Showing 11 results for Particle Swarm Optimization
C. Lucas, F. Tootoonchian, Z. Nasiri-Gheidari,
Volume 6, Issue 3 (9-2010)
Abstract
In this paper a brushless permanent magnet motor is designed considering
minimum thrust ripple and maximum thrust density (the ratio of the thrust to permanent
magnet volumes). Particle Swarm Optimization (PSO) is used as optimization method. Finite
element analysis (FEA) is carried out base on the optimized and conventional geometric
dimensions of the motor. The results of the FEA deal to the significant improvement of the all
objective functions.
C. Lucas , Z. Nasiri-Gheidari , F. Tootoonchian,
Volume 6, Issue 4 (12-2010)
Abstract
In this paper particle swarm optimization (PSO) is used for a design optimization of a linear permanent magnet synchronous motor (LPMSM) considering ultra low thrust force ripples, low magnet consumption, improved efficiency and thrust. The influence of PM material is discussed, too and the modular poles are proposed to achieve the best characteristic. PM dimensions and material, air gap and motor width are chosen as design variables. Finally 2-D finite element analyses validate the optimization results.
M. Barati, A. R. Khoogar, M. Nasirian,
Volume 7, Issue 4 (12-2011)
Abstract
Abstract: Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually associated with errors and accurate measurement of them is an expensive task, automatic calibration of robot link parameters makes the task of kinematics parameters determination much easier. In this paper a simple and easy to use algorithm is introduced for correction and calibration of robot kinematics parameters. Actually at several end-effecter positions, the joint variables are measured simultaneously. This information is then used in two different algorithms least square (LS) and Genetic algorithm (GA) for automatic calibration and correction of the kinematics parameters. This process was also tested experimentally via a three degree of freedom manipulator which is actually used as a coordinate measuring machine (CMM). The experimental Results prove that the Genetic algorithms are better for both parameter identification and calibration of link parameters.
R Subramanian, K Thanushkodi, A Prakash,
Volume 9, Issue 4 (12-2013)
Abstract
The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA), Differential Evolution (DE), Particle swarm optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.
H. Hasanzadeh Fard, S. A. Bahreyni , R. Dashti , H. A. Shayanfar,
Volume 11, Issue 2 (6-2015)
Abstract
Evaluation of the reliability parameters in micro-grids based on renewable energy sources is one of the main problems that are investigated in this paper. Renewable energy sources such as solar and wind energy, battery as an energy storage system and fuel cell as a backup system are used to provide power to the electrical loads of the micro-grid. Loads in the micro-grid consist of interruptible and uninterruptible loads. In addition to the reliability parameters, Forced Outage Rate of each component and also uncertainty of wind power, PV power and demand are considered for micro-grid. In this paper, the problem is formulated as a nonlinear integer minimization problem which minimizes the sum of the total capital, operational, maintenance and replacement cost of DERs. This paper proposes PSO for solving this minimization problem.
N. Tabrizi, E. Babaei, M. Mehdinejad,
Volume 12, Issue 1 (3-2016)
Abstract
Reactive power plays an important role in supporting real power transmission, maintaining system voltages within proper limits and overall system reliability. In this paper, the production cost of reactive power, cost of the system transmission loss, investment cost of capacitor banks and absolute value of total voltage deviation (TVD) are included into the objective function of the power flow problem. Then, by using particle swarm optimization algorithm (PSO), the problem is solved. The proposed PSO algorithm is implemented on standard IEEE 14-bus and IEEE 57-bus test systems and with using fuzzy satisfying method the optimal solutions are determined. The fuzzy goals are quantified by defining their corresponding membership functions and the decision maker is then asked to specify the desirable membership values. The obtained results show that solving this problem by using the proposed method gives much better results than all the other algorithms.
A. A. Khodadoost Arani, J. S. Moghani, A. Khoshsaadat, G. B. Gharehpetian,
Volume 12, Issue 2 (6-2016)
Abstract
Multilevel voltage source inverters have several advantages compare to traditional voltage source inverter. These inverters reduce cost, get better voltage waveform and decrease Total Harmonic Distortion (THD) by increasing the levels of output voltage. In this paper Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are used to find the switching angles for achieving to the minimum THD for output voltage waveform of the Cascaded H-bridge Multi-Level Inverters (MLI). These methods are used for a 27-level inverter for different modulation indices. Result of two methods is identical and in comparison to other methods have the smallest THD. To verify results of two mentioned methods, a simulation using MATLAB/Simulink software is presented.
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.
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.
G. Hamza, M. Sofiane, H. Benbouhenni, N. Bizon,
Volume 19, Issue 2 (6-2023)
Abstract
In this paper, a wind power system based on a doubly-fed induction generator (DFIG) is modeled and simulated. To guarantee high-performance control of the powers injected into the grid by the wind turbine, five intelligent super-twisting sliding mode controllers (STSMC) are used to eliminate the active power and current ripples of the DFIG. The STSMC controller is a high-order sliding mode controller which offers high robustness compared to the traditional sliding mode controller. In addition, it reduces the phenomenon of chattering due to the discontinuous component of the SMC technique. However, the simplicity, ease of execution, durability, and ease of adjusting response are among the most important features of this control compared to some other types. To increase the robustness and improve the response of STSMC, particle swarm optimization method is used for this purpose, where this algorithm is used for parameter calculation. The simulation results obtained using MATLAB software confirm the characteristics of the designed strategy in reducing chattering and ensuring good power control of the DFIG-based wind power.
Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (12-2023)
Abstract
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
