Currently,the international economic situation is becoming increasingly complex,and there is significant downward pressure on the global economy.In recent years,China’s infrastructure sector has experienced rapid gro...Currently,the international economic situation is becoming increasingly complex,and there is significant downward pressure on the global economy.In recent years,China’s infrastructure sector has experienced rapid growth,with the structure of its power engineering business gradually shifting from traditional infrastructure construction to more diversified areas such as production and operation,as well as emergency repairs.As a result,the transformation of mechanized construction in power transmission and transformation projects has become increasingly urgent.This article proposes a post-evaluation model based on game theory to improve comprehensive weighting and fuzzy grey relational projection sorting,which can be used to evaluate the optimal mechanized construction scheme for power transmission and transformation projects.The model begins by considering the entire lifecycle of power transmission and transformation projects.It constructs a post-evaluation index system that covers the planning and design stage,on-site construction stage,operation and maintenance stage,and the decommissioning and disposal stage,with corresponding calculation methods for each index.The fuzzy grey correlation projection sorting method is then employed to evaluate and rank the construction schemes.To validate the model’s effectiveness,a case study of a power transmission and transformation project in a specific region of China is used.The comprehensive benefits of three proposed mechanized construction schemes are evaluated and compared.According to the evaluation results,Scheme 1 is ranked the highest,with a membership degree of 0.870945,excelling in sustainability.These results suggest that the proposed model can effectively evaluate and make decisions regarding the optimal mechanized construction plan for power transmission and transformation projects.展开更多
In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme l...In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme learning machine(ELM)is proposed.In this paper,the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes,input weights and bias values of the ELM.Therefore,the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient-IPSO-ELM algorithm is constructed.Through the analysis of calculation examples,it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms,which verifies the effectiveness of the model.展开更多
The risk evaluation of power transmission and transformation projects is a complex and comprehensive evaluation process influenced by many factors and involves many indicators.In order to solve the uncertainty and fuz...The risk evaluation of power transmission and transformation projects is a complex and comprehensive evaluation process influenced by many factors and involves many indicators.In order to solve the uncertainty and fuzziness problems in the process of the multilevel fuzzy risk evaluation of power transmission and transformation projects,this paper introduces the cloud theory,which is specialized in the study of uncertainty problems and constructs the multilevel fuzzy comprehensive risk-evaluation model of power transmission and transformation projects based on the improved multilevel fuzzy-thought weighting based on the cloud model.Finally,the risk of the Beijing 220-kV Tangyu power transmission and transformation project is evaluated and the feasibility of the evaluation model is verified.The results of the evaluation and the evaluation layer cloud model are combined with MATLAB simulation to show that the risk level of the project is between large risk and general risk.展开更多
文摘Currently,the international economic situation is becoming increasingly complex,and there is significant downward pressure on the global economy.In recent years,China’s infrastructure sector has experienced rapid growth,with the structure of its power engineering business gradually shifting from traditional infrastructure construction to more diversified areas such as production and operation,as well as emergency repairs.As a result,the transformation of mechanized construction in power transmission and transformation projects has become increasingly urgent.This article proposes a post-evaluation model based on game theory to improve comprehensive weighting and fuzzy grey relational projection sorting,which can be used to evaluate the optimal mechanized construction scheme for power transmission and transformation projects.The model begins by considering the entire lifecycle of power transmission and transformation projects.It constructs a post-evaluation index system that covers the planning and design stage,on-site construction stage,operation and maintenance stage,and the decommissioning and disposal stage,with corresponding calculation methods for each index.The fuzzy grey correlation projection sorting method is then employed to evaluate and rank the construction schemes.To validate the model’s effectiveness,a case study of a power transmission and transformation project in a specific region of China is used.The comprehensive benefits of three proposed mechanized construction schemes are evaluated and compared.According to the evaluation results,Scheme 1 is ranked the highest,with a membership degree of 0.870945,excelling in sustainability.These results suggest that the proposed model can effectively evaluate and make decisions regarding the optimal mechanized construction plan for power transmission and transformation projects.
文摘In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme learning machine(ELM)is proposed.In this paper,the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes,input weights and bias values of the ELM.Therefore,the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient-IPSO-ELM algorithm is constructed.Through the analysis of calculation examples,it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms,which verifies the effectiveness of the model.
文摘The risk evaluation of power transmission and transformation projects is a complex and comprehensive evaluation process influenced by many factors and involves many indicators.In order to solve the uncertainty and fuzziness problems in the process of the multilevel fuzzy risk evaluation of power transmission and transformation projects,this paper introduces the cloud theory,which is specialized in the study of uncertainty problems and constructs the multilevel fuzzy comprehensive risk-evaluation model of power transmission and transformation projects based on the improved multilevel fuzzy-thought weighting based on the cloud model.Finally,the risk of the Beijing 220-kV Tangyu power transmission and transformation project is evaluated and the feasibility of the evaluation model is verified.The results of the evaluation and the evaluation layer cloud model are combined with MATLAB simulation to show that the risk level of the project is between large risk and general risk.
