An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSN...An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimation accuracy is improved by collaboration and measurement information fusion of the tasking nodes. The balanced distribution model of energy in WSNs is constructed to prolong the lifetime of the whole network. In addition, the communication energy and computation resource are saved by adaptively changed sampling intervals, and the real-time performance is satisfactory. The simulation results show that the estimation accuracy of the proposed scheme is improved compared with the nearest sensor scheduling scheme (NSSS) and adaptive sensor scheduling scheme (ASSS). Under satisfactory estimation accuracy, it has better performance in saving energy and energy balance than the dynamic collaborative scheduling scheme (DCSS).展开更多
Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and s...Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.展开更多
基金supported by the NSFC-Guangdong Joint Foundation Key Project (No. U0735003)the Oversea Cooperation Foundation (No.60828006)+2 种基金the Fundamental Research Funds for the Central Universities (No. 2009ZM0076)the Specialized Research Funds for the Doctoral Program of Higher Education of China (No. 20100172120028)the Scientific Research Funds for the Returned Overseas Chinese Scholars, State Education Ministry
文摘An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimation accuracy is improved by collaboration and measurement information fusion of the tasking nodes. The balanced distribution model of energy in WSNs is constructed to prolong the lifetime of the whole network. In addition, the communication energy and computation resource are saved by adaptively changed sampling intervals, and the real-time performance is satisfactory. The simulation results show that the estimation accuracy of the proposed scheme is improved compared with the nearest sensor scheduling scheme (NSSS) and adaptive sensor scheduling scheme (ASSS). Under satisfactory estimation accuracy, it has better performance in saving energy and energy balance than the dynamic collaborative scheduling scheme (DCSS).
基金supported by the National Natural Science Foundation of China(Nos.51767022 and 51575469)
文摘Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.
