Papers by Keyword: PSO Algorithm

Abstract: Large cylindrical shell is typically difficult to machine parts, and the cutting process of the bulky cylindrical shell is complicated. Through the actual requirements and conceptual design, the paper establishes the heavy-duty optimized database system for difficult-to-cut materials in order to improve its surface quality and production efficiency. The system is completed by combining Visual C++ 6.0 and SQL Server 2000.This paper mainly describes the implementation process and related interfaces of the data optimization and heavy inserts design. The data from optimization and tool design function block are validated its reliability by experiments and simulations, which proves that the system can be used for actual production.

Abstract: To realize the hybrid magnetic bearing (HMB) nonlinear decoupling control with high precision, a strategy of model reference adaptive control (MRAC) based on the least square support vector machine (LS-SVM) inverse is proposed. After analyzing the reversibility of HMB, the LS-SVM regression theory is used to identify the inverse model, the parameters of LS-SVM are optimized by Particle Swarm Optimization (PSO) algorithm. Then the nonlinear system is transformed into a pseudo-linear system by connecting the optimized the inverse model and the original unit. MRAC is designed to realize the compound linear control for HMB. Simulation results confirm that the identified inverse model has high precision and the compound control strategy has good performance.

Abstract: Taking the full network observability of power system and the least number of PMU as objective, to appearing fault situation in the grid, this paper proposes Differential Evolution and Particle Swarm Optimization (DEPSO) algorithm in view of the system failure rate. The improved DEPSO algorithm is global optimization, the algorithm takes the constraint condition of fault rate into account during the course of seeking optimal solutions. At the end, through the examples show that the algorithm compares with the existing optimization methods, which can reduce the number of PMU configuration and achieve completely observability of the system, at the same time, and stable operation of the system, through the simulation results verify feasibility and valibity of the algorithm.

Abstract: Distributed generators (DG) are much beneficial in reducing the losses effectively compared to other methods of loss reduction. It is expected to become more important in future generation. This paper deals with the multi DGs placement in radial distribution system to reduce the system power loss and improve the voltage profile by using the optimization technique of particle swarm optimization (PSO). The PSO provides a population-based search procedure in which individuals called particles change their positions with time. Initially, the algorithm randomly generates the particle positions representing the size and location of DG. The proposed PSO algorithm is used to determine optimal sizes and locations of multi-DGs. The objective function is the combination of real, reactive power loss and voltage profile with consideration of weights and impact indices with and without DG. Test results indicate that PSO method can obtain better results on loss reduction and voltage profile improvement than the simple heuristic search method on the IEEE33-bus and IEEE 90-bus radial distribution systems.

Abstract: Track irregularity is one of the most important aspects of the suspension control performance impact in Magnetic Transportation System (MTS). By using the traditional PID control, the problem was that it was difficult to confirm the PID parameters and have long settling time, even appear the chaotic phenomenon. However, using Expert PID(EPID) control method producesd over-fitting to initial assignments, local optimum induced easily and slow convergence rate problem. Based on the global optimization feature of PSO algorithm, it had been adopted to optimize the initial values of Expert PID control. An intelligent control algorithm for the maglev transportation system was put forward based on Expert PID optimized by Particle Swarm Optimization algorithm (EPID-PSO). The expert rule was that if the absolute value of the error trended to decrease, the PID current control keeped its maintenance; if not, then the PID current control applied strong functions. Under this rule, the dynamic error was reduced and the performance of track irregularity was improved. Simulation results by MATLAB proves that the control scheme has good robustness, shorter adjustment time, faster response time, achieving better quality of control under the three conditions of step signal, low frequence sine wave signal and high frequence square wave signal.

Abstract: This paper based on the idea of dynamic division of the load curve with the monotonicity of load curve and the PSO algorithm and gets the characteristics section. It fuses the sub-processes with the energy trajectory methods in order to describe the climbing peak and descending valley of the load curve closely, and characterizes the actual load curve. The multi-objective daily generation scheduling model is built based on the lowest cost of thermal power purchase of electricity and the lowest emissions of pollution gas. The optimal power flow based on combination of satisfaction degree and close degree are introduced to transform multi-objective into the single-objective optimization model. Decision makers can make interactive solution via adjusting goal satisfaction degree and close degree,so as to receive satisfactory results considering all aspects and make the daily generation scheduling based on energy-saving and environmental protection.

Abstract: The particle swarm optimization algorithm was improved in this paper, a novel self-adaption dynamic sub-swarms hybrid particle swarm optimization algorithm is proposed, in this algorithm, subgroup partition method based on dynamic clustering of particle adaptive value is adopted to divide particle group to different capability sub-group, then execute different optimize strategy to different subgroup, simultaneity, inertial weight and accelerating coefficient are adaptive set, through contacting adjustment of parameter with sub-group capability, the particle mode method and intersect and aberrance strategy of double deck are designed, The experimental results show that the algorithm has simple programming, good robust capability and strong optimizing capability which established the foundation of task planning of multi-UAV Cooperative.

Abstract: This paper proposes an effective way to optimization of the PI parameters of controllers in emergency power support. Transient simulation is used to evaluate the optimization objective function, an optimization algorithm based on the Particle Swarm Optimization method is used as the engine for producing new candidate points. The ITAE index is used to evaluate the dynamic response of AC system, the objective is to minimize the weighed sum of ITAE associated with different simulation states. The usefulness of the approach is demonstrated by a simulation of multiline HVDC-AC transmission in parallel in PSCAD.

Abstract: Three optimization models were proposed according to the problems emerged from self-propelled model test. And the object function to evaluated acoustic positionings precision of some given receivers arrays was created. The genetic algorithm code to solve this optimization problem was developed and validated by optimization model 1 afterwards. The optimum arrangements for model 1 and model 3 were acquired by calculating such code. The cross arrays was certificated to be the optimum ones. One new method to arrange acoustic receivers in self-propelled model test was drafted.

Abstract: A Rough-Fuzzy RBF Neural Network was raised based on PSO Algorithm. In this model,gives a knowledge acquisition method that based on rough set theory,the Rough-Fuzzy RBF neural network are constructed according to the results of the knowledge acquisition,the PSO are used to optimize the network parameters.This paper take number plate for example to conduct a simulation experiment.The results shows that the model can simplify the network training sample,optimize the network structure and enhance the systems study efficiency and the precision.