Research on real-time identification method of model parameters for the photovoltaic array

Chen, Xiang; Ding, Kun; Yang, Hang; Chen, Xihui; Zhang, Jingwei; Meng, Jian; Gao, Ruiguang; Liu, Zengquan

doi:10.1016/j.apenergy.2023.121157

Cited by 14 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although it is more beneficial, in terms of precision, its fitting requires moresophisticated computation. Also, it is very important to point out that the initial values of the parameters to be estimated should be adequately selected to avoid the non-convergence of the algorithm [26].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Abbassi,

Saidi,

Urooj

et al. 2023

Mathematics

View full text Add to dashboard Cite

Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 ×10−3, 1.387641 ×10−3, 1.719946 ×10−3, and 1.686104 ×10−3, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…As in the life cycle of all animals, maternity herds are the most-important members, which ensure the continuity of the herd's life by giving birth to new strong male gazelles. Male gazelles contribute significantly to the delivery of new gazelles and young males attempting to acquire female gazelles according to Equation (26).…”

Section: Maternity Herdsmentioning

confidence: 99%

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Abbassi,

Saidi,

Urooj

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…However, these methods are limited in practice because of the poor accuracy provided by simplifying the assumptions. In addition, the systems are usually described by transcendental equations that require arbitrary initial values to solve the equations, which affect their accuracy [7, 8]. Metaheuristic optimization algorithms have been used instead of analytical methods to overcome complex and non‐linear problems.…”

Section: Introductionmentioning

confidence: 99%

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells

Ismail,

Diab

2024

IET Renewable Power Gen

View full text Add to dashboard Cite

Parameter estimation of photovoltaic (PV) solar cells and module models pays attention to researchers owing to their importance in practical considerations. The single diode model (SDM) circuit with five unknown parameters is widely used to model PV solar cells and modules. In this paper, a novel approach called alternate optimization (AO) algorithm based on a discrete search is proposed to estimate the SDM parameters. The proposed algorithm provides efficient and robust performance, considering a limited set of discrete values and increasing the convergence speed. Two practical case studies with actual measurements are considered to assess the proposed AO algorithm: the RTC France solar cell and monocrystalline PV modules with different irradiations and temperatures. The numerical findings underscore the superior performance of the proposed AO algorithm across various metrics. Notably, it achieves an exceptional Root Mean Square Error (RMSE) of 7.7426 × 10−04 for the RTC France PV cell and approximately 1 × 10−03 RMSE for monocrystalline PV modules. Additionally, the algorithm exhibits unparalleled speed, showcasing the fastest convergence with an elapsed time of 1.66 × 10−05—markedly 4.45 times quicker than the fastest method documented in the literature for SDM parameter estimation. Furthermore, the proposed AO algorithm stands out for its efficiency, requiring a maximum of five iterations for parameter estimation, a substantial improvement compared to the more than 10 iterations typically needed by algorithms in the existing literature. Its robustness is also commendable, as evidenced by the stability of final RMSE values across a variety of experiments, distinguishing it from less robust algorithms found in the literature.

show abstract

“…It requires tribal knowledge about the internal electrical parameters of the PV generator influencing its performance. Indeed, in the present time, the demand for photovoltaic modeling methods has grown simultaneously with the great interest given to renewable energies [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Diverse electrical models were introduced in the literature to describe the physical phenomenon occurring inside PV generators. The one-diode model with five electrical parameters and the two-diode model with seven parameters are the most adopted by the authors [ 9 , [12] , [13] , [14] , [15] , [16] ]. To reduce the complexity and the nonlinearity degree of the current equation, the single-diode model is extensively employed in academic research [ [12] , [13] , [14] , [15] , [16] , [17] , [18] ].…”

Section: Introductionmentioning

confidence: 99%

An adaptable method for efficient modeling of photovoltaic generators’ performance based on the double-diode model

Tifidat,

Maouhoub,

Ait Salah

et al. 2024

Heliyon

View full text Add to dashboard Cite

Research on real-time identification method of model parameters for the photovoltaic array

Cited by 14 publications

References 41 publications

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells

An adaptable method for efficient modeling of photovoltaic generators’ performance based on the double-diode model

Contact Info

Product

Resources

About