Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review

Ajbar, Wassila; Parrales, A.; Huicochea, A.; Hernández, J.A.

doi:10.1016/j.rser.2021.111947

Cited by 47 publications

(8 citation statements)

References 282 publications

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“…According to Rao et al [38] and Ajbar et al [39], ANN models have the follo characteristics: According to Rao et al [38] and Ajbar et al [39], ANN models have the following characteristics:…”

Section: Peer Reviewmentioning

confidence: 99%

Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Gopi

Sharma²,

Sudhakar

et al. 2022

Sustainability

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Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system’s annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson’s R, coefficient of determination (R2), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor’s diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers.

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Section: Peer Reviewmentioning

confidence: 99%

Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Gopi

Sharma²,

Sudhakar

et al. 2022

Sustainability

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show abstract

“…In the field of CSP technologies, the parabolic trough collector (PTC) and the compound parabolic collector (CPC) [2,3] are the most investigated solar collectors with linear geometry. Researchers interested in this field can find multiple reviews on the state of the Energies 2024, 17, 1582 2 of 32 art [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Review of the Modeling of Parabolic Trough Solar Collectors Coupled to Solar Receivers with Photovoltaic/Thermal Generation

Chavarría-Domínguez,

De León-Aldaco,

Velázquez-Limón

et al. 2024

Energies

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This paper is a summary of the last ten years of work on the study of parabolic trough collectors (PTCs) and compound parabolic collectors (CPCs) coupled to photovoltaic and thermal solar receiver collectors (SCR-PVTs). While reviewing the state of the art, numerous review papers were found that focused on conventional solar receiver collector (SRC) technology for solar thermal generation. However, there is a lack of review papers summarizing SRC-PVT hybrid technology for solar electric/thermal generation, which would be beneficial for researchers interested in this area of research. This paper provides a review of SRC-PVT hybrid technologies. The theoretical foundations for analyzing and modeling PTC and CPC concentrators coupled to SRC-PVT are described, with an emphasis on modeling through thermal resistances and energy balances. Additionally, this section provides a concise overview of previous studies that have addressed the modeling of PTC and CPC collectors coupled to SCR-PVT, as well as experimental information useful for the validation of new mathematical models of SRC-PVT.

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“…They discovered once more that the CuO nanoparticle is the most effective, increasing thermal efficiency by 1.26% compared with Al 2 O 3 ’s 1.13%. Ajbar et al (2022) presented a thorough analysis of studies that used experimental, numerical and combined numerical–experimental methods to compile various factors that contribute to better PTSC performance. According to the study, the dispersion of Cu and CuO in the base fluid significantly increased the heat transfer coefficient compared with other nanoparticles.…”

Section: Introductionmentioning

confidence: 99%

Numerical and experimental studies of thermal performance enhancement for parabolic trough solar collector using none-circulated CuO/synthetic oil nanofluid

Elmnefi,

Al-Khazraji

2023

HFF

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Purpose One of the existing and commonly used solar energy harvesting devices is the parabolic trough solar collector (PTSC). Because of their ability to operate in low and medium temperatures, parabolic trough concentrators are widely used in power generation plants and industrial process heating applications. Therefore, the investigation of how different operating conditions affect these devices’ overall efficiency has received a great deal of attention in the recent decade. This study aims to enhance the thermal performance of the PTSC and reduce the system cost. Design/methodology/approach In the novel configuration, a noncirculated nanofluid absorbs solar radiation through a glass wall. The base fluid was synthetic oil (5W30), and the nanoparticles used were copper oxide. The heat captured is immediately absorbed by the water circulating inside the copper tube immersed in the nanofluid. ANSYS FLUENT 15.0 was used for carrying out computational fluid dynamics simulations for two models of single and triple copper tubes. The experimental results obtained from a test rig constructed for this purpose were compared with the numerical outcomes of the single copper tube model. Findings The findings of the simulation demonstrated that performance was superior for the single copper tube model over the triple copper tube model. The numerical findings of the single copper tube model were compared with the experimental results. The numerical and experimental results differed from 3.17% to 5.6%. Investigations were carried out to study the effects of varying the volumetric flow rate of (20, 40, 60 and 80 L/h) and water inlet temperatures of (300, 315 and 330 K) on the effectiveness and performance of the newly developed model. Additionally, two nanofluid volume fractions of 0.05% and 0.075% were used for investigating their effect on the performance of the novel configuration. According to the findings, the highest thermal efficiency of 55.31% was recorded at 0.075% concentration and 80 L/h volume flow rate. Originality/value In this study, a novel direct absorption solar collector configuration using a noncirculated nanofluid was designed to enhance the thermal efficiency of PTSC. This new approach makes it possible to boost the thermal performance of the PTSC and lower the system’s cost.

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Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review

Cited by 47 publications

References 282 publications

Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

A Review of the Modeling of Parabolic Trough Solar Collectors Coupled to Solar Receivers with Photovoltaic/Thermal Generation

Numerical and experimental studies of thermal performance enhancement for parabolic trough solar collector using none-circulated CuO/synthetic oil nanofluid

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