Photovoltaic module model parameterization method considering environment and time-varying factors

Abstract

The invention discloses a photovoltaic module model parameterization method considering environment and time-varying factors, and the method comprises the following steps: employing a double-iterationalgorithm to extract single-diode model parameters under a specific operation condition, and enabling a result of the algorithm to serve as a reference of later estimation; estimating a parameter value under an unknown condition based on the parameters extracted under the nearest neighbor condition; and further optimizing the estimation parameters by utilizing a grid point distance weighting result. The double-iteration algorithm provided by the invention only needs related data of three key working points, does not need additional working points or slope data, has a wider application space in actual engineering, has higher robustness and physical interpretability for noise compared with a traditional analytical method, and besides, has higher robustness and lower physical interpretability for noise compared with the traditional analytical method. The five model parameters estimated by the method all consider the influence of the operating environment, and degradation factors can be introduced to describe the degradation process of the photovoltaic module, which is more in line with the actual situation.

Description

technical field [0001] The invention relates to a parameterization method of a photovoltaic module model considering environment and time-varying factors, and belongs to the technical field of new energy sources. Background technique [0002] Accurate characterization of the electrical characteristics of photovoltaic modules is crucial to their condition monitoring and fault diagnosis, so as to ensure the reliable and efficient operation of photovoltaic systems. The most commonly used and basic diagnostic method is realized by comparing the measured value and the simulated value of the electrical quantity. The simulation method mainly includes empirical formula method, machine learning method and parameterized model method. Among them, the empirical formula method usually fails under low irradiance conditions far from the standard situation. In addition, these formulas generally only give the estimated value of a single electrical quantity, but cannot provide other variables...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problems of poor robustness and poor physical explainability in the existing model parameterization methods, provide a parameter extraction method based on a double iterative algorithm, and take into account the time-varying degradation factors of the operating environment

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