Papers by Keyword: PSO Algorithm

Abstract: The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

Abstract: The integration of distributed generator (DG) will bring a series of influence in distribution power system. With the trend of a large number of DG penetration, researches on the maximum integration capacity of DG become a hot research topic. A multi-objective optimization model with objective functions of the maximum DG output, network losses and voltage stability index are formulated. Particle swarm optimization (PSO) algorithm is adopted with variable inertia weight to calculate the maximum DG output. IEEE 33-bus test system with DG units is taken as one example to compute the maximum integration capacity. The results show that the proposed method is reasonable and effective.

Abstract: In this paper, we focuse on the study of parameter optimization problem of the uniform scalar dead zone quantizer (USDZQ).We used particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm for the optimization. We used the two kinds of optimization algorithm for ECG signal coding test.Those ECG signals were from the MIT-BIH arrhythmia database. At the end of this paper, the results of compression are compared.

Abstract: To accurately depict the dynamic characteristics for aircraft stall by aerodynamic model, a Wavelet Neural Network (WNN) stall aerodynamic modeling method based on Particle Swarm Optimization (PSO) algorithm and Artificial Fish Swarm (AFS) algorithm is proposed. Numerical examples show that the proposed method has a good prediction precision, and it is also effective and feasible to build the aerodynamic model from flight data for aircraft stall.

Abstract: The extraction of solar energy will be higher using the multijunction solar cells instead of single junction solar cell by splitting the solar spectrum. This work proposes a detailed study to identify the optimum interconnection method for various multijunction solar cells. An effective power electronic circuit could substantially enhance the efficiency and utilization of a photovoltaic (PV) power system constructed from multijunction solar cells. The multiple input dc-dc non-inverting buck-boost converter is used to demonstrate the advantage of the proposed interconnection technique, which can maintain a constant output voltage by performing both the buck and boost mode of operation. In order to ensure maximum power point (MPP) operation, a particle swarm optimization (PSO) algorithm is applied which needs only one MPP control for multiple solar modules resulting reduction in cost and complexity. The PSO algorithm has the ability to track the global maxima of the system even under complex illumination situations.

Abstract: In this paper, particle swarm optimization (PSO) algorithm is presented to optimize the five-effect evaporator system in a cane sugar production plants and realize the minimum loss of energy.Generally, the energy optimization of five-effect evaporator system is a so complex that can not be found a effective algrithm to resolve. The energy optimization of five-effect evaporator system requires considering various parameter criteria so as to meet the requirements of the production process. Hence, a energy optimization model of five-effect evaporator system is proposed. We tested the presented model and PSO algorithm at a cane sugar production plant in Guangxi province so as to support the application of industry.The results of the empirical study show that the energy optimization of five-effect evaporation in cane sugar production plants based on a PSO algorithm is feasible and effective.

Abstract: Tower crane hoisting system is the most important structural component of tower crane components. Its structural performance directly affects the performance of the overall tower crane. Combining the characteristics of The unconventional dedicate tower crane, established the mathematical model of hoisting system, and complied the particle swarm optimization algorithm program based on improved particle swarm optimization algorithm by MATLAB language, optimized analysis of hoisting system of The unconventional dedicate tower crane, and verified the optimization results by static analysis in the end. The result shows that the optimized analysis of tower crane hoisting system is viable and efficient by the improved particle swarm optimization algorithm.

Abstract: In order to improve the mathematical models accuracy of the electro-hydraulic servo loading system from the high-speed motorized spindle reliability test bench. This paper establishes the mathematical model based on the dynamic characteristics of the test bench, then establishes discrete mathematical model of the system based on the Z-transform, and finally uses particle swarm optimization (PSO) algorithm to identify the parameters of the discrete model. Additionally, the least square method is applied to identify the parameters of the model for measuring the PSO algorithm parameter identification capability in our paper. The experimental results show that the mathematical model, identified by the PSO algorithm, can simulate the loading process very well under the strong interference signals, and the result is better than that gotten by the least square method, which proves that the PSO algorithm has high identification accuracy and better capability in parameters identification .

Abstract: Resource schedule Strategy is the core technology of cloud computing. PSO algorithm is one of dynamic adaptation resource scheduling algorithms to cloud computing. The virtual machines and the hosts can be scheduled reasonable by adjusting parameters. The resource can be scheduled quickly because of the dynamic trend calculation of PSO algorithm, to ensure real-time of the Cloud Calculation.

Abstract: We consider an output based adaptive iterative learning control (AILC) for robotic systems with repetitive tasks in this paper. Since the joint velocities are not measurable, a sliding window of measurements and an averaging filter approach are used to design the AILC. Besides, the particle swarm optimization (PSO) is used to adjust the learning gains in the learning process to improve the learning performance. Finally, a Lyapunov like analysis is applied to show that the norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.