Artificial Neural Network Approach for More Accurate Solar Cell Electrical Circuit Model

Khomdram Jolson Singh, K. L. R. Kho, Sapam Jitu Singh, Yengkhom Chandrika Devi, N. Singh, S. Sarkar
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引用次数: 21

Abstract

The implementation of a neural network especially for improving the accuracy of the electrical equivalent circuit parameters of a solar cell is proposed. These electrical parameters mainly depend on solar irradiation and temperature, but their relationship is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the proposed neural network is trained once by using some measured current–voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly. Taking the effect of sunlight irradiance and ambient temperature into consideration, the output current and power characteristics of PV model are simulated and optimized. Finally, the proposed model has been validated with datasheet and experimental data from commercial PV module, Kotak PV-KM0060 (60Wp).The comparison show the higher accuracy of the ANN model than the conventional one diode circuit model for all operating conditions.
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基于人工神经网络的太阳能电池电路模型
提出了一种用于提高太阳能电池等效电路参数精度的神经网络实现方法。这些电参数主要取决于太阳辐照度和温度,但它们之间的关系是非线性的,不容易用任何解析方程来表达。因此,所提出的神经网络只需要使用一些实测的电流-电压曲线进行一次训练,并且只需快速读取太阳辐照和温度样本即可估计出等效电路参数。考虑太阳光辐照度和环境温度的影响,对光伏模型的输出电流和功率特性进行了仿真和优化。最后,利用商用光伏组件Kotak PV- km0060 (60Wp)的数据表和实验数据对所提出的模型进行了验证。结果表明,在所有工况下,人工神经网络模型都比传统的单二极管电路模型具有更高的精度。
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International Journal of Computer Science and Applications
International Journal of Computer Science and Applications Computer Science-Computer Science Applications
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期刊介绍: IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.
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