Analysis and research on chaotic dynamics behaviour of wind power time series at different time scales

2021 ◽  
Author(s):  
Zhongda Tian
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Time Scales ◽  
Chaotic Dynamics ◽  
Different Time Scales

scholarly journals Characterizing ecosystem-atmosphere interactions from short to interannual time scales

2007 ◽  
Vol 4 (3) ◽  
pp. 1405-1435
Author(s):  
M. D. Mahecha ◽  
M. Reichstein ◽  
H. Lange ◽  
N. Carvalhais ◽  
C. Bernhofer ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
System Analysis ◽  
Singular System ◽  
Low Frequency ◽  
Data Adaptive ◽  
Effective Signal ◽  
Observation Space ◽  
Different Time Scales ◽  
Observation Period

Abstract. Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series are now partly comprising an observation period of one decade. In this study, we explored whether the observation period is already sufficient to detect cross relationships of the variables beyond the annual cycle as they are expected from comparable studies in climatology. We explored the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an effective signal to noise separation. We found that most observations (NEE, GPP, Reco, VPD, LE, H, u, P) were influenced significantly by low frequency components (interannual variability). Furthermore we extracted a set of nonlinear relationships and found clear annual hysteresis effects except for the NEE-Rg relationship which turned out to be the sole linear relationship in the observation space. SSA provides a new tool to investigate these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing since it can be applied as novel gap filling approach relying on the temporal time series structure only.

scholarly journals Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3427 ◽  
Author(s):  
Geovanny Marulanda ◽  
Antonio Bello ◽  
Jenny Cifuentes ◽  
Javier Reneses
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Time Scales ◽  
Electricity Markets ◽  
Distribution Functions ◽  
Wind Power Generation ◽  
Environmental Benefits ◽  
Different Time Scales ◽  
Temporal Dependencies

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.

POPULATION GROWTH MODELING WITH BOOM AND BUST PATTERNS: THE IMPULSIVE DIFFERENTIAL EQUATION FORMALISM

2015 ◽  
Vol 23 (supp01) ◽  
pp. S135-S149 ◽  
Author(s):  
FERNANDO CÓRDOVA-LEPE ◽  
GONZALO ROBLEDO ◽  
JAVIER CABRERA-VILLEGAS
Keyword(s):  
Differential Equation ◽  
Population Dynamics ◽  
Time Scales ◽  
Chaotic Dynamics ◽  
Impulsive Differential Equation ◽  
Dynamical Behavior ◽  
Single Species ◽  
Open Problems ◽  
Boom And Bust ◽  
Different Time Scales

This note gives an overview on basic mathematical models describing the population dynamics of a single species whose vital dynamics has different time scales. We present five cases combining two time–scales with Malthusian growth in at least one scale. The dynamical behavior shows a progressive complexity, from "naive" to chaotic dynamics (in the Li–Yorke's sense). In addition, some open problems and new results are presented.

scholarly journals The Empirical Adaptive Wavelet Decomposition (EAWD): An adaptive decomposition for the variability analysis of observation time series in atmospheric science

2022 ◽  
Author(s):  
Olivier Delage ◽  
Thierry Portafaix ◽  
Hassan Bencherif ◽  
Alain Bourdier ◽  
Emma Lagracie
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Observation Time ◽  
Stationary Time Series ◽  
Original Time Series ◽  
Adaptive Wavelet ◽  
Time Frequency ◽  
Variability Analysis ◽  
Original Time ◽  
Different Time Scales

Abstract. Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several time and space scales. As a consequence, measurements time series have often characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different time-scales. The variability analysis of a time series consists in decomposing it into several mode of variability, each mode representing the fluctuations of the original time series at a specific time-scale. Such a decomposition enables to obtain a time-frequency representation of the original time series and turns out to be very useful to estimate the dimensionality of the underlying dynamics. Decomposition techniques very well suited to non-linear and non-stationary time series have recently been developed in the literature. Among the most widely used of these technics are the empirical mode decomposition (EMD) and the empirical wavelet transformation (EWT). The purpose of this paper is to present a new adaptive filtering method that combines the advantages of the EMD and EWT technics, while remaining close to the dynamics of the original signal made of atmospheric observations, which means reconstructing as close as possible to the original time series, while preserving its variability at different time scales.

scholarly journals Characterizing ecosystem-atmosphere interactions from short to interannual time scales

2007 ◽  
Vol 4 (5) ◽  
pp. 743-758 ◽  
Author(s):  
M. D. Mahecha ◽  
M. Reichstein ◽  
H. Lange ◽  
N. Carvalhais ◽  
C. Bernhofer ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
System Analysis ◽  
Ecosystem Respiration ◽  
Singular System ◽  
Global Radiation ◽  
Net Ecosystem Exchange ◽  
Temporal Correlation ◽  
Different Time Scales ◽  
Observation Period

Abstract. Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series currently comprise an observation period of up to one decade. In this study, we explored whether the observation period is already sufficient to detect cross-relationships between the variables beyond the annual cycle, as they are expected from comparable studies in climatology. We investigated the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an effective signal to noise separation. We found that most observations (Net Ecosystem Exchange, NEE, Gross Primary Productivity, GPP, Ecosystem Respiration, Reco, Vapor Pressure Deficit, VPD, Latent Heat, LE, Sensible Heat, H, Wind Speed, u, and Precipitation, P) were influenced significantly by low-frequency components (interannual variability). Furthermore, we extracted a set of nontrivial relationships and found clear seasonal hysteresis effects except for the interrelation of NEE with Global Radiation (Rg). SSA provides a new tool for the investigation of these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing, since it can be applied as a novel gap filling approach relying on the temporal correlation structure of the time series structure only.

Detection of Rainfall and Runoff Trends of the Adda River in Lecco (1845-2014) at Different Time Scales

10.29007/gbqh ◽  
2018 ◽  
Author(s):  
Roberto Ranzi ◽  
Massimo Tomirotti ◽  
Michele Brunetti ◽  
Alice Crespi ◽  
Maurizio Maugeri
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Statistical Tests ◽  
Annual Runoff ◽  
Water Levels ◽  
Cycle Period ◽  
Catchment Scale ◽  
Significance Level ◽  
Runoff Series ◽  
Different Time Scales

A recovery of ancient records of the Como Lake water levels at the Fortilizio in Lecco hydrometric station enabled the reconstruction of a time series of daily water level and runoff from the Como Lake spanning the 1845-2014 period. In parallel, the monthly areal precipitation at the Adda river catchment scale was estimated for the same 170 years- long period. This time series, which is one of the longest available for Italian riverbasins will support analyses of the reasons of changes in the runoff regime in response to climatic and anthropogenic changes. A comparison of the two series applying the Mann- Kendall, Spearman and Theil-Sen trend tests, shows a decline, in the long term, of runoff and a more significant one of precipitation. Because some changes in the operation at the outlet of the Como Lake occurred after 1946 and also in the storage capacity of the upstream reservoirs the time series was splitted in two periods, before and after 1946. The results of the statistical tests for both precipitation and runoff in three time periods are consistent, but only for the time series of annual runoff the decline is statistically significant with 5% significance level. To analyse if changes occurred at different time scales the wavelet transform was applied to the daily runoff series. Finally the Fourier power spectrum of the the daily runoff data shows a signal of higher energy corresponding to a period between 11 and 13 years, close to the sunspots cycle period, and its significance is under investigation.

Data-driven stochastic model for cross-interacting processes with different time scales

2021 ◽  
Author(s):  
Andrey Gavrilov ◽  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Alexander Feigin
Keyword(s):  
Time Series ◽  
Stochastic Model ◽  
Time Scales ◽  
Time Scale ◽  
Sampling Interval ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Middle Pleistocene ◽  
Model Based ◽  
Different Time Scales

<p>The problem of modeling interaction between processes with different time scales is very important in geoscience. In this report, we propose a new form of empirical evolution operator model based on the analysis of multiple time series representing processes with different time scales. We assume that the time series are given on the same time interval.</p><p>To construct the model, we extend the previously developed general form of nonlinear stochastic model based on artificial neural networks and designed for the case of time series with constant sampling interval [1]. This sampling interval is related to the main time scale of the process under consideration, which is described by the deterministic component of the model, while the faster time scales are modeled by its stochastic component, possibly depending on the system’s state. This model also includes slower processes in the form of weak time-dependence, as well as external forcing. The structure of the model is optimized using Bayesian approach [1]. The model has proven its efficiency in a number of applications [2-4].</p><p>The idea of modeling time series with different time scales is to formulate the above-described model individually for each time scale, and then to include the parameterized influence of the other time scales in it. Particularly, the influence of “slower” time series is included in the form of parameter trends, and the influence of “faster” time series is included by time-averaging their statistics. The algorithm and first results of comparison between the new model and the model without cross-interactions will be discussed.</p><p>The work was supported by the Russian Science Foundation (Grant No. 20-62-46056).</p><p>1. Gavrilov, A., Loskutov, E., & Mukhin, D. (2017). Bayesian optimization of empirical model with state-dependent stochastic forcing. Chaos, Solitons & Fractals, 104, 327–337. http://doi.org/10.1016/j.chaos.2017.08.032</p><p>2. Mukhin, D., Kondrashov, D., Loskutov, E., Gavrilov, A., Feigin, A., & Ghil, M. (2015). Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models. Journal of Climate, 28(5), 1962–1976. http://doi.org/10.1175/JCLI-D-14-00240.1</p><p>3. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2019). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics, 52(3–4), 2199–2216. http://doi.org/10.1007/s00382-018-4255-7</p><p>4. Mukhin, D., Gavrilov, A., Loskutov, E., Kurths, J., & Feigin, A. (2019). Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition. Scientific Reports, 9(1), 7328. http://doi.org/10.1038/s41598-019-43867-3</p>

Analysis on the Fluctuation of Wind Power

2015 ◽  
Vol 733 ◽  
pp. 199-202
Author(s):  
Rui Hao Wang
Keyword(s):  
Neural Network ◽  
Energy Storage ◽  
Wind Power ◽  
Time Scales ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Power Fluctuation ◽  
Power Fluctuations ◽  
Different Time Scales

This paper is aimed at exploring the characteristic fluctuation of wind power based on samples from a certain wind farm. First, the paper is to analyze fluctuations of wind power at different time scales. According to a sliding difference algorithm to build wind power fluctuations evaluation. Wind power fluctuation index for different time scales are used to fit probability distributions, indicating that the best form of distribution of wind power fluctuations is t location scale distribution. Secondly, considering the wind power has the characteristics of non-linear, non-stationary signal of the data, it fully meets the wavelet neural network analysis of the characteristics of the data. Therefore, select wavelet neural network training and testing so as to make predictions about the future of the total power of wind farm. It points out the differences between different regions covered by the index from the fluctuation characteristics of wind power, thus further understanding the fluctuation characteristics of wind power: Influenced by the time and space distribution and other factors, there is a big difference between the output power fluctuation characteristics of single wind generator and wind farm, which is because of the different wind machine in the field by the wind energy differences, and the wake effect of organic groups, making frequent fluctuations in power distribution; the fluctuation of wind is gentle, i.e. with increasing spatial distribution scale, so gentle effect occurs to wind power fluctuations. Finally, through the analysis of the fluctuation characteristics of power, power factor and analyses the influence of the characteristics of fluctuation, the paper draws a conclusion of the following improvement programs to overcome the adverse effects of wind power fluctuation of power grid operation: the rational allocation of energy storage devices, expanding the coverage area of a wind farm, or improving the design of the windmill, which will make wind farms adapt to different wind directions, thus eliminating the impact of fluctuations on the power grid from the wind farm power output by the energy storage device, and covering the area of large wind farms can adapt to different wind directions, and with power complementary, it has achieved the amount of stable power transmission into the grid.

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