Opposite Normalized Trust-Region Reflective (ONTRR): A New Algorithm for Parameter Extraction of Single, Double and Triple Diode Solar Cell Models

Abstract

To accurately, efficiently and reliably extract the parameters of single, double and triple diode solar cell models, this paper proposes a randomly initialized opposite normalized trust-region reflective (ONTRR) algorithm. The novelty of ONTRR lies primarily in two amendments to the standard TRR search. (1) Random opposite initialization is added to decrease the initial point sensitivity of TRR and thus reduce the possibility of being trapped in local optima. (2) Min-max normalization is embedded to eliminate the negative effects arising from different magnitudes of model parameter values and thus drive the derivative-dependent TRR search in an efficient manner. The proposed ONTRR algorithm is evaluated and compared to other state-of-the-art algorithms using four benchmarked I–V datasets with two commonly used objective functions. To be objective and reproducible, the comparative experiments are carried out with default random seeds for 1000 independent runs instead of the 30, 50, or 100 runs used in most studies in the literature. The comparison results demonstrate that for all 12 of the test cases, the proposed ONTRR algorithm consistently achieves the highest accuracy with the least computational effort, and is often superior to the best-performing algorithms reported in the literature in terms of convergence speed, average accuracy and statistical stability.

Comparative Study of Parameter Extraction from a Solar Cell or a Photovoltaic Module by Combining Metaheuristic Algorithms with Different Simulation Current Calculation Methods

“…The corresponding RMSE values are 9.86022 × 10 −4 , 7.73006 × 10 −4 , and 7.72986 × 10 −4 . These results are in good agreement with references [6,7,17,20,29,[39][40][41][42][43][44][45][46][47][48]. Furthermore, the SDM parameters (I ph , I 0 , R s , R sh , and n) attained from the PWP 201 PV module by using the approximation method, the LW method, and the NR method are (1.03051 A, 3.48226 µA, 0.03337 Ω, 27.27728 Ω, and 1.35119), (1.03143 A, 2.63808 µA, 0.03432 Ω, 22.82337 Ω, and 1.32217), and (1.03143 A, 2.63813 µA, 0.03432 Ω, 22.82332 Ω, and 1.32218), respectively.…”

“…These results are in good accordance with references [41,42,45,46,49]. Moreover, if N s R s , N s R sh , and N s n are considered as the series resistance, parallel resistance, and ideal factor of a PV module (see Equations ( 1) and ( 2)), our results are also consistent with references [17,20,29,39,40,43,44,47,50,51]. It should be mentioned that the SDM parameter-extraction results achieved by the LW method and the NR method are almost identical for both the solar cell and the PV module.…”

“…The best RMSEs obtained from the French RTC solar cell by using the approximation method and the NR method are 9.57663 × 10 −4 and 6.93709 × 10 −4 , respectively. In contrast, the best RMSEs achieved by using the approximation method and NR method (or LW method) in references [17,20,37,38,40,41,45,46,48] are 9.82485 × 10 −4 and 7.42 × 10 −4 , respectively. Furthermore, the best RMSEs achieved from the PWP 201 PV module by using the approximation method and the NR method are 2.37528 × 10 −3 and 1.99051 × 10 −3 , respectively.…”

“…Due to the unreasonable settings of the parameter boundary, the extracted I 01 approaches I 02 , the extracted n 1 approaches n 2 , and even I 01 > I 02 and n 1 > n 2 [13,14,16]. Up to now, there are still many researchers who use this incorrect DDM parameter boundary to extract the DDM parameters of the French RTC solar cell [17][18][19][20]. In fact, according to the research results of PV specialists on the DDM parameter ranges of crystalline silicon solar cells based on semiconductor device theory [21], n 1 ≈ 1, n 2 is usually greater than 2, and I 02 is usually 3-7 orders of magnitude higher than I 01 .…”

Application of an offline grey box method for predicting the manoeuvring performance

“…The TRR method has an advantage in handling constraints compared to other line search methods, as stated in [47]. For a detailed algorithm of the TRR method, refer to the studies of Coleman and Li [50], Zhang and Zhang [51], and Gao et al [52].…”