The multi-fractal ity over China are studied behaviors of relative humid using the multi-fractal de trended fluctuation analysis （DFA） method. Three multi fractal parameters （the spectrum width Aa, the asymmetry Aa...The multi-fractal ity over China are studied behaviors of relative humid using the multi-fractal de trended fluctuation analysis （DFA） method. Three multi fractal parameters （the spectrum width Aa, the asymmetry Aaas, and the long-range correlation exponent a0） of the singularity spectrum are introduced to quantify the multi-fractal behaviors. The results show that multi-frac tality exists in daily humidity records over most stations in China and is mainly due to the broad distribution of the probability density of the sequence values. Strong multi fractal behaviors over some stations in the Yunnan, Guangdong, and Inner Mongolia provinces are obvious. These behaviors are mainly caused by different long range correlations between large and small fluctuations. The asymmetry of the singularity of relative humidity records is weak, except for a small number of stations in the far east and west of China, where the singularity spec trum is left-skewed. Finally, the long-range correlations in North China are stronger than those in South China, which indicates better predictability in North China. By studying the parameters of the multi-fractal spectrum, various data of long-range power law correlations of the relative humidity records are obtained, which may pro vide theoretical support for climate prediction.展开更多
Flood frequency analysis procedure was performed on annual maximum discharge data of River Oshun at Iwo in Osun State, Nigeria for the period 1985 to 2002 utilizing three probability distribution models namely： Extre...Flood frequency analysis procedure was performed on annual maximum discharge data of River Oshun at Iwo in Osun State, Nigeria for the period 1985 to 2002 utilizing three probability distribution models namely： Extreme EVI （value Type-l）, LN （Log normal） and LPIII （Log Pearson Type III）. The models were used to predict and compare corresponding flood discharge estimates at 2, 5, 10, 25, 50, 100 and 200 years return periods. The results indicated that Extreme Value Type 1 distribution predicted discharge values ranging from 26.6 m3/s for two years to 431.8 m3/s for 200 years return periods; the Log Pearson Type III distribution predicted discharge values ranging from 127.2 m3/s for two years to 399.54 m3/s for 200 years return periods and the Log normal distribution predicted discharge values ranging from 116.2 m3/s for two years to 643.9 m3/s for 200 years return periods. From the results~ it was concluded that for lower return periods （T_〈 50 yrs） Extreme Value Type 1 and Log Pearson Type III could be used to estimate flood quantile values at the station while for higher return periods （T 〉 50 yrs） Log Normal probability distribution model which gives higher estimates could be utilized for safe design in view of the short length of discharge records used for the analysis.展开更多
The fluctuation characteristics is the inherent property of wind power.Through analysis of a large number of wind t＇anns based on measured data,we find it describes the best probability distribution of wind power flu...The fluctuation characteristics is the inherent property of wind power.Through analysis of a large number of wind t＇anns based on measured data,we find it describes the best probability distribution of wind power fluctuation for the mixed Gauss distribution of two components,and try to carry out the physical interpretation of two components.Further discussion is between the probability distribution of fluctuating wind power time difference and whole relationship.It is found that the two have basic similarity.Through comparing the different time level data quantified losses the information of wind power fluctuation,quantitative determination of the degree of impact prediction.We can summarize and understand of wind power fluctuation,constructing instance from the wind farm construction and monitoring prediction two aspect recommendations to overcome the adverse effects of wind power fluctuations on the power grid operation.展开更多
基金supported by the National Natural Science Foundation of China (40975027)
文摘The multi-fractal ity over China are studied behaviors of relative humid using the multi-fractal de trended fluctuation analysis （DFA） method. Three multi fractal parameters （the spectrum width Aa, the asymmetry Aaas, and the long-range correlation exponent a0） of the singularity spectrum are introduced to quantify the multi-fractal behaviors. The results show that multi-frac tality exists in daily humidity records over most stations in China and is mainly due to the broad distribution of the probability density of the sequence values. Strong multi fractal behaviors over some stations in the Yunnan, Guangdong, and Inner Mongolia provinces are obvious. These behaviors are mainly caused by different long range correlations between large and small fluctuations. The asymmetry of the singularity of relative humidity records is weak, except for a small number of stations in the far east and west of China, where the singularity spec trum is left-skewed. Finally, the long-range correlations in North China are stronger than those in South China, which indicates better predictability in North China. By studying the parameters of the multi-fractal spectrum, various data of long-range power law correlations of the relative humidity records are obtained, which may pro vide theoretical support for climate prediction.
文摘Flood frequency analysis procedure was performed on annual maximum discharge data of River Oshun at Iwo in Osun State, Nigeria for the period 1985 to 2002 utilizing three probability distribution models namely： Extreme EVI （value Type-l）, LN （Log normal） and LPIII （Log Pearson Type III）. The models were used to predict and compare corresponding flood discharge estimates at 2, 5, 10, 25, 50, 100 and 200 years return periods. The results indicated that Extreme Value Type 1 distribution predicted discharge values ranging from 26.6 m3/s for two years to 431.8 m3/s for 200 years return periods; the Log Pearson Type III distribution predicted discharge values ranging from 127.2 m3/s for two years to 399.54 m3/s for 200 years return periods and the Log normal distribution predicted discharge values ranging from 116.2 m3/s for two years to 643.9 m3/s for 200 years return periods. From the results~ it was concluded that for lower return periods （T_〈 50 yrs） Extreme Value Type 1 and Log Pearson Type III could be used to estimate flood quantile values at the station while for higher return periods （T 〉 50 yrs） Log Normal probability distribution model which gives higher estimates could be utilized for safe design in view of the short length of discharge records used for the analysis.
文摘The fluctuation characteristics is the inherent property of wind power.Through analysis of a large number of wind t＇anns based on measured data,we find it describes the best probability distribution of wind power fluctuation for the mixed Gauss distribution of two components,and try to carry out the physical interpretation of two components.Further discussion is between the probability distribution of fluctuating wind power time difference and whole relationship.It is found that the two have basic similarity.Through comparing the different time level data quantified losses the information of wind power fluctuation,quantitative determination of the degree of impact prediction.We can summarize and understand of wind power fluctuation,constructing instance from the wind farm construction and monitoring prediction two aspect recommendations to overcome the adverse effects of wind power fluctuations on the power grid operation.
