Optimal Design of Fuzzy-AGC Based on PSO&RCGA to Improve Dynamic Stability of Interconnected Multi-area Power Systems

Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this regard,AGC system based on fuzzy logic,i.e.,so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system.Apart from that,simultaneous coordination scheme based on particle swarm optimization(PSO)along with real coded genetic algorithm(RCGA)is suggested to coordinate FLAGCs of the all areas.To clarify the high efficiency of aforementioned strategy,two different interconnected multi-area power systems,i.e.,three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies.The potency of this strategy has been thoroughly dealt with by considering the step load perturbation(SLP)in both the under study power systems.To sum up,the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC(CAGC)in each power system in order to damp out the power system oscillations.

基于实数编码遗传算法的超二次模型参数提取计算

如何从真实景物形状信息中计算出对其几何形状描述的超二次基元模型的参数,是从二维图像或三维数据中进行超二次模型重建的关键,这个过程称为超二次模型的参数提取,也称为超二次拟合计算.由于传统的搜索算法很容易陷入局部最优解,因此,本文将实数编码遗传算法应用于超二次模型的参数提取中,针对不同的遗传算法控制策略、遗传算子等进行了大量的拟合计算数值比较实验.数值实验表明,应用实数编码遗传算法进行超二次模型参数提取计算具有效率高、鲁棒性好、精度高的优点.

基于边缘计算的5G云网络架构计算卸载策略研究

针对传统云计算架构无法满足工业物联网对低业务时延的要求,本研究引入MEC技术,提出一种基于工业物联网的分布式云边缘计算(CE-IIo T)网络架构,以实现分布式处理工业物联网业务。然后,建立了CE-IIo T中业务响应时延模型,提出基于RCGA-CO算法的计算任务卸载策略,以实现降低工业物联网业务响应时延。最后,通过仿真实验证明了,相较于Greedy LB算法,本研究提出的基于RCGA-CO算法计算任务卸载策略的业务平均响应时延最低,且在故障情况下,该策略可有效保证业务处理的可靠性。

Hybrid Neuro Fuzzy Controller for Automatic Generation Control of Multi Area Deregulated Power System

This paper is intended in investigating the Automatic Generation Control (AGC) problem of a deregulated power system using Adaptive Neuro Fuzzy controller. Here, three area control structure of Hydro-Thermal generation has been considered for different contracted scenarios under diverse operating conditions with non-linearities such as Generation Rate Constraint (GRC) and Backlash. In each control area, the effects of the feasible contracts are treated as a set of new input signals in a modified traditional dynamical model. The key benefit of this strategy is its high insensitivity to large load changes and disturbances in the presence of plant parameter discrepancy and system nonlinearities. This newly developed scheme leads to a flexible controller with a simple structure that is easy to realize and consequently it can be constructive for the real world power system. The results of the proposed controller are evaluated with the Hybrid Particle Swarm Optimisation (HCPSO), Real Coded Genetic Algorithm (RCGA) and Artificial Neural Network (ANN) controllers to illustrate its robustness.

Bandwidth optimization of a Planar Inverted-F Antenna using binary and real coded genetic algorithms

With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide larger bandwidth and at the same time small dimensions. Although the gain in bandwidth performances of an antenna are directly related to its dimensions in relation to the wavelength, the aim is to keep the overall size of the antenna constant and from there, find the geometry and structure that give the best performance. The design and bandwidth optimization of a Planar Inverted-F Antenna (PIFA) were introduced in order to achieve a larger bandwidth in the 2 GHz band, using two optimization techniques based upon genetic algorithms (GA), namely the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). During the optimization process, the different PIFA models were evaluated using the finite-difference time domain (FDTD) method-a technique belonging to the general class of differential time domain numerical modeling methods.

基于实数编码遗传算法的桥梁有限元模型修正方法

为克服传统桥梁有限元模型修正迭代优化过程中存在的局部收敛和提高模型修正精度,提出了联合实数编码遗传算法与静动力实测数据的有限元模型修正方法;引入四边形等参元理论和牛顿迭代法编制宏命令,实现有限元模型中车辆荷载的快速自动加载;基于结构有限元模型静动力特性构造目标函数,以实数编码遗传算法为优化策略,采用MATLAB平台建立了有限元模型修正框架;通过对一个简支框架结构的数值模拟,对比了所提出优化方法与其他方法的收敛效率和修正结果,以验证所提出方法的有效性;采用拉丁超立方体抽样分析了有限元模型参数变化对桥梁动力响应的影响,以确定待修正参数,并采用所提方法修正了一座改建的空心板桥梁的实体有限元模型。分析结果表明:零阶算法和一阶算法对参数的敏感性和修正范围依赖大,选用敏感性较小的参数或者参数修正范围大于50%将会导致错误的修正结果;实数编码遗传算法对初始输入不敏感,可避免局部收敛的情况;采用灵敏度分析得到的主要待修正参数有空心板弹性模量、现浇层弹性模量以及支座横桥向和顺桥向的约束刚度;修正后的空心板弹性模量增幅约为19.13%,现浇层弹性模量增幅约为16.00%,横向约束刚度增幅约为46.21%,纵向约束刚度增幅约为72.72%,修正后的有限元模型的静动力特性与实测响应吻合良好,各测点静力响应误差均小于4%,动力响应误差小于3%。