In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling met...In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.展开更多
To explore the non-linear relationship between risk sources and the hazard degree levels of accidents,and to precisely predict the hazard impact of metro operation accidents,we pro-pose the ordered constraint Apriori-...To explore the non-linear relationship between risk sources and the hazard degree levels of accidents,and to precisely predict the hazard impact of metro operation accidents,we pro-pose the ordered constraint Apriori-RF method for forecasting metro operation accident hazard degree levels.First,the hazard degree of metro operation accidents is quantified from three dimensions:casualties,train delays,and facility damages.K-means clustering is then applied to categorize hazard degree levels.Second,the ordered constraint Apriori algorithm is employed to mine valid association rules between metro operation risk sources and accident hazard degree levels.These valid association rules are subsequently employed in the random forest(RF)algorithm for training,establishing a reliable and accu-rate prediction model.Finally,the method is validated using metro accident data from a city in China.The research results indicate that the ordered constraint Apriori-RF method enhances the effectiveness of association rule mining by 74.9%and exhibits higher compu-tational efficiency.The predicted values of the ordered constraint Apriori-RF method have small errors.Compared to traditional RF algorithms,the root mean square error(RMSE)is reduced by 14%,and the weighted root mean square error(WRMSE)is reduced by 36%,demonstrating the higher accuracy of the ordered constraint Apriori-RF method and its clear advantages.The research findings provide a precise and effective method for quanti-tatively predicting the hazard degree levels of metro operation accidents,holding signifi-cant theoretical and practical value in ensuring metro operation safety and implementing accident mitigation and prevention measures.展开更多
基金supported by Science and Technology project of the State Grid Corporation of China“Research on Active Development Planning Technology and Comprehensive Benefit Analysis Method for Regional Smart Grid Comprehensive Demonstration Zone”National Natural Science Foundation of China(51607104)
文摘In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.
基金supported by"The Shanghai Philosophy and Social Science Planning Project"under Grant 2022BGL001.
文摘To explore the non-linear relationship between risk sources and the hazard degree levels of accidents,and to precisely predict the hazard impact of metro operation accidents,we pro-pose the ordered constraint Apriori-RF method for forecasting metro operation accident hazard degree levels.First,the hazard degree of metro operation accidents is quantified from three dimensions:casualties,train delays,and facility damages.K-means clustering is then applied to categorize hazard degree levels.Second,the ordered constraint Apriori algorithm is employed to mine valid association rules between metro operation risk sources and accident hazard degree levels.These valid association rules are subsequently employed in the random forest(RF)algorithm for training,establishing a reliable and accu-rate prediction model.Finally,the method is validated using metro accident data from a city in China.The research results indicate that the ordered constraint Apriori-RF method enhances the effectiveness of association rule mining by 74.9%and exhibits higher compu-tational efficiency.The predicted values of the ordered constraint Apriori-RF method have small errors.Compared to traditional RF algorithms,the root mean square error(RMSE)is reduced by 14%,and the weighted root mean square error(WRMSE)is reduced by 36%,demonstrating the higher accuracy of the ordered constraint Apriori-RF method and its clear advantages.The research findings provide a precise and effective method for quanti-tatively predicting the hazard degree levels of metro operation accidents,holding signifi-cant theoretical and practical value in ensuring metro operation safety and implementing accident mitigation and prevention measures.
