نمایش همه 2 نتیجه
Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach
Modern power systemsرایگان!
Modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more significant today due to the increasing number of microgrids (MGs). The MGs mostly use renewable energies in electrical power production that are varying naturally. These changes and usual uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses a new online intelligent approach by using a combination of the fuzzy logic and the particle swarm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control synthesis is compared with the pure fuzzy PI and the Ziegler-Nichols PI control design methods .
Optimum Shape Design of Double-Layer Grids by Particle Swarm Optimization Using Neural Networks
In this paper, an efرایگان!
In this paper, an efficient method is proposed for optimum shape design of double-layer grids. In optimization process, the weight of structure is considered as objective function. The design variables are the number of spans divisions of grid in two directions, the height of between two layers and the cross sectional area of elements. The design constraints are considered as limitations of the stress and slenderness of elements and the displacement requirements of joints. The optimization is carried out by particle swarm algorithm that is suitable for discrete and continuous variables. To reduce the computational time of optimization process, the structural responses are predicted using properly trained radial basis function neural network. This network is a robust network for predicting the structural responses. The numerical results demonstrate the robustness and high performance of the suggested method for the optimum shape design of double-layer grids in.