Abstract

The paper presents a hierarchical polynomial chaos expansion-based probabilistic approach to analyze the single diode solar cell model under Gaussian parametric uncertainty. It is important to analyze single diode solar cell model response under random events or factors due to uncertainty propagation. The optimal values of five electrical parameters associated with the single diode model are estimated using six deterministic optimization techniques through the root-mean-square minimization approach. Values corresponding to the best objective function response are further utilized to describe the probabilistic design space of each random electrical parameter under uncertainty. Adequate samples of each parameter corresponding Gaussian uncertain distribution are generated using Latin hypercube sampling. Furthermore, a multistage probabilistic approach is adopted to evaluate the model response using low-cost polynomial chaos series expansion and perform global sensitivity analysis under specified Gaussian distribution. Coefficients of polynomial basis functions are calculated using least square and least angle regression techniques. Unlike the highly non-linear and complex single diode representation of solar cells, the polynomial chaos expansion model provides a low computational burden and reduced complexity. To ensure reproducibility, probabilistic output response computed using proposed polynomial chaos expansion model is compared with the true model response. Finally, a multidimensional sensitivity analysis is performed through Sobol decomposition of polynomial chaos series representation to quantify the contribution of each parameter to the variance of the probabilistic response. The validation and assessment result shows that the output probabilistic response of the solar cell under Gaussian parametric uncertainty correlates to a Rayleigh probability distribution function. Output response is characterized by a mean value of 0.0060 and 0.0760 for RTC France and Solarex MSX83 solar cells, respectively. The standard deviation of pm 0.0034 and pm 0.0052 was observed in the probabilistic response for RTC France and Solarex MSX83 solar cells, respectively.

Highlights

  • The conventional power infrastructure widely employing fossil fuels for a centralized generation has seen a shift toward renewable-based technologies

  • This section aims to assess the application of six different meta-heuristic algorithms: Firefly algorithm, Particle Swarm optimization, Multi-Variate Newton Raphson (MVNR), Wind-Driven optimization (WDO), Adaptive WindDriven optimization (AWDO), and Black Widow optimization (BWO) for parameterization of a single diode model of a solar cell through an MATLAB code

  • An experimental database for a commercial (RTC France) 57 mm silicon solar cell under 1kW/m2 at 308K is selected as a benchmark test case, while another experimental database for Solarex MSX83 (36 cells) solar panel under 1kW/m2 at 298K is selected as a practical test case for solving the deterministic parameterization problem of a solar cell

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Summary

Introduction

The conventional power infrastructure widely employing fossil fuels for a centralized generation has seen a shift toward renewable-based technologies. Cubas et al [5] proposed a technique to modify the implicit Shockley current equation into an explicit expression using Lambert W function which can be solved to find the optimal values of solar cell electrical parameters. Liao et al [15] proposed a teaching–learning-based optimization for estimating five and seven electrical parameters associated with a single diode and double diode model of a solar cell. It involves iterative improvement in mean score of the whole candidate population through previous learning. The objective is to minimize the cost function defined by Eq (7) to find a unique solution of S within its defined range

Objective function formulation
Results and discussion
Conclusion
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