Performance Modeling of the Weather Impact on a Utility-Scale PV Power Plant in a Tropical Region

Gopi, Ajith; Sudhakar, K.; Ngui, Wai Keng; Krishnan, Ananthu R.; Priya, S. Shanmuga

doi:10.1155/2021/5551014

Cited by 29 publications

(10 citation statements)

References 28 publications

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“…e study sheds light on the performance of solar power plants connected to the medium-term grid in real-world settings in central India. e plant's standard performance ratio and capacity factor were discovered to be 80.72 percent and 19.27 percent, respectively; [18] included a comprehensive investigation of the PV plant's performance over time and estimated the energy output based on key meteorological data gathered from a solar radiation resource assessment (SRRA) station established at the PV power plant's site. e wet seasons have an impact on solar output and performance, which is a common occurrence in the humid tropical area.…”

Section: Related Literaturementioning

confidence: 99%

Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Yadav

Kannan

Meraj

et al. 2022

Mathematical Problems in Engineering

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Climate change and pollution are serious issues that are driving people to adopt renewable energy instead of fossil fuels. Most renewable energy technologies rely on atmospheric conditions to generate power. Solar energy is a renewable energy source that causes the least environmental damage. Solar energy can be converted to electricity, which necessitates the use of a PV system. This study presents a design, which analyses the output power performance of PV, using machine learning technique in India and Malaysia; using this, we would get the predicted amount of solar power using different weather conditions for both India and Malaysia. This study is divided into two sections, such as the data collection section and the implementation system. Dataset was collected from a weather NASA website, which took various weather parameters, based on which the model will be evaluated. The proposed research work is developed using ANN and is an amalgamation of statistical regression and neural networks, which help the model to get high accuracy by helping the model learn more complex relationships between parameters, which is able to evaluate the output power performance of photovoltaic cells with different environmental condition parameters in India and Malaysia. The ANN models are found to successfully predict PV output power with root mean square error (RMSE) of 1.5565, which was used as a measure of our model’s accuracy. This ANN model also outperforms other models available in the literature. This will have a noteworthy contribution in scaling the PV deployment in countries such as India and Malaysia and will increase the share of PV power in their national power production, as it would give the industry and the two countries an idea as to how the predicted output PV power would vary based on weather conditions, such as temperature.

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Section: Related Literaturementioning

confidence: 99%

Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Yadav

Kannan

Meraj

et al. 2022

Mathematical Problems in Engineering

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“…A combined visualization of monthly unit/kWp simulated and actual generation has been carried out, considering the methodologies detailed in [43][44][45][46][47] and illustrated in Figure 14. It can be easily visualized that in the summer months, the two seasonal Plant-2 performs better as compared to the fixed tilt plant in both simulation and actual, and it satisfies the design of PV plants on an annual basis.…”

Section: Impact Of Tilt Angle On Yearly Plant Performancementioning

confidence: 99%

Performance Evaluation of a MW-Size Grid-Connected Solar Photovoltaic Plant Considering the Impact of Tilt Angle

Sharma

Mahela²,

Hussien

et al. 2022

Sustainability

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This paper presents a study for the estimation of generation from a large-scale, grid-interfaced solar PV plant using the PVsyst software. This study aims to investigate the effect of tilt angle on the performance of the grid-integrated solar PV plant. Two types of tilt angle test plants, i.e., a fixed tilt angle of 30° (1 MW) and two seasonal tilt angles, in summer 13° and in winter 30° (2.5 MW), have been selected at the same location in Bikaner, India. The performance of the proposed test solar power plants, rated at 1 MW (fixed tilt angle) and 2.5 MW (two seasonal tilt angles), is established by comparing the results obtained using the PVsyst software with the practical data of annual solar insolation. It is established that the radiation incident on PV modules will increase by 2.41% if two seasonal tilt angles are considered. Hence, the annual capacity utilization factor (CUF) has increased by 0.26%. Furthermore, it is established that the proposed method’s performance is superior compared to the statistical methods reported in the literature.

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“…One of the recent articles by Gopi et al 19 presented a new performance modeling to analyze and model the effect of weather impact on performance of a utility-scale PVPP in a tropical region. They verified their model measured performance data from a 2 MW PVPP.…”

Section: Introductionmentioning

confidence: 99%

A countrywide analysis of 27 solar power plants installed at different climates

Özden

2022

Sci Rep

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The world is still heavily using nonconventional energy sources, which are worryingly based on carbon. The step is now alternative energy sources hoping that they will be more environmentally friendly. One of the important energy conversion forms by using these sources is photovoltaic solar systems. These type of power plants is on the increase in everyday on the world. Before investment a solar power plant in a specified region, a techno-economic analyse is performed for that power plant by using several meteorological data like solar irradiance and ambient temperature. However, this analyses generally lacks evaluation on effects of climatic and geographical conditions. In this work, 5 years of data of 27 grid-connected photovoltaic power plants are investigated, which are installed on seven different climate types in Turkey. Firstly, the power plants are categorized considering the tilt angles and Köppen–Gieger climate classification. The performance evaluations of the plants are mainly conducted using monthly average efficiencies and specific yields. The monthly average efficiencies, which were classified using the tilts and climate types were from 12 to 17%, from 12 to 16% and from 13 to 15% for tilts 30°/10°, 25° and 20°, respectively. The variation in the specific yields decrease with elevation as y(x) = − 0.068x + 1707.29 (kWh/kWp). As the performances of photovoltaic systems for some locations within the Csb climatic regions may relatively lower than some other regions with same climate type. Thus, techno-economic performance for PVPP located in this climate classification should be carefully treated.

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Performance Modeling of the Weather Impact on a Utility-Scale PV Power Plant in a Tropical Region

Cited by 29 publications

References 28 publications

Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Performance Evaluation of a MW-Size Grid-Connected Solar Photovoltaic Plant Considering the Impact of Tilt Angle

A countrywide analysis of 27 solar power plants installed at different climates

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