Analysis of wind characteristics parameters with the application of lidar and mast

Sharma, Pramod Kumar; Gautam, Atul; Warudkar, Vilas; Ahmed, Siraj; Bhagoria, J.L.

doi:10.1002/we.2580

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Some scholars have also used probability distribution and parameter estimation methods to fit wind data and evaluate wind potential. Sharma P K et al 10 compared the effect of different probability distribution models on wind data fitting. Gautam et al 11 compared 11 Weibull parameter estimation methods and presented the applicable scope, advantages, and disadvantages of each method based on empirical analysis.…”

Section: Introductionmentioning

confidence: 99%

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Jiang

2023

Sci Rep

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A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal–trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) and a dependency matrix attention mechanism (DMAttention) based on cosine similarity to predict the concentration of air pollutants. This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. In this paper, the long short-term memory network (LSTM) and the gated recurrent unit network (GRU) are set as the baseline models to design experiments. At the same time, to test the generalization performance of the model, short-term forecasts in hours were performed using PM2.5, PM10, SO2, NO2, CO, and O3 data. The experimental results show that the model proposed in this paper is superior to the comparison model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE values of the 6 kinds of pollutants are 6.800%, 10.492%, 9.900%, 6.299%, 4.178%, and 7.304%, respectively. Compared with the baseline LSTM and GRU models, the average reduction is 49.111% and 43.212%, respectively.

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Section: Introductionmentioning

confidence: 99%

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Jiang

2023

Sci Rep

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“…Several scanning technologies and strategies have been adopted to probe the atmospheric boundary layer for wind energy applications. Profiling lidars have been deployed for freestream resource assessment (Gottschall et al, 2012;Optis et al, 2021;Sharma et al, 2021), wake characterization (Heisel et al, 2018) and plant induction quantification (Jacquet et al, 2022;Letizia et al, 2022). Scanning lidars, i.e., those that feature the ability to direct the laser beam in arbitrary directions (among other abilties), offer additional flexibility and permit a more targeted characterization of the wind plant flow.…”

Section: Introductionmentioning

confidence: 99%

Characterization of wind turbine flow through nacelle-mounted lidars: a review

Letizia,

Brugger,

Bodini

et al. 2023

Front. Mech. Eng.

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This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic view of the inflow and wake characteristics required to characterize and optimize wind turbine performance, carry out model validation and calibration, and aid in real-time control. The need to balance the enhanced capabilities and limitations of lidars compared to traditional anemometers inspired a broad variety of approaches for scan design and wind reconstruction, which we discuss in this review. We give particular emphasis to identifying common guidelines and gaps in the available literature with the aim of providing an exhaustive picture of the state-of-the-art techniques for reconstructing wind plant flow using nacelle-mounted lidars.

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“…It is a graphical technique that provides information about the direction and frequency of wind at a site. A wind rose diagram is a circular diagram that shows the percentage of time the wind blows from different directions [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Wind Power Potentials in Chittagong, Bangladesh Using Weibull Distribution Function

Tarik Aziz Patwary,

Djamal Hissein Didane,

Sami Al-Alimi

et al. 2023

ARFMTS

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Wind resource assessment is a key stage in planning for a wind energy project to predict the output power. Therefore, a full understanding of the wind characteristics at the potential site is required. This study examines the potential wind power of twelve meteorological stations in the Chittagong division over a tenure of ten-year ranges from 2010 to 2019. The statistical analyzer as the Weibull two-parameter distribution method has been utilized to determine the characteristics of wind speed and wind energy potential at the selected locations. The "WRPLOT" software has also been used to display the wind direction and the corresponding wind speed direction. Among these stations, Ambagan has a high wind factor, ranging from 2.1 to 3.2 m/s. The highest wind speed for Ambagan station was recorded in April (3.26 m/s) and the lowest was in November (1.06 m/s) compared to other selected stations. The Weibull form parameter (k) and scale parameter (c) at Ambagan station had the highest values of 1.5 and 3.5 m/s, respectively. With additional analysis and observation of these parameters, it became possible to predict the location in Chittagong with the greatest wind energy potential. In terms of wind direction, the prevailing wind direction of all stations is generally in the south. Furthermore, the results concluded that all of the analyzed parameters considered for wind behavior were numerically higher for Ambagan in contrast to other stations of the Chittagong division. This study clearly indicates that the Ambagan station should be used to maximize the potential of wind energy to attain the future goals of sustainable development for Bangladesh.

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Analysis of wind characteristics parameters with the application of lidar and mast

Cited by 8 publications

References 38 publications

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Characterization of wind turbine flow through nacelle-mounted lidars: a review

Assessment of Wind Power Potentials in Chittagong, Bangladesh Using Weibull Distribution Function

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