RBF network. The IA is efficiently combined with the
recursive least-squares (RLS) method. The objective function for the
identification is regarded as the antigen. The candidates of the RBF
parameters such as the centers and widths are coded into binary bit
strings as the antibodies and searched by the IA. On the other hand,
the candidates of both the weighting parameters of the RBF network
and the system parameters of the linear dynamic part are updated
by the RLS method. Simulation results are shown to illustrate the
proposed method.
Abstract: This paper proposes an efficient method for the design
of two channel quadrature mirror filter (QMF) bank. To achieve
minimum value of reconstruction error near to perfect reconstruction,
a linear optimization process has been proposed. Prototype low pass
filter has been designed using Kaiser window function. The modified
algorithm has been developed to optimize the reconstruction error
using linear objective function through iteration method. The result
obtained, show that the performance of the proposed algorithm is
better than that of the already exists methods.
Abstract: An enhanced particle swarm optimization algorithm
(PSO) is presented in this work to solve the non-convex OPF
problem that has both discrete and continuous optimization variables.
The objective functions considered are the conventional quadratic
function and the augmented quadratic function. The latter model
presents non-differentiable and non-convex regions that challenge
most gradient-based optimization algorithms. The optimization
variables to be optimized are the generator real power outputs and
voltage magnitudes, discrete transformer tap settings, and discrete
reactive power injections due to capacitor banks. The set of equality
constraints taken into account are the power flow equations while the
inequality ones are the limits of the real and reactive power of the
generators, voltage magnitude at each bus, transformer tap settings,
and capacitor banks reactive power injections. The proposed
algorithm combines PSO with Newton-Raphson algorithm to
minimize the fuel cost function. The IEEE 30-bus system with six
generating units is used to test the proposed algorithm. Several cases
were investigated to test and validate the consistency of detecting
optimal or near optimal solution for each objective. Results are
compared to solutions obtained using sequential quadratic
programming and Genetic Algorithms.
Abstract: The application of a Static Synchronous Series Compensator (SSSC) controller to improve the transient stability performance of a power system is thoroughly investigated in this paper. The design problem of SSSC controller is formulated as an optimization problem and Particle Swarm Optimization (PSO) Technique is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor angle of the generator is involved; transient stability performance of the system is improved. The proposed controller is tested on a weakly connected power system subjected to different severe disturbances. The non-linear simulation results are presented to show the effectiveness of the proposed controller and its ability to provide efficient damping of low frequency oscillations. It is also observed that the proposed SSSC controller improves greatly the voltage profile of the system under severe disturbances.
Abstract: Directional over current relays (DOCR) are commonly used in power system protection as a primary protection in distribution and sub-transmission electrical systems and as a secondary protection in transmission systems. Coordination of protective relays is necessary to obtain selective tripping. In this paper, an approach for efficiency reduction of DOCRs nonlinear optimum coordination (OC) is proposed. This was achieved by modifying the objective function and relaxing several constraints depending on the four constraints classification, non-valid, redundant, pre-obtained and valid constraints. According to this classification, the far end fault effect on the objective function and constraints, and in consequently on relay operating time, was studied. The study was carried out, firstly by taking into account the near-end and far-end faults in DOCRs coordination problem formulation; and then faults very close to the primary relays (nearend faults). The optimal coordination (OC) was achieved by simultaneously optimizing all variables (TDS and Ip) in nonlinear environment by using of Genetic algorithm nonlinear programming techniques. The results application of the above two approaches on 6-bus and 26-bus system verify that the far-end faults consideration on OC problem formulation don-t lose the optimality.
Abstract: In this paper an algorithm is used to detect the color defects of ceramic tiles. First the image of a normal tile is clustered using GCMA; Genetic C-means Clustering Algorithm; those results in best cluster centers. C-means is a common clustering algorithm which optimizes an objective function, based on a measure between data points and the cluster centers in the data space. Here the objective function describes the mean square error. After finding the best centers, each pixel of the image is assigned to the cluster with closest cluster center. Then, the maximum errors of clusters are computed. For each cluster, max error is the maximum distance between its center and all the pixels which belong to it. After computing errors all the pixels of defected tile image are clustered based on the centers obtained from normal tile image in previous stage. Pixels which their distance from their cluster center is more than the maximum error of that cluster are considered as defected pixels.
Abstract: Overcurrent (OC) relays are the major protection
devices in a distribution system. The operating time of the OC relays
are to be coordinated properly to avoid the mal-operation of the
backup relays. The OC relay time coordination in ring fed
distribution networks is a highly constrained optimization problem
which can be stated as a linear programming problem (LPP). The
purpose is to find an optimum relay setting to minimize the time of
operation of relays and at the same time, to keep the relays properly
coordinated to avoid the mal-operation of relays.
This paper presents two phase simplex method for optimum time
coordination of OC relays. The method is based on the simplex
algorithm which is used to find optimum solution of LPP. The
method introduces artificial variables to get an initial basic feasible
solution (IBFS). Artificial variables are removed using iterative
process of first phase which minimizes the auxiliary objective
function. The second phase minimizes the original objective function
and gives the optimum time coordination of OC relays.
Abstract: This paper undertakes the problem of optimal
capacitor placement in a distribution system. The problem is how to
optimally determine the locations to install capacitors, the types and
sizes of capacitors to he installed and, during each load level,the
control settings of these capacitors in order that a desired objective
function is minimized while the load constraints,network constraints
and operational constraints (e.g. voltage profile) at different load
levels are satisfied. The problem is formulated as a combinatorial
optimization problem with a nondifferentiable objective function.
Four solution mythologies based on algorithms (GA),tabu search
(TS), and hybrid GA-SA algorithms are presented.The solution
methodologies are preceded by a sensitivity analysis to select the
candidate capacitor installation locations.
Abstract: The objective of positioning the fixture elements in
the fixture is to make the workpiece stiff, so that geometric errors in
the manufacturing process can be reduced. Most of the work for
optimal fixture layout used the minimization of the sum of the nodal
deflection normal to the surface as objective function. All deflections
in other direction have been neglected. We propose a new method for
fixture layout optimization in this paper, which uses the element
strain energy. The deformations in all the directions have been
considered in this way. The objective function in this method is to
minimize the sum of square of element strain energy. Strain energy
and stiffness are inversely proportional to each other. The
optimization problem is solved by the sequential quadratic
programming method. Three different kinds of case studies are
presented, and results are compared with the method using nodal
deflections as objective function to verify the propose method.
Abstract: A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.
Abstract: This paper presents a method for the optimal
allocation of Distributed generation in distribution systems. In this
paper, our aim would be optimal distributed generation allocation for
voltage profile improvement and loss reduction in distribution
network. Genetic Algorithm (GA) was used as the solving tool,
which referring two determined aim; the problem is defined and
objective function is introduced. Considering to fitness values
sensitivity in genetic algorithm process, there is needed to apply load
flow for decision-making. Load flow algorithm is combined
appropriately with GA, till access to acceptable results of this
operation. We used MATPOWER package for load flow algorithm
and composed it with our Genetic Algorithm. The suggested method
is programmed under MATLAB software and applied ETAP
software for evaluating of results correctness. It was implemented on
part of Tehran electricity distributing grid. The resulting operation of
this method on some testing system is illuminated improvement of
voltage profile and loss reduction indexes.
Abstract: This paper presents optimal based damping controllers of Unified Power Flow Controller (UPFC) for improving the damping power system oscillations. The design problem of UPFC damping controller and system configurations is formulated as an optimization with time domain-based objective function by means of Adaptive Tabu Search (ATS) technique. The UPFC is installed in Single Machine Infinite Bus (SMIB) for the performance analysis of the power system and simulated using MATLAB-s simulink. The simulation results of these studies showed that designed controller has an tremendous capability in damping power system oscillations.
Abstract: Recently, Genetic Algorithms (GA) and Differential
Evolution (DE) algorithm technique have attracted considerable
attention among various modern heuristic optimization techniques.
Since the two approaches are supposed to find a solution to a given
objective function but employ different strategies and computational
effort, it is appropriate to compare their performance. This paper
presents the application and performance comparison of DE and GA
optimization techniques, for flexible ac transmission system
(FACTS)-based controller design. The design objective is to enhance
the power system stability. The design problem of the FACTS-based
controller is formulated as an optimization problem and both the PSO
and GA optimization techniques are employed to search for optimal
controller parameters. The performance of both optimization
techniques has been compared. Further, the optimized controllers are
tested on a weekly connected power system subjected to different
disturbances, and their performance is compared with the
conventional power system stabilizer (CPSS). The eigenvalue
analysis and non-linear simulation results are presented and
compared to show the effectiveness of both the techniques in
designing a FACTS-based controller, to enhance power system
stability.
Abstract: This paper presents an optimal design of poly-phase induction motor using Quadratic Interpolation based Particle Swarm Optimization (QI-PSO). The optimization algorithm considers the efficiency, starting torque and temperature rise as objective function (which are considered separately) and ten performance related items including harmonic current as constraints. The QI-PSO algorithm was implemented on a test motor and the results are compared with the Simulated Annealing (SA) technique, Standard Particle Swarm Optimization (SPSO), and normal design. Some benchmark problems are used for validating QI-PSO. From the test results QI-PSO gave better results and more suitable to motor-s design optimization. Cµ code is used for implementing entire algorithms.
Abstract: The necessity of solving multi dimensional
complicated scientific problems beside the necessity of several
objective functions optimization are the most motive reason of born
of artificial intelligence and heuristic methods.
In this paper, we introduce a new method for multiobjective
optimization based on learning automata. In the proposed method,
search space divides into separate hyper-cubes and each cube is
considered as an action. After gathering of all objective functions
with separate weights, the cumulative function is considered as the
fitness function. By the application of all the cubes to the cumulative
function, we calculate the amount of amplification of each action and
the algorithm continues its way to find the best solutions. In this
Method, a lateral memory is used to gather the significant points of
each iteration of the algorithm. Finally, by considering the
domination factor, pareto front is estimated. Results of several
experiments show the effectiveness of this method in comparison
with genetic algorithm based method.
Abstract: This paper presents an approach for daily optimal operation of distribution networks considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. A genetic algorithm is used to solve the optimal operation problem. The approach is tested on an IEEE34 buses distribution feeder.
Abstract: Mixed Model Production is the practice of assembling
several distinct and different models of a product on the same
assembly line without changeovers and then sequencing those models
in a way that smoothes the demand for upstream components. In this
paper, we consider an objective function which minimizes total
stoppage time and total idle time and it is presented sequence
dependent set up time. Many studies have been done on the mixed
model assembly lines. But in this paper we specifically focused on
reducing the idle times. This is possible through various help policies.
For improving the solutions, some cases developed and about 40 tests
problem was considered. We use scatter search for optimization and
for showing the efficiency of our algorithm, experimental results
shows behavior of method. Scatter search and help policies can
produce high quality answers, so it has been used in this paper.
Abstract: In this paper, an attempt has been made to obtain nonsensitive
solutions in the multi-objective optimization of a
photovoltaic/thermal (PV/T) air collector. The selected objective
functions are overall energy efficiency and exergy efficiency.
Improved thermal, electrical and exergy models are used to calculate
the thermal and electrical parameters, overall energy efficiency,
exergy components and exergy efficiency of a typical PV/T air
collector. A computer simulation program is also developed. The
results of numerical simulation are in good agreement with the
experimental measurements noted in the previous literature. Finally,
multi-objective optimization has been carried out under given
climatic, operating and design parameters. The optimized ranges of
inlet air velocity, duct depth and the objective functions in optimal
Pareto front have been obtained. Furthermore, non-sensitive solutions
from energy or exergy point of view in the results of multi-objective
optimization have been shown.