Showing 50 results for Optimization
M. R. Mosavi, A. Rashidinia,
Volume 13, Issue 3 (9-2017)
Abstract
Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Function (RBF) has been developed. In many previous works all parameter of RBF NN are optimizing by evolutionary algorithm such as Particle Swarm Optimization (PSO), but in our approach shape parameter and centers of RBF NN are calculated in better way, in addition, search space for PSO algorithm will be reduced which cause more accurate and faster approach. The obtained results show that RMS has been reduced about 0.13 meter. Moreover, results are tabulated in the tables which verify the accuracy and faster convergence nature of our approach in both on-line and off-line training methods.
R. Pour Ebrahim, S. Tohidi, A. Younesi,
Volume 14, Issue 1 (3-2018)
Abstract
In this paper, a new sensorless model reference adaptive method is used for direct control of active and reactive power of the doubly fed induction generator (DFIG). In order to estimate the rotor speed, a high frequency signal injection scheme is implemented. In this study, to improve the accuracy of speed estimation, two methods are suggested. First, the coefficients of proportional-integral (PI) blocks are optimized by using Krill Herd algorithm. In the second method, the fuzzy logic control method is applied in the estimator structure instead of PI controllers. The simulation results for the proposed methods illustrate that the estimated speed perfectly matches the actual speed of the DFIG. In addition, the desired slip value is achieved due to the accurate response. On the other hand, the active and reactive power responses have fast dynamics and relatively low oscillations. Moreover, the fuzzy controller shows more robustness against the variations of machine parameters.
E. Babaei, M. Shadnam Zarbil, E. Shokati Asl,
Volume 15, Issue 1 (3-2019)
Abstract
In this paper, a new topology for cascaded multilevel inverter based on quasi-Z-source converter is proposed. In the proposed topology the magnitude of output DC voltage is not limited to the sum of magnitude of DC voltage sources. Moreover, the reliability of the circuit due to capability of short circuit by Z-source network is increased. The quasi-Z- source converter in different modes is analyzed and the voltage gain is obtained. Also, the values of quasi-Z-source network components are designed. In the proposed topology, the number of DC voltage sources, the number of switches, installation area and cost in comparison with conventional multilevel inverters are significantly reduced. Three algorithms to determine the magnitude of DC voltage sources are proposed. Then the optimal structures for the minimum number of switches and DC voltage sources to generate the maximum voltage levels are presented. Moreover, the control method for the proposed topology is described. To verify the performance of the proposed topology, simulation and experimental results of proposed topology are presented.
M. A. Trimukhe, B. G. Hogade,
Volume 15, Issue 2 (6-2019)
Abstract
In this paper a particle swarm optimization (PSO) algorithm is presented to design a compact stepped triangle shape antenna in order to obtain the proper UWB bandwidth as defined by FCC. By changing the various cavity dimensions of the antenna, data to develop PSO program in MATLAB is achieved. The results obtained from the PSO algorithm are applied to the antenna design to fine-tune the bandwidth. Bandwidth optimization for ultra-wideband frequency of 3.1 GHz to 10.6 GHz is achieved by applying PSO algorithm. High-Frequency Structure Simulator (HFSS) software tool is used for the simulation. An optimized antenna is fabricated, tested and test results are found in accordance with simulation results.
S. Mavaddati,
Volume 15, Issue 3 (9-2019)
Abstract
Blind voice separation refers to retrieve a set of independent sources combined by an unknown destructive system. The proposed separation procedure is based on processing of the observed sources without having any information about the combinational model or statistics of the source signals. Also, the number of combined sources is usually predefined and it is difficult to estimate based on the combined sources. In this paper, a new algorithm is introduced to resolve these issues using empirical mode decomposition technique as a pre-processing step. The proposed method can determine precisely the number of mixed voice signals based on the energy and kurtosis criteria of the captured intrinsic mode functions. Also, the separation procedure employs a grey wolf optimization algorithm with a new cost function in the optimization procedure. The experimental results show that the proposed separation algorithm performs prominently better than the earlier methods in this context. Moreover, the simulation results in the presence of white noise emphasize the proper performance of the presented method and the prominent role of the presented cost function especially when the number of sources is high.
M. S. Hosseini, H. Javadi, S. Vaez-Zadeh,
Volume 16, Issue 1 (3-2020)
Abstract
Linear flux switching motors with simple passive segmented secondary, referred as Segmented Secondary Linear Flux Switching Motors (SSLFSMs), have low cost secondary and therefore are applicable to transportation systems like Maglev. However, it is shown that the SSLFSMs suffer from high thrust ripples. In this paper, minimizing SSLFSM thrust ripples besides maximizing its developed thrust are performed by considering the motor dimensions as design variables. Since the optimization of the motor is a high dimensional problem, a multi-level optimization method is employed to improve the machine performances and efficiency. According to the effects of the design variables on the optimization objectives, a sensitivity analysis is carried out to divide the design variables into two levels: mild-sensitive level and strong-sensitive level. Then, the two levels of design variables are optimized based on a mathematical model. Two different optimization methods as the Design of Experiment (DOE) and the Response Surface Method (RSM) are used in mild-sensitive level and the Genetic Algorithm (GA) is also used in strong-sensitive level. Based on FEM analysis, electromagnetic performance of the original motor and the optimal one are compared and the validity of the proposed optimization method is verified. Also, the effectiveness of the mathematical model used in thrust and thrust ripples calculations is evaluated and verified.
A. Nobahari, M. R. Mosavi, A. Vahedi,
Volume 16, Issue 1 (3-2020)
Abstract
A methodology is proposed for optimal shaping of permanent magnets with non-conventional and complex geometries, used in synchronous motors. The algorithm includes artificial neural network-based surrogate model and multi-objective search based optimization method that will lead to Pareto front solutions. An interior permanent magnet topology with crescent-shaped magnets is also introduced as the case study, on which the proposed optimal shaping methodology is applied. Produced torque per magnets mass and percentage torque ripple are considered as the objectives, in order to take both performance and cost into account. Multi-layer perceptron architecture used to create the approximated model is trained to fit the samples collected via time-stepping finite element simulations. The methodology can be easily generalized to offer a fast and accurate method to optimally define arbitrary permanent magnet shape parameters in various synchronous motors.
D. Jamunaa, G. K. Mahanti, F. N. Hasoon,
Volume 16, Issue 2 (6-2020)
Abstract
This paper describes the synthesis of digitally excited pencil/flat top dual beams simultaneously in a linear antenna array constructed of isotropic elements. The objective is to generate a pencil/flat top beam pair using the excitations generated by the evolutionary algorithms. Both the beams share common variable discrete amplitude excitations and differ in variable discrete phase excitations. This synthesis is treated as a multi-objective optimization problem and is handled by Quantum Particle Swarm Optimization algorithm duly controlling the fitness functions. These functions include many of the radiation pattern parameters like side lobe level, half power beam width and beam width at the side lobe level in both the beams along with the ripple in the flat top band of flat top beam. In addition to it, the dynamic range ratio of the amplitudes excitations is set below a certain level to diminish the mutual coupling effects in the array. Two sets of experiments are conducted and the effectiveness of this algorithm is proved by comparing it with various versions of swarm optimization algorithms.
M. Khalaj-Amirhosseini, M. Nadi-Abiz,
Volume 16, Issue 2 (6-2020)
Abstract
Phase Perturbation Method (PPM) is introduced as a new phase-only synthesis method to design reflectarray antennas so as their sidelobe level is reduced. In this method, only the reflected phase of conventional unit cells are perturbed from their required values. To this end, two approaches namely the conventional Optimization method and newly introduced Phase to Amplitude Approximation (PAA) method are proposed. Finally, a reflectarray antenna is designed and fabricated to have a low sidelobe level and its performance is investigated.
S. M. Ejabati, S. H. Zahiri,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the number of algorithm individuals and the creation of feasible subspaces appropriate to environmental conditions. Accordingly, to prevent early convergence along with the increasing speed of local search, the search space is divided with respect to the conditions of each moment into subspaces labeled as focused search area, and focused individuals are recruited to make search for it. Moreover, the structure of the design is in such a way that it often adapts itself to environmental condition, and there is no need to identify any change in the environment. The framework proposed for particle swarm optimization algorithm has been implemented as one of the most notable static optimization and a new optimization method referred to as ant lion optimizer. The results from moving peak benchmarks (MPB) indicated the good performance of the proposed framework for dynamic optimization. Furthermore, the positive performance of practices was assessed with respect to real-world issues, including clustering for dynamic data.
N. Sayyadi Shahraki, S. H. Zahiri,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new and comprehensive proposed criterion, and also meet different design specifications such as DC gain, Gain-Band Width product (GBW), Phase Margin (PM), Slew Rate (SR), Common Mode Rejection Ratio (CMRR), Power Supply Rejection Ratio (PSRR), etc. The proposed MOLA contains several automata and each automaton is responsible for searching one dimension. The workability of the proposed approach is evaluated in comparison with the most well-known category of intelligent meta-heuristic Multi-Objective Optimization (MOO) methods such as Particle Swarm Optimization (PSO), Inclined Planes system Optimization (IPO), Gray Wolf Optimization (GWO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The performance of the proposed MOLA is demonstrated in finding optimal Pareto fronts with two criteria Overall Non-dominated Vector Generation (ONVG) and Spacing (SP). In simulations, for the desired application, it has been shown through Computer-Aided Design (CAD) tool that MOLA-based solutions produce better results.
A. N. Patel, B. N. Suthar,
Volume 16, Issue 3 (9-2020)
Abstract
Optimization of specific power of axial flux permanent magnet brushless DC (PMBLDC) motor based on genetic algorithm optimization technique for an electric vehicle application is presented. Double rotor sandwiched stator topology of axial flux permanent magnet brushless DC motor is selected considering its best suitability in electric vehicle applications. Rating of electric motor is determined based on vehicular dynamics and application needs. Double rotor sandwiched stator axial flux PMBLDC motor is designed considering various assumed design variables. Initially designed axial flux PMBLDC motor is considered as a reference motor for further analysis. Optimization of the specific power of electric motor for electric vehicle applications is a very important design issue. The Genetic Algorithm (GA) based optimization technique is proposed for optimization of specific power of axial flux permanent magnet brushless DC motor. Optimization with an objective of maximum specific power with the same torque rating is performed. Three-dimensional finite element analysis is performed to validate the proposed GA based specific power optimization. Close agreement between results obtained from finite element analysis and analytical design establishes the correctness of the proposed optimization technique. The performance of the improved motor is compared with the initially designed reference motor. It is analyzed that the specific power of axial flux PMBLDC motor is enhanced effectively with the application of GA based design optimization technique.
A. Bahmanyar, H. Borhani-Bahabadi, S. Jamali,
Volume 16, Issue 3 (9-2020)
Abstract
To realize the self-healing concept of smart grids, an accurate and reliable fault locator is a prerequisite. This paper presents a new fault location method for active power distribution networks which is based on measured voltage sag and use of whale optimization algorithm (WOA). The fault induced voltage sag depends on the fault location and resistance. Therefore, the fault location can be found by investigation of voltage sags recorded throughout the distribution network. However, this approach requires a considerable effort to check all possible fault location and resistance values to find the correct solution. In this paper, an improved version of the WOA is proposed to find the fault location as an optimization problem. This optimization technique employs a number of agents (whales) to search for a bunch of fish in the optimal position, i.e. the fault location and its resistance. The method is applicable to different distribution network configurations. The accuracy of the method is verified by simulation tests on a distribution feeder and comparative analysis with two other deterministic methods reported in the literature. The simulation results indicate that the proposed optimized method gives more accurate and reliable results.
A. Rajabi, H. Lexian,
Volume 17, Issue 1 (3-2021)
Abstract
One of the important requirements in projectiles is to design a power supply for fuse consumption. In this study, an optimum design for the power supply, which includes a Miniaturized Inertia Generator (MIG), was introduced. The main objective of this research was to optimize the dimensions of the MIG with the aim of increasing energy. To achieve this, the design of experiment (DOE) was carried out through RSM-BBD to optimize six parts of the MIG. Numerical simulations were performed using Maxwell’s software. After analyzing of results by ANOVA and extracting the optimum result from the RSM, a Miniaturized Inertia Generator was fabricated with optimum dimensions. The results showed that the MIG with optimum dimensions at an acceleration of 800’g could generate 15.25V and stores the generated energy using an RLC circuit within 1ms. The experimental results which were obtained by the shock test system showed that 14.75V was charged on a capacitor within 1.1ms which has good conformity with the numerical results. The results indicated that the proposed design not only increased the MIG efficiency, but also determined the effect of each parameter on the produced energy and efficiency.
A. Jabbari, F. Dubas,
Volume 17, Issue 2 (6-2021)
Abstract
In this paper, we present a mathematical model for determining the optimal radius of the iron pole shape in spoke-type permanent-magnet (PM) machines (STPMM) in order to minimize the pulsating torque components. The proposed method is based on the formal resolution of the Laplace’s and Poisson’s equations in a Cartesian pseudo-coordinate system with respect to the relative permeability effect of iron core in a subdomain model. The effect of PM width on the optimal radius of the iron pole has been investigated. In addition, for initial and optimal machines, the effect of the iron core relative permeability on the STPMM performances was studied at no-load and on-load conditions considering three certain PM widths. Moreover, the effect of iron pole shape on pulsating torque components with respect to certain values of iron core relative permeability was studied by comparing cogging torque, ripple and reluctance torque waveforms. In order to validate the results of the proposed analytical model, three motors with different PM widths were considered as case studies and their performance results were compared analytically and numerically. Two prototype spoke-type machines were fabricated and the experimental results were compared to analytical results. It can be seen that the analytical modeling results are consistent with the numerical analysis and experimental results.
M. Ahmadinia, J. Sadeh,
Volume 17, Issue 4 (12-2021)
Abstract
In this paper, an accurate fault location scheme based on phasor measurement unit (PMU) is proposed for shunt-compensated transmission lines. It is assumed that the voltage and current phasors on both sides of the shunt-compensated line have been provided by PMUs. In the proposed method, the faulted section is determined by presenting the absolute difference of positive- (or negative-) sequence current angles index, firstly. After determining faulted section, the voltage phasor at the shunt-compensator terminal is estimated via the sound section. The faulted section can be assumed as a perfect transmission line that synchronized voltage and current phasors at one end and voltage phasor at the other end are available. Secondly, a new fault location algorithm is presented to locate the precise fault point in the faulted section. In this algorithm, the location of the fault and the fault resistance are calculated simultaneously by solving an optimization problem, utilizing the heuristic Particle Swarm Optimization (PSO) method. The simulation results in MATLAB/SIMULINK platform demonstrate the high performance of the proposed method in finding the fault location in shunt-compensated transmission lines. The proposed scheme has high accuracy for both symmetrical and asymmetrical fault types and high fault resistance.
A. Mansoori, A. Sheikhi Fini, M. Parsa Moghaddam,
Volume 18, Issue 1 (3-2022)
Abstract
In recent years, the increasing of non-dispatchable resources has posed severe challenges to the operation planning of power systems. Since these resources are random in nature, the issue of flexibility to cover their uncertainty and variability has become an important research topic. Therefore, having flexible resources to cover changes in the generation of these resources during their operation can play an essential role in eliminating node imbalances, system reliability, providing the required flexible ramping capacity, and reducing system operating costs. Among flexibility resources, there are quick-act generation units such as gas units that can play an important role in covering net load changes. Also, on the demand side, the optimal design of demand response programs as responsive resources to price and incentive signals, by modifying the system load factor can prevent severe ramps at net load, especially during peak load hours, and as a result, increase system flexibility while decreasing operational cost of the power system. In this paper, unlike the existing literature, the effect of the mentioned flexibility resources (both on the generation side and the demand side) in day-ahead operation planning under high penetration of wind generation units has been studied on the IEEE RTS 24-bus test system. Also, for this scheduling, a mixed-integer, two-stage, and tri-level adaptive robust optimization have been used, which is solved by column-and-constraint generation decomposition-based algorithm to clear the energy and ramping capacity reserve jointly.
A. Saffari, S. H. Zahiri, M. Khishe,
Volume 18, Issue 1 (3-2022)
Abstract
In this paper, multilayer perceptron neural network (MLP-NN) training is used by the grasshopper optimization algorithm with the tuning of control parameters using a fuzzy system for the big data sonar classification problem. With proper tuning of these parameters, the two stages of exploration and exploitation are balanced, and the boundary between them is determined correctly. Therefore, the algorithm does not get stuck in the local optimization, and the degree of convergence increases. So the main aim is to get a set of real sonar data and then classify real sonar targets from unrealistic targets, including noise, clutter, and reverberation, using GOA-trained MLP-NN developed by the fuzzy system. To have accurate comparisons and prove the GOA performance developed with fuzzy logic (called FGOA), nine benchmark algorithms GOA, GA, PSO, GSA, GWO, BBO, PBIL, ES, ACO, and the standard backpropagation (BP) algorithm were used. The measured criteria are concurrency speed, ability to avoid local optimization, and accuracy. The results show that FGOA has the best performance for training datasets and generalized datasets with 96.43% and 92.03% accuracy, respectively.
T. Barforoushi, R. Heydari,
Volume 18, Issue 2 (6-2022)
Abstract
Curtailment of the production of wind resources due to uncertainty can affect the expansion of the transmission networks. The issue that needs to be addressed is how to expand the transmission network, which is accompanied by increasing wind energy utilization. In this paper, a new framework is proposed to solve the transmission expansion planning (TEP) problem in the presence of wind farms, considering wind curtailment cost. The proposed model is a risk-constrained stochastic bi-level problem that, the difference between the expected social welfare and investment cost is maximized at the upper level where optimal decisions on expansion plans are adopted by the independent system operator (ISO). To make the best use of wind generation resources, a new term called wind power curtailment cost is added to the upper level. Also, the risk index is included in expansion decisions. The market-clearing is considered at the lower level, aiming at maximizing social welfare. Uncertainties relating to wind power and the forecasted demand are modeled by sets of scenarios. Using duality theory, the proposed framework is modeled as mixed-integer linear programming (MILP) problem. The model is examined using the classical Garver’s six-bus test system and the IEEE 24-bus reliability test system (RTS). The results show that by considering the wind curtailment cost, the transmission network is expanded in a way that increases the wind energy utilization factor from 92.05% to 95.17%.
M. Soruri, S. M. Razavi, M. Forouzanfar,
Volume 18, Issue 3 (9-2022)
Abstract
Power amplifier is one of the main components in the RF transmitters. It must provide various stringent features that can lead to complicating the design. In this paper, a new optimizing method based on the inclined planes system optimization algorithm is presented for the design of a discrete power amplifier. It is evaluated in a 2.4-3 GHz power amplifier, which is designed based on “Cree’s CGH40010F GaN HEMT”. The optimization goals are input and output return losses, Power Added Efficiency, and Gain. Large signal simulation of the optimized power amplifier shows a good performance across the bandwidth. In this frequency range, the input and output return losses are about lower than -10 dB, the Power Added Efficiency is greater than 51%, while the Gain is higher than 13.5 dB. A two-tone test with a frequency space of 1 MHz is applied for the linearity evaluation of the designed power amplifier. The obtained result shows that the power amplifier has good linearity with a low memory effect.
