Parameter Estimation of LFM Signal in the Fractional Fourier Domain via Curve-Fitting Optimization Technique

2010 ◽  
Author(s):  
Fang Zhang ◽  
Lin Qi ◽  
Enqing Chen ◽  
Xiaomin Mu
Keyword(s):  
Parameter Estimation ◽  
Curve Fitting ◽  
Optimization Technique ◽  
Fourier Domain

A novel way of parameter estimation of solar photovoltaic system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rahul Bisht ◽  
Afzal Sikander
Keyword(s):  
Parameter Estimation ◽  
Mean Absolute Error ◽  
Optimization Technique ◽  
Absolute Error ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Solar Panels ◽  
Content Type ◽  
Solar Photovoltaic System

Purpose This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

scholarly journals Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Technique ◽  
Convergence Time ◽  
Generation Gap ◽  
Model Order ◽  
Real Coded Genetic Algorithm ◽  
The Mean ◽  
Conventional Genetic Algorithm

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

Optimum NURBS Curve Fitting for Geometry Parameterization of Gas Turbine Blades’ Sections: Part II — Swarm Intelligence Techniques

2012 ◽  
Author(s):  
A. Safari ◽  
H. G. Lemu
Keyword(s):  
Swarm Intelligence ◽  
Gas Turbine ◽  
Curve Fitting ◽  
Point Cloud ◽  
Optimization Technique ◽  
Turbine Blades ◽  
Optimization Techniques ◽  
Invasive Weed ◽  
Nurbs Curve ◽  
Good Ability

In part I of this study an optimum NURBS curve fitting by two evolutionary optimization techniques was successfully designed. These methods were implemented to optimize the location of a set of NURBS control points for the measured point cloud of four segments of a gas turbine compressor airfoil shape. The purpose of the optimization was to demonstrate the good ability of evolutionary techniques, in particular Genetic Algorithms, in optimizing such curve fitting problems. The objective of part II is to examine two alternative solutions for NURBS curve fitting of the same airfoil point cloud with swarm intelligence optimization technique. Indeed, the same work has been done by applying two basically different optimization approaches that is Particle Swarm Optimization and Invasive Weed Optimization. Results allow seeing a number of advantages as well as some disadvantages in this optimum curve fitting approach in comparison to the previous techniques applied by authors.

Application of Genetic Algorithms on Parameter Estimation of HVDC Circuit Breaker

2011 ◽  
Vol 52-54 ◽  
pp. 1009-1014 ◽  
Author(s):  
Tao Jin ◽  
Wei Chen ◽  
Tao Ning
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parameter Estimation ◽  
High Voltage ◽  
Curve Fitting ◽  
Circuit Breaker ◽  
High Voltage Circuit Breaker ◽  
Oscillation Circuit ◽  
Dc Circuit Breaker

In order to estimate the parameters of capacitance, inductance and resistance in oscillation circuit DC circuit breaker a method of parameter estimation of High-Voltage circuit break based on genetic algorithms is proposed and which can properly assess the performance of DC circuit breaker. In this paper A genetic algorithm is introduced and applied in parameter estimation of High-Voltage circuit breaker and then compare it with the traditional MATLAB curve fitting, the result clearly reveal the advantages of genetic algorithm.

scholarly journals Developing Photovoltaic Cells Parameter Estimation Algorithm Based on Equilibrium Optimization Technique

2021 ◽  
Author(s):  
TechniqueAbdelhady Ramadan ◽  
◽  
Salah Kamel ◽  
Nabil Neggaz ◽  
Ali S. Alghamdi ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Solar Cell ◽  
Optimization Problem ◽  
Optimization Algorithms ◽  
Optimization Technique ◽  
Estimation Algorithm ◽  
Renewable Energy Resources ◽  
Pv Systems ◽  
Parameter Estimation Algorithm ◽  
Physical Phenomena

Nowadays all the world does its best to develop the power generation systems that depend on nature in order to reduce the dependence on fuel. Photovoltaic (PV) systems are considered one of the most important renewable energy resources. Scientific research has gained a high interest, especially in PV cell modeling and parameter estimation. The estimation of optimum parameters for the PV model has been considered the main target of the paper optimization problem. Equilibrium optimization (EO) algorithm is considered one of optimization algorithms inspired from nature physical phenomena. EO algorithm has been inspired from the nature physical process of controlling mass balance through specific volume until reaching equilibrium state. In this paper, an EO algorithm has been proposed and applied to prepare a mathematical model for photovoltaic solar cell. The challenge in this optimization problem is the non-linearity in PV solar cell characteristic. The EO algorithm has been evaluated through the following items. EO has been applied to estimate the parameters of different PV models such as single, double and triple PV models, which have different complexity. Applying the previous item for real PV application. The obtained results have been compared though different functions such as root mean square value and absolute mean error. In all cases, EO obtained results have been compared with the more recent optimization algorithms such as Particle swarm optimization (PSO), Teaching learn Based Optimization (TLBO) and Harries Hawk optimization (HHO). From the all obtained results, EO algorithm gives more accurate PV models in comparison with other optimization algorithms.

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