Quantizing the deterministic nonlinearity in wind speed time series

Samet, Haidar; Marzbani, Fatemeh

doi:10.1016/j.rser.2014.07.130

Cited by 38 publications

(13 citation statements)

References 47 publications

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“…The scientific research of the topic focuses on understanding the variations of urban air pollutants with respect to time, investigating the contribution of environmental and meteorological parameters to those variations through modeling, forecasting, and predicting future trends and alterations. Noteworthy approaches utilize neural networks [5][6][7][8][9][10][11][12][13][14][15][16], wavelets [17,18], statistical [19,20], and deterministic [21] models, and, currently, cutting-edge techniques based on chaos and complexity (e.g., References [4,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]). Since no forecast method is solid, reliable, and accurate enough to sufficiently match all air-contamination time series [37], it is a challenging task to achieve credible estimations.…”

Section: Introductionmentioning

confidence: 99%

Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece

et al. 2019

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This work examines if chaos and long memory exist in PM 10 concentrations recorded in Athens, Greece. The algorithms of Katz, Higuchi, and Sevcik were employed for the calculation of fractal dimensions and Rescaled Range (R/S) analysis for the calculation of the Hurst exponent. Windows of approximately two months' duration were employed, sliding one sample forward until the end of each utilized signal. Analysis was applied to three long PM 10 time series recorded by three different stations located around Athens. Analysis identified numerous dynamical complex fractal time-series segments with patterns of long memory. All these windows exhibited Hurst exponents above 0.8 and fractal dimensions below 1.5 for the Katz and Higuchi algorithms, and 1.2 for the Sevcik algorithm. The paper discusses the importance of threshold values for the postanalysis of the discrimination of fractal and long-memory windows. After setting thresholds, computational calculations were performed on all possible combinations of two or more techniques for the data of all or two stations under study. When all techniques were combined, several common dates were found for the data of the two combinations of two stations. When the three techniques were combined, more common dates were found if the Katz algorithm was not included in the meta-analysis. Excluding Katz's algorithm, 12 common dates were found for the data from all stations. This is the first time that the results from sliding-window chaos and long-memory techniques in PM 10 time series were combined in this manner.

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Section: Introductionmentioning

confidence: 99%

Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece

et al. 2019

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“…According to the authors, consideration of all these parameters increased the prediction accuracy of the proposed model. A procedure to make judgment about the nonlinearity of wind speed time series was proposed in [6]. The proposed method solves the problem with quantizing the nonlinear deterministic components.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks

2018

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This article suggests the application of multiresolution analysis by Wavelet Transform-WT and Echo State Networks-ESN for the development of tools capable of providing wind speed and power generation forecasting. The models were developed to forecast the hourly mean wind speeds, which are applied to the wind turbine's power curve to obtain wind power forecasts with horizons ranging from 1 to 24 h ahead, for three different locations of the Brazilian Northeast. The average improvement of Normalized Mean Absolute Error-NMAE for the first six, twelve, eighteen and twenty-four hourly power generation forecasts obtained by using the models proposed in this article were 70.87%, 71.99%, 67.77% and 58.52%, respectively. These results of improvements in relation to the Persistence Model-PM are among the best published results to date for wind power forecasting. The adopted methodology was adequate, assuring statistically reliable forecasts. When comparing the performance of fully-connected feedforward Artificial Neural Networks-ANN and ESN, it was observed that both are powerful time series forecasting tools, but the ESN proved to be more suited for wind power forecasting.

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“…Consequently, these key elements improve selectivity and reliability of protection schemes . For this reason, the importance of this protection is growing with increasing penetration of DG units to power systems …”

Section: Introductionmentioning

confidence: 99%

“…5 For this reason, the importance of this protection is growing with increasing penetration of DG units to power systems. 6 On the other hand, in order to handle the increased fault current, protection equipment such as fuses and breakers should be replaced with higher rating equipment, 7 which is a time consuming and expensive approach. Utilizing fault current limiters (FCLs) is one of the most attractive techniques to overcome these problems.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of directional relay algorithms in presence of wind turbines and fault current limiters

Samet

Ghanbari

Ashtiani

et al. 2018

Int Trans Electr Energ Syst

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Summary Connection of distributed generations (DGs) to distribution networks changes their radial construction to mesh type and increases fault current level. In order to prevent over current relay tripping in case of out‐of‐zone faults, direction of faults must be determined. On the other hand, fault current limiters (FCLs) are a suitable technique to reduce the issue of fault current increasing. In this paper, it is confirmed that operation of FCLs affects voltage and current signals, drastically. Since fault direction is detected by directional relays using these signals, hence, FCLs can affect the performance of the relays. In the other hand, if the added DG is a wind turbine, it may affect the directional relays performance due to intricate fault behavior of wind turbines. To investigate the mentioned hypothesis, some of directional relaying algorithms are evaluated in presence of wind turbine and FCL.

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Quantizing the deterministic nonlinearity in wind speed time series

Cited by 38 publications

References 47 publications

Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece

Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece

Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks

Evaluation of directional relay algorithms in presence of wind turbines and fault current limiters

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