scholarly journals A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 472
Author(s):  
Mohamed H. Hassan ◽  
Salah Kamel ◽  
M. A. El-Dabah ◽  
Hegazy Rezk
Keyword(s):  
Optimization Problems ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Decision Variable ◽  
Unknown Parameters ◽  
Local Optima ◽  
Pv Module ◽  
Squared Error ◽  
Gradient Based ◽  
Solution Methodology

In this paper, a modified version of a recent optimization algorithm called gradient-based optimizer (GBO) is proposed with the aim of improving its performance. Both the original gradient-based optimizer and the modified version, MGBO, are utilized for estimating the parameters of Photovoltaic models. The MGBO has the advantages of accelerated convergence rate as well as avoiding the local optima. These features make it compatible for investigating its performance in one of the nonlinear optimization problems like Photovoltaic model parameters estimation. The MGBO is used for the identification of parameters of different Photovoltaic models; single-diode, double-diode, and PV module. To obtain a generic Photovoltaic model, it is required to fit the experimentally obtained data. During the optimization process, the unknown parameters of the PV model are used as a decision variable whereas the root means squared error between the measured and estimated data is used as a cost function. The results verified the fast conversion rate and precision of the MGBO over other recently reported algorithms in solving the studied optimization problem.

Finding Better Local Optima in Topology Optimization via Tunneling

2018 ◽  
Author(s):  
Shanglong Zhang ◽  
Julián A. Norato
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
Structural Response ◽  
Free Form ◽  
Local Optima ◽  
Design Representation ◽  
Tunneling Method ◽  
Optimization Parameters ◽  
Gradient Based ◽  
Geometric Primitives

Topology optimization problems are typically non-convex, and as such, multiple local minima exist. Depending on the initial design, the type of optimization algorithm and the optimization parameters, gradient-based optimizers converge to one of those minima. Unfortunately, these minima can be highly suboptimal, particularly when the structural response is very non-linear or when multiple constraints are present. This issue is more pronounced in the topology optimization of geometric primitives, because the design representation is more compact and restricted than in free-form topology optimization. In this paper, we investigate the use of tunneling in topology optimization to move from a poor local minimum to a better one. The tunneling method used in this work is a gradient-based deterministic method that finds a better minimum than the previous one in a sequential manner. We demonstrate this approach via numerical examples and show that the coupling of the tunneling method with topology optimization leads to better designs.

Parameter estimation of single diode PV module based on Nelder-Mead optimization algorithm

2018 ◽  
Vol 15 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Alivarani Mohapatra ◽  
Byamakesh Nayak ◽  
Kanungo Barada Mohanty
Keyword(s):  
Parameter Estimation ◽  
Optimization Algorithm ◽  
Environmental Conditions ◽  
Reference Model ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Pv System ◽  
Content Type ◽  
Pv Module ◽  
Proposed Model

Purpose This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module considering the environmental conditions. Design/methodology/approach At a particular temperature and irradiation, experimental current-voltage (I-V) and power-voltage (P-V) characteristics are drawn and considered as a reference model. The PV system model with unknown model parameters is considered as the adaptive model whose unknown model parameters are to be adapted so that the simulated characteristics closely matches with the experimental characteristics. A single diode (Rsh) model with five unknown model parameters is considered here for the parameter estimation. Findings The key advantages of this method are that parameters are estimated considering environmental conditions. Experimental characteristics are considered for parameter estimation which gives accurate results. Parameters are estimated considering both I-V and P-V curves as most of the applications demand extraction of the actual power from the PV module. Originality/value The proposed model is compared with other three well-known models available in the literature considering various statistical errors. The results show the superiority of the proposed model with a minimum error for both I-V and P-V characteristics.

scholarly journals Improved Hybrid Parameters Extraction of a PV Module Using a Moth Flame Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2798
Author(s):  
Safi Allah Hamadi ◽  
Aissa Chouder ◽  
Mohamed Mounir Rezaoui ◽  
Saad Motahhir ◽  
Ameur Miloud Kaddouri
Keyword(s):  
Extraction Procedure ◽  
Parameter Extraction ◽  
Standard Test ◽  
Maximum Power ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Operating Condition ◽  
Pv Module ◽  
Test Conditions ◽  
Parameters Extraction

The identification of actual photovoltaic (PV) model parameters under real operating condition is a crucial step for PV engineering. An accurate and trusted model depends mainly on the accuracy of the model parameters. In this paper, an accurate and enhanced methodology is intended for PV module parameters extraction in outdoor conditions. The proposed methodology combines numerical methods and analytical formulations of the one diode model to derive the five unknown parameters in any operating condition of irradiance and temperature. First, the measured I-V curves at a random weather condition are translated to standard test conditions (i.e., G = 1000 W/m2, T = 25 °C), using translation equations. The second step consists of using an optimization algorithm namely the moth flame algorithm (MFO) to find out the five parameters at standard test conditions. Analytical formulations, at a random irradiance and temperature, are then used to express the unknown parameters at any irradiance and temperature. The proposed approach is validated under outdoor conditions against measured I-V curves at different irradiances and temperatures. The validation has also been performed under dynamic operation where the measured maximum power point coordinates (MPP) are compared to the predicted maximum power points. The obtained results from the adopted hybrid methodology confirm the accuracy of the parameter extraction procedure.

scholarly journals PV Module Parameters Estimation Using Newton Raphson

2019 ◽  
pp. 137-149
Author(s):  
Ranjith Dharmarajan ◽  
Rajeswari Ramachandran
Keyword(s):  
Parameters Estimation ◽  
Model Parameters ◽  
Solar Pv ◽  
Pv System ◽  
Shunt Resistance ◽  
The Real ◽  
Pv Module ◽  
Current Series ◽  
Real Performance ◽  
Newton Raphson

The estimation of solar photovoltaic (PV) system with help of electrical model parameters, such as photon generated current, the diode saturation current, series resistance, shunt resistance, and diode ideality factor, are desirable to predict the real performance characteristics of solar PV under varying environmental conditions. Finally, performance indices, such as PV characteristics curve are estimated for the various solar PV panels, using Newton Raphson (NR) to reveal the effectiveness of the proposed method. Also, validation with `experimental data has been considered. Finally, through the comparative analysis of the results, it is revealed that the proposed method offers solar PV characteristics closer to the real characteristics.

Adjoint- and Hybrid-Based Hessians for Optimization Problems in System Identification

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Souransu Nandi ◽  
Tarunraj Singh
Keyword(s):  
System Identification ◽  
Optimization Problems ◽  
Initial Conditions ◽  
Synthetic Data ◽  
Model Parameters ◽  
Computationally Efficient ◽  
Step Size ◽  
Adjoint Sensitivity ◽  
Gradient Based ◽  
Discrete Measurement

An adjoint sensitivity-based approach to determine the gradient and Hessian of cost functions for system identification of dynamical systems is presented. The motivation is the development of a computationally efficient approach relative to the direct differentiation (DD) technique and which overcomes the challenges of the step-size selection in finite difference (FD) approaches. An optimization framework is used to determine the parameters of a dynamical system which minimizes a summation of a scalar cost function evaluated at the discrete measurement instants. The discrete time measurements result in discontinuities in the Lagrange multipliers. Two approaches labeled as the Adjoint and the Hybrid are developed for the calculation of the gradient and Hessian for gradient-based optimization algorithms. The proposed approach is illustrated on the Lorenz 63 model where part of the initial conditions and model parameters are estimated using synthetic data. Examples of identifying model parameters of light curves of type 1a supernovae and a two-tank dynamic model using publicly available data are also included.

Smooth Coated Otter Optimizer: A New Nature Inspired Optimizer

2019 ◽  
Vol 13 ◽  
Author(s):  
Rajani ◽  
Naresh Kumar ◽  
Kuldeep Singh Kaswan
Keyword(s):  
Software Reliability ◽  
Optimization Problems ◽  
Reliability Estimation ◽  
Model Parameters ◽  
Hunting Behavior ◽  
Local Optima ◽  
Reliability Models ◽  
Team Cooperation ◽  
Software Reliability Models ◽  
Nature Inspired Algorithm

Background: Nature inspired Algorithms have become predominant from the last few decades due to their derivative free nature and capability of avoiding the local optima problem. These algorithms have already shown humongous success in software reliability estimation. Objective: A new nature inspired algorithm inspired by the hunting capability of smooth coated otter’s for parameter optimization of software reliability models is proposed. Hunting in group is the most striking feature of the otters. In this paper, smart hunting behavior of otter’s is implemented to optimize software reliability model parameters. Method: Otters strongly co-operate together and show ideal team cooperation. Intelligent fish hunting capability of smooth coated otters makes them different from other swarm intelligence based algorithms. Three phases of otter hunting mainly moving in V formation in the direction of movement of the prey, approaching violently through the creek and then attacking the prey on the shore are implemented. Three software reliability models are used for showing the suitability of proposed algorithm. Result: The evaluation of study shows that proposed algorithm beat ABC, GA and PSO algorithms by 75% and 50% in terms of lower value of SSE and lower value of MSE. Conclusion: Intelligent foraging behavior of smooth coated otter provides significant gain capability in parameter estimation of software reliability models. The results are motivating. Further smooth coated otter optimizer can be applied to other optimization problems.

scholarly journals A New Two-Parameter Burr-Hatke Distribution: Properties and Bayesian and Non-Bayesian Inference with Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Ahmed Z. Afify ◽  
Hassan M. Aljohani ◽  
Abdulaziz S. Alghamdi ◽  
Ahmed M. Gemeay ◽  
Abdullah M. Sarg
Keyword(s):  
Real Life ◽  
Estimation Methods ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Bayesian Techniques ◽  
Bathtub Shape ◽  
Mathematical Properties ◽  
Squared Error ◽  
And Behavior ◽  
Two Parameter

This article introduces a two-parameter flexible extension of the Burr-Hatke distribution using the inverse-power transformation. The failure rate of the new distribution can be an increasing shape, a decreasing shape, or an upside-down bathtub shape. Some of its mathematical properties are calculated. Ten estimation methods, including classical and Bayesian techniques, are discussed to estimate the model parameters. The Bayes estimators for the unknown parameters, based on the squared error, general entropy, and linear exponential loss functions, are provided. The ranking and behavior of these methods are assessed by simulation results with their partial and overall ranks. Finally, the flexibility of the proposed distribution is illustrated empirically using two real-life datasets. The analyzed data shows that the introduced distribution provides a superior fit than some important competing distributions such as the Weibull, Fréchet, gamma, exponential, inverse log-logistic, inverse weighted Lindley, inverse Pareto, inverse Nakagami-M, and Burr-Hatke distributions.

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

scholarly journals Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMax

2021 ◽  
Author(s):  
Moritz Mühlenthaler ◽  
Alexander Raß ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Markov Model ◽  
Optimization Problems ◽  
Formal Analysis ◽  
Particle Swarm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Local Optima ◽  
Sorting Problem

AbstractMeta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain “easy” reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration.

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