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
It is of great importance to study the characteristics of wind power output for the healthy and secure & stable of power grid. Based on the actual operating data, the probability distribution of the power fluctuat...It is of great importance to study the characteristics of wind power output for the healthy and secure & stable of power grid. Based on the actual operating data, the probability distribution of the power fluctuations of the wind farm in Hainanand the variation of wind power annual, seasonal, daily active output is analyzed. The study showed thatHainanProvincehas obvious seasonal variation of wind power output characteristics, higher levels of output of the year generally in winter or summer, spring and autumn to contribute small. The average wind power output will contribute to “low day and high night”, with certain peaking capacity. Shorter time scales, changes in the wind power to smaller amount, not to bring too much impact on system operation, while a long time fluctuations affect the scheduling and running on the grid.展开更多
Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam st...Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.展开更多
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.
文摘It is of great importance to study the characteristics of wind power output for the healthy and secure & stable of power grid. Based on the actual operating data, the probability distribution of the power fluctuations of the wind farm in Hainanand the variation of wind power annual, seasonal, daily active output is analyzed. The study showed thatHainanProvincehas obvious seasonal variation of wind power output characteristics, higher levels of output of the year generally in winter or summer, spring and autumn to contribute small. The average wind power output will contribute to “low day and high night”, with certain peaking capacity. Shorter time scales, changes in the wind power to smaller amount, not to bring too much impact on system operation, while a long time fluctuations affect the scheduling and running on the grid.
基金Supported by the National Natural Science Foundation of China(51777015)the Research Foundation of Education Bureau of Hunan Province(20A021).
文摘Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.
