A Solar PV Model Parameter Estimation Based on the Enhanced Self-Organization Maps

Mouncef El marghichi
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引用次数: 0

Abstract

Solar photovoltaic (PV) is a commonly utilized renewable energy source, with PV cells often being modeled as electric circuits. The identification of suitable circuit model parameters for PV cells is vital for performance evaluation, efficiency calculations, and the implementation of maximum power point tracking in solar PV systems. However, modeling the solar PV system is a nonlinear problem that requires an efficient algorithm. In this paper, we employ the enhanced self-organization maps (EASOM) to efficiently reduce the search space for parameter estimation in solar PV models. Our algorithm trains the SOM network on a subset of solutions, identifies the top solution's neural unit, generates a population of potential solutions, and selects the best candidate using a cost function, which represents the best PV model parameters obtained. The performance of EASOM is verified by extracting the parameters of the single diode (SDM) and double diode (DDM) models for the STM6-40/36 PV module. EASOM outperformed state-of-the-art algorithms with the lowest RMSE and MSE values of 8.3 mA and 6.87e-05 and achieved the lowest maximum error values of 27.37 mA and 20.52 mA, as well as low power error of 66.04 mW and 62.8 mW for SDM and DDM models.
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基于增强自组织映射的太阳能光伏模型参数估计
太阳能光伏(PV)是一种常用的可再生能源,光伏电池通常被建模为电路。确定合适的光伏电池电路模型参数对于太阳能光伏系统的性能评估、效率计算和最大功率点跟踪的实现至关重要。然而,太阳能光伏系统的建模是一个非线性问题,需要一个有效的算法。本文采用增强自组织映射(enhanced self-organization maps, EASOM)有效地减少了太阳能光伏模型参数估计的搜索空间。我们的算法在解决方案的子集上训练SOM网络,识别顶部解决方案的神经单元,生成潜在解决方案的总体,并使用成本函数选择最佳候选方案,该函数表示获得的最佳PV模型参数。通过提取STM6-40/36光伏组件的单二极管(SDM)和双二极管(DDM)模型参数,验证EASOM的性能。EASOM算法的RMSE和MSE分别为8.3 mA和6.87e-05,最大误差分别为27.37 mA和20.52 mA,功率误差分别为66.04 mW和62.8 mW。
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来源期刊
Periodica polytechnica Electrical engineering and computer science
Periodica polytechnica Electrical engineering and computer science Engineering-Electrical and Electronic Engineering
CiteScore
2.60
自引率
0.00%
发文量
36
期刊介绍: The main scope of the journal is to publish original research articles in the wide field of electrical engineering and informatics fitting into one of the following five Sections of the Journal: (i) Communication systems, networks and technology, (ii) Computer science and information theory, (iii) Control, signal processing and signal analysis, medical applications, (iv) Components, Microelectronics and Material Sciences, (v) Power engineering and mechatronics, (vi) Mobile Software, Internet of Things and Wearable Devices, (vii) Solid-state lighting and (viii) Vehicular Technology (land, airborne, and maritime mobile services; automotive, radar systems; antennas and radio wave propagation).
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