Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical str...Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.展开更多
A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area d...A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.展开更多
基金Supported by the National Natural Science Foundation of China(61374166,6153303)the Doctoral Fund of Ministry of Education of China(20120010110010)the Fundamental Research Funds for the Central Universities(YS1404,JD1413,ZY1502)
文摘Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.
基金supported in part by National Key Basic Research Program of China(973 Program:2013CB228205)National Natural Science Foundation of China(51177051,51477055).
文摘A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.
