The characterization of PV unit can be made using one-, two-, and triple-diode electrical model. Each model has its own merits in terms of number of unknown parameters to be extracted and burden of calculation, and etc. In most applications, one-diode model (1-DM) is sufficed for the purpose of simulation and analysis in steady-state and dynamic conditions. This paper cares of extracting the unknown five parameters of the 1-DM exploiting the real I-V dataset points. New effort of employing the slime mould algorithm (SMA) and its improved version (ImSMA) is addressed to attain the same goal. Lambert W-function or omega function is used for accurate calculus of PV current. Two benchmarking test cases widely used in the literature are demonstrated and analysed to appraise the performance of SMA/ImSMA complete with subsequent analysis and discussions. The best ImSMA’s results of root mean squared current errors are 7.73006e−4 A and 1.3798e−2 A for RTC solar cell and STP6-120/36; respectively. Various scenarios under varied conditions are demonstrated utilizing the cropped best values of the model’s parameters In addition to that, performance measures are made to validate the cropped results along with comparisons to other recent competing algorithms. It can be concluded that the validations in consort with established outcomes signify the ImSMA in recognizing the PV unidentified 1-DM parameters.

中文翻译：

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使用改进的粘菌优化器和兰伯特 W 函数进行光伏模型参数识别

可以使用一个、两个和三个二极管电气模型来表征光伏单元。每种模型在需要提取的未知参数数量和计算负担等方面都有其优点。在大多数应用中，单二极管模型（1-DM）足以满足稳态仿真和分析的目的和动态条件。本文致力于利用真实的 IV 数据集点来提取 1-DM 的未知五个参数。采用粘菌算法（SMA）及其改进版本（ImSMA）的新努力旨在实现相同的目标。朗伯W函数或欧米伽函数用于准确计算PV电流。演示和分析了文献中广泛使用的两个基准测试用例，以评估 SMA/ImSMA 的性能，并进行后续分析和讨论。对于RTC太阳能电池和STP6-120/36，ImSMA的最佳均方根电流误差结果为7.73006e−4 A和1.3798e−2 A；分别。利用模型参数的裁剪最佳值演示了不同条件下的各种场景。此外，还进行了性能测量来验证裁剪结果以及与其他最新竞争算法的比较。可以得出结论，与既定结果相结合的验证表明 ImSMA 能够识别 PV 未识别的 1-DM 参数。