This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effec...This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the algorithm.In this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE39-bus system, and obtains the Pareto optimal solutions,from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method.展开更多
According to the least square criterion of minimizing the misfit between modeled and observed data, this paper provides a preconditioned gradient method to invert the visco-acoustic velocity structure on the basis of ...According to the least square criterion of minimizing the misfit between modeled and observed data, this paper provides a preconditioned gradient method to invert the visco-acoustic velocity structure on the basis of using sparse matrix LU factorization technique to directly solve the visco-acoustic wave forward problem in space-frequency domain. Numerical results obtained in an inclusion model inversion and a layered homogeneous model inversion demonstrate that different scale media have their own frequency responses, and the strategy of using low-frequency inverted result as the starting model in the high-frequency inversion can greatly reduce the non-tmiqueness of their solutions. It can also be observed in the experiments that the fast convergence of the algorithm can be achieved by using diagonal elements of Hessian matrix as the preconditioned operator, which fully incorporates the advantage of quadratic convergence of Gauss-Newton method.展开更多
基金supported by National Natural Science Foundation of China(No.50807041)
文摘This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the algorithm.In this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE39-bus system, and obtains the Pareto optimal solutions,from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method.
文摘According to the least square criterion of minimizing the misfit between modeled and observed data, this paper provides a preconditioned gradient method to invert the visco-acoustic velocity structure on the basis of using sparse matrix LU factorization technique to directly solve the visco-acoustic wave forward problem in space-frequency domain. Numerical results obtained in an inclusion model inversion and a layered homogeneous model inversion demonstrate that different scale media have their own frequency responses, and the strategy of using low-frequency inverted result as the starting model in the high-frequency inversion can greatly reduce the non-tmiqueness of their solutions. It can also be observed in the experiments that the fast convergence of the algorithm can be achieved by using diagonal elements of Hessian matrix as the preconditioned operator, which fully incorporates the advantage of quadratic convergence of Gauss-Newton method.
