Genetic Algorithm Optimization Toolbox Research Articles

Optimizing the PID Controller by Using the Genetic Algorithm

This paper aims to develop a Proportional Integral Derivative (PID) controller for the Automatic Voltage Regulator (AVR) of the Synchronous Generator, Automatic Voltage Regulator is responsible for keeping the output voltage of the Generator constant and stable. The Automatic Voltage Regulator system can be represented by a fourth order Transfer Function. The parameters of the controller are evaluated by using the Genetic Algorithm tuning method, for this method the Genetic Algorithm Optimization toolbox in MATLAB is used. A PID controller and a PID Filter controller are developed in order to come up with the optimum controller design for the system. The performance of the Genetic Algorithm based controller has been compared with the classical Ziegler-Nichols based controller. The results show that the Genetic Algorithm based controller outperforms the Ziegler-Nichols based controller, the optimum controller design is achieved by the GA based PID filter controller.

Solution to the economic dispatch problem of the Nigerian power system using genetic algorithm

This paper aims to solve the economic dispatch problem of the 28-bus Nigerian power system using genetic algorithm. The power flow solution of the network is first obtained using Newton-Raphson technique; the solution thus obtained is used to determine the loss coefficients of the network. For this study, a forecasted load demand of 2000MW will be considered, MATLAB's genetic algorithm optimization toolbox is used to obtain the optimum generation level of each unit. The optimal power output of each scheduled generating units was obtained after 200 iterations at a minimal generation cost of ₦136,370.205/hr. A power loss of 11.32MW in the network was also obtained using Kron's loss formula. Keywords: Genetic Algorithm, Economic Dispatch, loss coefficient, Optimization, Constraint

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

The number of electric vehicles and renewable energy resources integrated into the power system is increasing day by day. The objective behind the development of electric vehicles and renewable energy sources is to build a sustainable and green power system. The renewables either don't possess system inertia or have less system inertia, therefore, they don't effectively respond to the load variations. The battery storage system of electric vehicles is used as the first line of defense to counter the effect of load/frequency variations and make the system stable. As active power is inversely proportional to the system frequency, for this purpose electric vehicles are included in the microgrid environment. In this paper, an isolated microgrid having a reheat turbine system, wind turbine system, photovoltaic system, and electric vehicles is studied. The output of the renewables is not controlled to utilize its maximum output power. Therefore, adaptive droop control and fuzzy PI control mechanisms are implemented to cater to the frequency variations of the isolated microgrid; the former regulates the power of electric vehicles while maintaining the energy needs of each EV and the later controls the output power of reheat turbine system according to the frequency variation. Furthermore, the genetic algorithm optimization toolbox is utilized to optimize the parameters of the adaptive and fuzzy PI controllers. The proposed model is developed in MATLAB/Simulink which shows that these control techniques effectively sustained the system frequency of isolated microgrid in the desired limits.

Optimization Design of Magnetic Gear Based on Genetic Algorithm Toolbox of Matlab

Concentric magnetic gear (CMG) is a transmission gear with a novel structure and a promising prospect. However, the calculation of its electromagnetic field is more complicated. In this paper, an exact analytical method is used to calculate the magnetic field distribution and electromagnetic torque. The genetic algorithm optimization toolbox of matlab (GAOT) provides a good tool for its optimization design. The optimization results show that the genetic algorithm is practicable and effective, and provide a new approach for the analysis and design of the concentric magnetic gear. It is significant for the research and application of the concentric magnetic gear.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.

Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms

In this paper the synthesis of the predictive controller for control of the nonlinear object is considered. It is supposed that the object model is not known. The method is based on a digital recurrent network (DRN) model of the system to be controlled, which is used for predicting the future behavior of the output variables. The cost function which minimizes the difference between the future object outputs and the desired values of the outputs is formulated. The function ga of the Matlab’s Genetic Algorithm Optimization Toolbox is used for obtaining the optimum values of the control signals. Controller synthesis is illustrated for plants often referred to in the literature. Results of simulations show effectiveness of the proposed control system.

Design Method of Beam Forming Using Genetic Algorithm

For the high side-lobe defeats of conventional beam applied to actual sensor array, a new method of beam forming based on genetic algorithm is proposed. The genetic algorithm optimization toolbox will be used to solve the problem of beam forming. The problem of beam forming will be formed into genetic algorithm function expressions which can get the desired array weights. Computer simulation results show that the design of beam meet our requirements. Tank experiment data simulation results fully demonstrate the feasibility and effectiveness of the method.

Genetic Algorithm Optimization of a Double Four-Bar Manipulator

In order to optimize the dimension of a manipulator, the optimization requirements are analyzed. Then the mathematical model and optimization objectives are established. Next, the lengths of the manipulator are optimized by Matlab genetic algorithm optimization toolbox. The structural strength and bearing installation space are considered at the same time. The trajectory and transmission angle are compared. Finally, the lengths which meet the use requirements are obtained. This optimization method provides a reference for similar mechanism.

Based on Artificial Neural Network Simulation of Alloy Finite Element

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

Based on Ant Colony Algorithm Simulation of Single Crystal Alloy Finite Element

we have used the metaphor of ant colonies to define "the Ant system", a class of distributed algorithms for combinatorial optimization. In this paper we analyze some properties of Ant-cycle, the up to now best performing of the ant algorithms we have tested. We report many results regarding its performance when varying the values of control parameters and we compare it with some FEM algorithms. And in accordance with treatment principles, the microstructure of the alloy is simulated. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

Exergetic Optimization of a Refrigeration Cycle for Re-Liquefaction of LNG Boil-Off Gas

The development of a liquefaction process for liquefied natural gas boil-off re-liquefaction plants will be addressed to provide an environmentally friendly and cost effective solution for gas transport. Onboard boil-off gas (BOG) reliquefaction is a new technology that liquefies BOG and returns it to the cargo tanks instead of burning it. Exergetic efficiency optimization for a cryogenic refrigeration cycle for re-liquefaction of LNG boil-off gas is performed. Thermodynamic modeling has been performed based on energy and exergy analyses. Objective problem is developed based on maximization of the plant exergetic efficiency and selected decision variables and constraints. Optimization process is performed using MATLAB genetic algorithm optimization toolbox. The results of exergetic efficiency optimization are compared with the corresponding results of the base case system obtained in the previous study. Finally, effects of some operating parameters on the exergetic efficiency are discussed by sensitivity analysis.