An efficient tree seed inspired algorithm for parameter estimation of Photovoltaic models

Beşkirli, Ayşe; Dağ, İdris

doi:10.1016/j.egyr.2021.11.103

Cited by 23 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To provide a comprehensive comparison, Table 8 compares the estimated parameters and RMSE values for the SDM obtained using the GOANM algorithm with other more recent optimization approaches, including gradient based optimizer with criss‐cross and NM algorithms (CCNMGBO) [41], improved moth flame algorithm (IMFOL) [42], ranking teaching–learning‐based optimization (RTLBO) [43], dynamic leader multi‐verse optimizer (DLMVO) [45], reptile search algorithm with Cauchy mutation and opposition‐based learning strategies (OBL‐RSACM) [46], chaos game optimization‐least squares (CGO‐LS) [47], artificial humming bird optimization (AHO) [48], particle swarm optimization with random reselection (PSOCS) [49], elite learning adaptive differential evolution (ELADE) [50], coyote optimization algorithm (COA) [51], gradient based optimizer with differential evolution and sine cosine algorithm (SDGBO) [44], hunger games search algorithm with quantum Nelder–Mead (IHGS) [52], bald eagle search (BES) [53], differential evolution (DE) [56], improved tunicate swarm optimization (ITSA) [54], tree seed inspired algorithm (TSA) [55]. The results highlight the GOANM's superior performance, as it achieves the lowest RMSE value among all the compared methods.…”

Section: Experimental Results and Statistical Analysismentioning

confidence: 99%

“…Table 16 provides a comparison of the estimated parameters and RMSE values for the PV module obtained using the GOANM algorithm with other more recent optimization approaches, including CCNMGBO [41], IMFOL [42], RTLBO [43], DLMVO [45], OBL‐RSACM [46], CGO‐LS [47], AHO [48], PSOCS [49], ELADE [50], COA [51], SDGBO [44], IHGS [52], BES [53], DE [56], ITSA [54], TSA [55]. The results show that the GOANM algorithm achieves the lowest RMSE value among the compared methods, indicating its superior performance in accurately modelling the Photowatt‐PWP201 PV module.…”

Section: Experimental Results and Statistical Analysismentioning

confidence: 99%

“…The power–voltage ( P – V ) and current–voltage ( I – V ) curve characteristics of the optimized SDM using the GOANM algorithm closely matched the experimental data. Comparative analysis with other recent optimization approaches (Gradient‐based optimizer with Criss‐cross and Nelder–Mead algorithm [41], local escape operators based improved moth flame algorithm [42], ranking teaching–learning‐based algorithm [43], gradient based optimizer with differential evolution and sine cosine algorithm [44], dynamic leader multi‐verse optimizer [45], reptile search algorithm with Cauchy mutation and opposition‐based learning strategies [46], chaos game optimization‐least squares [47], artificial humming bird optimization [48], particle swarm optimization with random reselection mechanism [49], elite learning adaptive differential evolution [50], coyote optimization algorithm [51], hunger games search with quantum Nelder–Mead [52], bald eagle search [53], tunicate swarm optimization [54], improved tree seed inspired algorithm [55], differential evolution [56]) further highlighted the GOANM's superior performance, as it achieved the lowest RMSE value among all the methods considered. Similar observations were made for the DDM optimization using the GOANM approach, indicating its effectiveness in fine‐tuning the parameters of the DDM for the RTC France solar cell case study.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Ekinci,

Izci,

Hussien

2024

IET Renewable Power Gen

View full text Add to dashboard Cite

The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.

show abstract

Section: Experimental Results and Statistical Analysismentioning

confidence: 99%

Section: Experimental Results and Statistical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Ekinci,

Izci,

Hussien

2024

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…In this section, parameters of the single diode model (SDM) in the photovoltaic (PV) model [64] are optimized by using to explore its effectiveness further. In the experiment, two sets of data [65] from the RTC France PV module and the Photowatt-PWP201 PV module were used to estimate the parameters of the SDM model of the RTC France PV module; the unknown parameters of the Photowatt-PWP201 PV module were estimated.…”

Section: Parameter Estimation Of Solar Photovoltaic Modelmentioning

confidence: 99%

Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation

Yan,

Hu,

Zhang

2024

Biomimetics

View full text Add to dashboard Cite

To address the shortcomings of the recently proposed Fick’s Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick’s Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation strategy, interweaving-based comprehensive learning strategy, and seagull update strategy. First, the differential variation strategy is added in the search phase to increase the randomness and expand the search degree of space. Second, by introducing the Gaussian local variation, the search diversity is increased, and the exploration capability and convergence efficiency are further improved. Further, a comprehensive learning strategy that simultaneously updates multiple individual parameters is introduced to improve search diversity and shorten the running time. Finally, the stability of the update is improved by adding a global search mechanism to balance the distribution of molecules on both sides during seagull updates. To test the competitiveness of the algorithms, the exploration and exploitation capability of the proposed FLAS is validated on 23 benchmark functions, and CEC2020 tests. FLAS is compared with other algorithms in seven engineering optimizations such as a reducer, three-bar truss, gear transmission system, piston rod optimization, gas transmission compressor, pressure vessel, and stepped cone pulley. The experimental results verify that FLAS can effectively optimize conventional engineering optimization problems. Finally, the engineering applicability of the FLAS algorithm is further highlighted by analyzing the results of parameter estimation for the solar PV model.

show abstract

“…The solution of optimization problems with different characteristics, such as singleobjective and multi-objective discrete problems [1,2] or continuous problems [3,4], with meta-heuristic algorithms, is becoming increasingly common. Recently, swarm intelligencebased intelligent optimization algorithms have been proposed and successfully used.…”

Section: Introductionmentioning

confidence: 99%

A New Approach Based on Collective Intelligence to Solve Traveling Salesman Problems

Kiran,

Beskirli

2024

Biomimetics

View full text Add to dashboard Cite

This paper presents a novel approach based on the ant system algorithm for solving discrete optimization problems. The proposed method is based on path construction, path improvement techniques, and the footprint mechanism. Some information about the optimization problem and collective intelligence is used in order to create solutions in the path construction phase. In the path improvement phase, neighborhood operations are applied to the solution, which is the best of the population and is obtained from the path construction phase. The collective intelligence in the path construction phase is based on a footprint mechanism, and more footprints on the arc improve the selection chance of this arc. A selection probability is also balanced by using information about the problem (e.g., the distance between nodes for a traveling salesman problem). The performance of the proposed method has been investigated on 25 traveling salesman problems and compared with state-of-the-art algorithms. The experimental comparisons show that the proposed method produced comparable results for the problems dealt with in this study.

show abstract

An efficient tree seed inspired algorithm for parameter estimation of Photovoltaic models

Cited by 23 publications

References 20 publications

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation

A New Approach Based on Collective Intelligence to Solve Traveling Salesman Problems

Contact Info

Product

Resources

About