Duan Tao

Bio: Duan Tao is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: AC power & Swarm intelligence. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

Power System Multi-Objective Reactive Power Optimization Based on Adaptive Focusing Particle Swarm Optimization Algorithm

Abstract: Based on the improvement of balance performance of particle swarm optimization in global and local searching, an adaptive focusing particle swarm optimization(AFPSO) is proposed which is an adaptive swarm intelligence optimization algorithm possessing better global searching ability and faster searching speed.In this paper the proposed AFPSO algorithm is applied to power system reactive power optimization.Taking optimal control principle as its foundation and led in the index of static voltage stability, a multi-objective reactive power optimization model in which the minimum active network loss and maximum static voltage stability margin are considered comprehensively is built.Simulation results of IEEE 30-bus system show that AFPSO algorithm can enhance power system voltage stability, meanwhile economic operation of power system is also implemented, thus the effectiveness and superiority of AFPSO algorithm are verified.

Application of Parallel Adaptive Particle Swarm Optimization Algorithm in Reactive Power Optimization of Power System

Abstract: There are defects in traditional particle swarm optimization (PSO) algorithm, i.e., its prematurity and slow convergence speed in the late evolutionary phase. For this reason, a method for power system reactive power optimization based on a parallel adaptive PSO (PAPSO) algorithm is proposed. Firstly, the initial population is divided into N subpopulations stochastically; then the search in each subpopulation is performed individually by the proposed method, thus the parallel calculation of the adaptive PSO algorithm is implemented. To avoid the search in subpopulations falls into local optimal solution, the two-value crossover operator is led in to exchange the information among subpopulations and update the position of related particles, thus the global search ability of the algorithm is ensured and the diversity of population can be kept. During the search process in each subpopulation, current search direction is adaptively updated in accordance with egotistic direction, altruistic direction and pro-activeness direction to improve the convergence speed of the algorithm. Simulation results of IEEE 30-bus system show that as for reactive power optimization the proposed algorithm is feasible and effective.

Reactive Power Optimization in Regional Grids by Improved PSO Algorithm

Abstract: The optimal control of reactive power and voltage is one of the essential issues in power system's secure and economical operation. After analyzing the model of reactive power optimization in regional power grids and the mechanism of particle swarm algorithm, this paper has proposed an improved discrete PSO algorithm with dynamic parameters and a diversion strategy to solve ORP problem in regional power networks. Compared with several other algorithms, the simulation results of tests on IEEE 30 bus system and an actual regional network indicates that the improved algorithm is more efficient and attains better voltage profile and less active power loss.

Inductive reactive power compensation optimization for 10kV distribution network with distributed generations

Abstract: Massive distributed generations (DGs), e.g., the small hydropower stations, accessing to the regional power network would result in the shortage of inductive reactive power, which in turn would cause the high voltage issue. An inductive reactive power optimization configuration method towards the 10kV distribution network with DGs is proposed. The methodology was conducted with the minimum of the power loss and the minimum of the voltage deviation as its objectives, and the two objectives were computed by utilizing the multi-objective particle swarm algorithm to obtain the Pareto optimal solutions. Then the fuzzy decision-making approaches are used to get the compromise solution so as to determine the reactive power compensation capacity. An actual regional network is simulated through the Matlab software, and the compared simulation results certify that the proposed method not only has a good voltage regulation effect, but also has the economy superiority in terms of reducing the total amount of the inductive reactive power compensation.

Reactive Power Optimization in Power System Based on Adaptive Particle Swarm Optimization

Abstract: This paper summarizes the reactive power optimization of power system characteristics and requirements, proposed to target the active power loss of reactive power optimization mathematical model, And the traditional classical algorithm can not handle the limitations of discrete variables, using the adaptive particle swarm optimization algorithm to solve the problem of reactive power optimization. By testing on IEEE30 bus system simulation, comparing different algorithm optimization results show the effectiveness and superiority of APSO algorithm.