Wavelet Multi-Scale Analysis of Wind Turbines Smoothing Effect and Power Fluctuations

Meglic, Antun; Goić, Ranko

doi:10.1109/irsec53969.2021.9741097

Cited by 4 publications

(3 citation statements)

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“…Fit: sigmoid curve Calculated Previous papers [26,27] show that the maximum theoretical smoothing effect in wind farms can be observed for periods of fluctuations below approximately 100 s, independently on the number of WTGs in the wind farm. Generally, the frequency scales at which smoothing effects in wind farms approach maximum/minimum theoretical limits can also slightly change depending on the wind farm terrain complexity and the distance between WTGs, as noted by the authors in [25]. Small changes between smoothing exponent curves can also be expected for different seasons.…”

Section: Smoothing Exponents ( )mentioning

confidence: 86%

“…The MODWT is used to derive scale-based SEI in an approach previously used by the authors in [25] to inspect power fluctuations and smoothing effects in wind farms. The SEI as the representation of power fluctuations on various frequency scales is analysed by (a) aggregating power time series of N = 1, 2, ..., M wind turbines, where M is the total number of WTGs in wind farm; (b) for each N, MODWT is applied on aggregated power time series to obtain multiple time scales representations; (c) estimating wavelet variance (unbiased estimation according to [23]) for each scale; (d) SEI calculation by taking the square root of previously-obtained variances.…”

Section: Power Fluctuations and Smoothing Effectmentioning

confidence: 99%

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Impact of Time Resolution on Curtailment Losses in Hybrid Wind-Solar PV Plants

Meglic

Goić

2022

Energies

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Curtailment losses for large-scale hybrid wind–solar photovoltaic (PV) plants with a single grid connection point are often calculated in 1 h time resolution, underestimating the actual curtailment losses due to the flattening of power peaks occurring in shorter time frames. This paper analyses the curtailment losses in hybrid wind–PV plants by utilising different time resolutions of wind and PV production while varying the grid cut-off power, wind/solar PV farm sizes, and shares of wind/PV capacity. Highly resolved 1 s measurements from the operational wind farm and pyranometer are used as an input to specialized wind and PV farm power production models that consider the smoothing effect. The results show that 15 min resolution is preferred over 1 h resolution for large-scale hybrid wind–PV plants if more accurate assessment of curtailment losses is required. Although 1 min resolution additionally increases the estimation accuracy over 15 min resolution, the improvement is not significant for wind and PV plants with capacity above approx. 10 MW/10 MWp. The resolutions shorter than 1 min do not additionally increase the estimation accuracy for large-scale wind and PV plants. More attention is required when estimating curtailment losses in wind/PV plants with capacity below approx. 10 MW/10 MWp, where higher underestimation can be expected if lower time resolutions are used.

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Section: Smoothing Exponents ( )mentioning

confidence: 86%

Section: Power Fluctuations and Smoothing Effectmentioning

confidence: 99%

Impact of Time Resolution on Curtailment Losses in Hybrid Wind-Solar PV Plants

Meglic

Goić

2022

Energies

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“…. While it occurs to the MODWT scale filter, the same thing applies: if the TWD scale filter is written as (Meglic & Goic, 2021) 𝑔 = [𝑔 0 , 𝑔 1 , 𝑔 2 , ⋯ , 𝑔 𝐿−1 ] then the MODWT scale filter can be written as 𝑔 ̃= [𝑔 ̃0, 𝑔 ̃1, 𝑔 ̃2, ⋯ , 𝑔 ̃𝐿−1 ] where 𝑔 ̃𝑙 = 𝑔 𝑙…”

Section: Maximum Overlap Discrete Wavelet Transform (Modwt) Methodsmentioning

confidence: 99%

The MODWT-ARIMA Model in Forecasting The COVID-19 Cases

Imro'ah,

Huda

2023

JTAM

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The Maximal Overlap Discrete Wavelet Transform-Autoregressive Integrated Moving Average (MODWT-ARIMA) is a forecasting method that uses the ARIMA model generated from MODWT data. The purpose of this study is to analyze an investigation into the MODWT-ARIMA model with regard to the total number of COVID-19 cases in DKI Jakarta. For this study, daily data on cases of Covid-19COVID-19 in DKI Jakarta were obtained. The model is trained with data from April 3, 2022, to June 11, 2022 (referred to as the "in-sample"), and the outcomes of the prediction are tested with data from June 12, 2022, to June 18, 2022 (referred to as the "out-sample"). These data exhibit trends and are organizedorganised into four data series using MODWT. The ARIMA modelling technique is applied to each of the produced sequences. When using the MODWT-ARIMA approach, the RMSE value obtained from the in-sample data is found to be lower than the RMSE value obtained from the out-sample data. In light of the findings, Iit t became clear that MODWT-ARIMA is better suited for estimation than prediction. The fact that the RMSE value for the data acquired from the in-sample is lower than the RMSE value for the data collected from the out-sample demonstrates that this is the case.

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