Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm
Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm
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使用混合信赖域反射算法从测量的 I-V 特性曲线中提取光伏模型的参数
DOI：
10.1016/j.apenergy.2018.09.161
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发表时间：
2018-12
期刊：
影响因子：
11.2
通讯作者：
pei jie lin
中科院分区：
文献类型：
--
作者：
wu lijun;chen zhicong;long chao;cheng shuying;pei jie lin
从实测的电流-电压（I-V）特性曲线中准确、高效、可靠地提取太阳能光伏（PV）模型参数，对于原位光伏阵列实际运行状态的评估、建模和诊断具有重要意义。近年来提出了基于数值启发式优化算法的参数提取方法。然而，这些方法的效率和可靠性受到启发式或随机搜索策略的限制。本文将信任区域反射（TRR）确定性算法与人工蜂群（ABC）元启发式算法相结合，提出了一种新的混合算法ABC-TRR，以改进PV模型的参数提取。ABC-TRR算法结合了ABC的全局探索能力和TRR的局部利用能力，在精度、收敛性和可靠性之间取得了很好的平衡。利用基准光伏组件Photowatt-PWP201和RTC法国太阳能电池的标准I-V曲线以及实验室光伏组件/串/阵列的实测I-V曲线，对所提出的ABC-TRR混合算法进行了评估，并与其他最先进的算法进行了比较。综合实验分析和对比结果表明，本文提出的ABC-TRR算法具有与目前报道的最佳算法相同的精度和最高的总体可靠性。更重要的是，ABC-TRR算法的收敛速度比目前报道得最好的算法平均快4.69倍。鉴于这些优点，本文提出的ABC-TRR算法可以准确、高效、可靠地从实测的I-V曲线中提取PV模型参数。此外，实验表明，参数提取结果可以用于指示部分遮阳和异常退化情况。
Accurate, efficient and reliable parameter extraction of solar photovoltaic (PV) models from the measured current-voltage (I-V) characteristic curves is important for evaluation, modelling, and diagnosis of the actual operating state of in-situ PV arrays. In recent years, numerical heuristic optimization algorithms based parameter extraction methods have been proposed. However, the efficiency and reliability of these methods are limited due to heuristic or stochastic searching strategies. In this paper, by combining the trust-region reflective (TRR) deterministic algorithm with the artificial bee colony (ABC) metaheuristic algorithm, a new hybrid algorithm ABC-TRR is proposed to improve the parameter extraction of PV models. The ABC-TRR algorithm combines the global exploration capability of the ABC and the local exploitation of the TRR, which achieves a good tradeoff among accuracy, convergence and reliability. The proposed ABC-TRR hybrid algorithm is evaluated and compared with other state-of-the-art algorithms using the standard I-V curves of the benchmark Photowatt-PWP201 PV module and RTC France solar cell as well as the measured I-V curves of a laboratory PV module/string/array. Comprehensive experimental analysis and comparison results demonstrate that the proposed ABC-TRR algorithm achieves the same level of accuracy as the best reported algorithms with the highest overall reliability. More importantly, the ABC-TRR algorithm converges 4.69 times faster than the best-reported algorithms on average. In view of these advantages, the proposed ABC-TRR algorithm is a promising alternative for accurately, efficiently and reliably extracting the parameters of PV models from measured I-V curves. In addition, it was experimentally demonstrated that the parameter extraction result can be used to indicate the partial shading and abnormal degradation conditions.
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