Approximating parameters of photovoltaic models using an amended reptile search algorithm

Chauhan, Sumika; Vashishtha, Govind; Kumar, Anil

doi:10.1007/s12652-022-04412-9

Cited by 22 publications

(7 citation statements)

References 42 publications

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“…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%

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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

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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.

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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

“…For instance, Newton Raphson and Levenberg–Marquardt damping parameter are combined in 27 to get accurate values of unknown parameters of SDM and DDM model of solar PV. In the same line, hybridization of opposition-based learning reptile search algorithm and Cauchy mutation strategy is presented in 28 , a hybrid approach, based on modified third order Newton Raphson has been given in 29 . Recently, some recent artificial intelligent techniques, such as sanitized teacher learning-based optimization 30 , jellyfish search optimizer 31 , success-history adaptation differential evolution with linear population size reduction 32 have also been in the research to get accurate values of the unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

An efficient data sheet based parameter estimation technique of solar PV

Charu,

Thakur,

Rawat

et al. 2024

Sci Rep

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This work develops an efficient parameter estimation technique, based on manufacturer datasheet, to obtain unknown parameter of solar photovoltaic (PV), precisely. Firstly, a nonlinear least square objective function, in terms of variables given in manufacturer datasheet, has been developed. Then, two optimization techniques, namely the Particle Swarn Optimization (PSO) and Harmony Search (HS) are applied on the developed objective function to achieve the optimized result. Further, the correctness of the developed technique is tested by estimating the performance indices, namely percentage maximum power deviation index (%MPDI) and overall model deviation index (OMDI), of two different solar PV, viz., Kyocera KD210GH-2PU (poly-crystalline), and Shell SQ85 (mono-crystalline). It is shown that developed method with PSO outperforms the HS. The developed method with PSO gives the values of %MPDI and OMDI of 0.0214% and 0.213, only. Also, the existing methods, based on hybrid, multi-objective function, numerical method, have been considered for the comparative analysis. It is revealed through the comparative studies that the developed method with PSO has smaller value of MPDI (= 0.0041%) and OMDI (0.005) than the other existing methods. Further, the convergence of the developed method has also been estimated to check the speed of estimation. It is shown that the developed technique converges only in 5 s. In addition, the developed technique avoids the need of extensive data as it is based on manufacturer datasheet.

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“…However, when using VMD method to decompose signals, it is necessary to manually select the number of decomposition layers and other parameters. It is worth noting that the reasonable selection of model parameters is critical to the whole detection system (Chauhan et al, 2022). To sum up, although the above traditional models have achieved good detection results, they are relatively dependent on signal processing and artificial feature extraction, and the model lacks universality and intelligence.…”

Section: Introductionmentioning

confidence: 99%

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

Yan

Liu

et al. 2023

Transactions of the Institute of Measurement and Control

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The leakage of natural gas pipelines may cause significant safety accidents and cause severe economic losses to society. Therefore, timely detection and location of pipeline leak is an effective measure to reduce losses. Traditional methods need to extract the leak features from signals and then input them into convolutional neural networks to realize leakage diagnosis, which is a tedious process. This paper proposes a pipeline leak detection method based on residual connections and one-dimensional separable convolutional neural network (1D-RC-SCNN) and optimizes the structure and parameters of the model. The model uses one-dimensional separable convolution to replace the traditional convolutional neural network, which effectively reduces the number of model parameters. The improved residual connection can prevent the training data from overfitting and speed up the model convergence. The leakage signal after segmentation and preprocessing is directly sent to the 1D-RC-SCNN model, the leakage features are adaptively extracted, and then, the results are output through the Softmax classifier. The method directly performs end-to-end processing on the leaked signal, which improves the data processing speed. Finally, the cross-correlation method is used to locate the pipeline leak. The effectiveness of the proposed method is verified by building an experimental platform and compared with related models. The experimental results show that the method has high accuracy for the detection and localization of pipeline leak.

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Approximating parameters of photovoltaic models using an amended reptile search algorithm

Cited by 22 publications

References 42 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

An efficient data sheet based parameter estimation technique of solar PV

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

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