There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fa...There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.展开更多
To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the ...To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.展开更多
基金The paper is supported by the 863 Program of China under Grant No 2006AA04A110
文摘There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.
基金supported by Natural Science Foundation of Beijing Municipality(No.3161002)National Key R&D Program(No.2017YFB0903300).
文摘To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.
