Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispat...Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are con firmed by case studies.This study provides refere nee for mathematical model of multiojective optimizati on of MG and can be widely used in current research field.展开更多
The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper pro...The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper proposes an MTS variable optimization method based on chaos quantum-behavior particle swarm.In order to avoid the influence of complex collinearity on the distance measurement results,the Gram-Schmidt orthogonalization method is first used to calculate the Mahalanobis distance(MD)value.Then,the optimal threshold point of the system classification is determined through the receiver operating characteristic(ROC)curve;the misclassification rate and the selected variables are defined;the multi-objective mixed programming model is built.The chaos quantum-behavior particle swarm optimization(CQPSO)algorithm is proposed to solve the optimization combination,and the algorithm performs binary coding on the particle based on probability.Using the optimized combination of variables,a new Mahalanobis-Taguchi metric based prediction system is established to complete the task of precise discrimination.Finally,a fault diagnosis for the steel plate is taken as an example.The experimental results show that the proposed method can effectively enhance the iterative speed and optimization precision of the particles,and the prediction accuracy of the optimized MTS is significantly improved.展开更多
基金State Grid Corporation Science and Technology Project(520605190010).
文摘Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are con firmed by case studies.This study provides refere nee for mathematical model of multiojective optimizati on of MG and can be widely used in current research field.
基金the National Natural Science Foundation of China(No.61473144)。
文摘The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper proposes an MTS variable optimization method based on chaos quantum-behavior particle swarm.In order to avoid the influence of complex collinearity on the distance measurement results,the Gram-Schmidt orthogonalization method is first used to calculate the Mahalanobis distance(MD)value.Then,the optimal threshold point of the system classification is determined through the receiver operating characteristic(ROC)curve;the misclassification rate and the selected variables are defined;the multi-objective mixed programming model is built.The chaos quantum-behavior particle swarm optimization(CQPSO)algorithm is proposed to solve the optimization combination,and the algorithm performs binary coding on the particle based on probability.Using the optimized combination of variables,a new Mahalanobis-Taguchi metric based prediction system is established to complete the task of precise discrimination.Finally,a fault diagnosis for the steel plate is taken as an example.The experimental results show that the proposed method can effectively enhance the iterative speed and optimization precision of the particles,and the prediction accuracy of the optimized MTS is significantly improved.
