Robust Unit Commitment Considering the Temporal and Spatial Correlations of Wind Farms Using a Data-Adaptive Approach

2018 ◽  
Author(s):  
Yipu Zhang ◽  
Xiaomeng Ai ◽  
Jinyu Wen ◽  
Jiakun Fang ◽  
Cheng Luo
Keyword(s):  
Unit Commitment ◽  
Wind Farms ◽  
Spatial Correlations ◽  
Adaptive Approach ◽  
Temporal And Spatial ◽  
Data Adaptive

A low-complexity data-adaptive approach for premature ventricular contraction recognition

2013 ◽  
Vol 8 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Peng Li ◽  
Chengyu Liu ◽  
Xinpei Wang ◽  
Dingchang Zheng ◽  
Yuanyang Li ◽  
...  
Keyword(s):  
Premature Ventricular Contraction ◽  
Low Complexity ◽  
Ventricular Contraction ◽  
Adaptive Approach ◽  
Data Adaptive

scholarly journals Integration Capability Evaluation of Wind and Photovoltaic Generation in Power Systems Based on Temporal and Spatial Correlations

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 171 ◽  
Author(s):  
Hua Zhou ◽  
Huahua Wu ◽  
Chengjin Ye ◽  
Shijie Xiao ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Power Systems ◽  
Wind Power ◽  
Energy Generation ◽  
Copula Model ◽  
Spatial Correlations ◽  
Temporal And Spatial ◽  
Renewable Energy Generation ◽  
Pv Power Generation

With the rapid growth of renewable energy generation, it has become essential to give a comprehensive evaluation of renewable energy integration capability in power systems to reduce renewable generation curtailment. Existing research has not considered the correlations between wind power and photovoltaic (PV) power. In this paper, temporal and spatial correlations among different renewable generations are utilized to evaluate the integration capability of power systems based on the copula model. Firstly, the temporal and spatial correlation between wind and PV power generation is analyzed. Secondly, the temporal and spatial distribution model of both wind and PV power generation output is formulated based on the copula model. Thirdly, aggregated generation output scenarios of wind and PV power are generated. Fourthly, wind and PV power scenarios are utilized in an optimal power flow calculation model of power systems. Lastly, the integration capacity of wind power and PV power is shown to be able to be evaluated by satisfying the reliability of power system operation. Simulation results of a modified IEEE RTS-24 bus system indicate that the integration capability of renewable energy generation in power systems can be comprehensively evaluated based on the temporal and spatial correlations of renewable energy generation.

scholarly journals Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability

2011 ◽  
Vol 2011 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Khademul Islam Molla ◽  
Poly Rani Ghosh ◽  
Keikichi Hirose
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Reference Signal ◽  
Complex Signal ◽  
Intrinsic Mode Functions ◽  
Climate Data ◽  
Climate Signal ◽  
Adaptive Approach ◽  
Climate Change And Variability ◽  
Data Adaptive

This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are collected from a specific area of Bihar in India. Fractional Gaussian noise (fGn) is used here as the reference signal. The climate signal and fGn (of same length) are combined to produce bivariate (complex) signal which is decomposed using BEMD into a finite number of sub-band signals named intrinsic mode functions (IMFs). Both of climate signal as well as fGn are decomposed together into IMFs. The instantaneous frequencies and Fourier spectrum of IMFs are observed to illustrate the property of BEMD. The lowest frequency oscillation of climate signal represents the annual cycle (AC) which is an important factor in analyzing climate change and variability. The energies of the fGn's IMFs are used to define the data adaptive threshold to separate AC. The IMFs of climate signal with energy exceeding such threshold are summed up to separate the AC. The interannual distance of climate signal is also illustrated for better understanding of climate change and variability.

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