A matrix real-coded genetic algorithm to the unit commitment problem

An approach to solving the unit commitment (UC) problem is presented based on a matrix real-coded genetic algorithm (MRCGA) with new repairing mechanism and window mutation. The MRCGA chromosome consists of a real number matrix representing the generation schedule. Using the proposed coding, the MRCGA can solve the UC problem through genetic operations and avoid coping with a suboptimal economic dispatch (ED) problem. The new repairing mechanism guarantees that the generation schedule satisfies system and unit constraints. The window mutation improves the MRCGA searching performance. Numerical results show an improvement in the solution cost compared with the results obtained from other algorithms.     

On-line identification of continuous time-delay systems using the genetic algorithm

This paper proposes a new kind of on-line identification method of continuous time-delay systems from sampled input-output data. In order to track the time-varying system parameters as well as time-delay, the recursive linear least squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA) which has a high potential for global optimization. The time-delay is coded into binary bit strings and searched by the GA, while the system parameters are updated by the RLS method. Furthermore, this method (GALS method) is hybridized with the sequential nonlinear least squares method to improve the speed of convergence. Simulation results show that both the GALS and hybrid methods are efficient in the case that the system changes abruptly, and among them the hybrid method has superior convergence property to the GALs method and yields excellent estimation results in the case that the system changes with time continuously.     

遗传算法PID参数优化的改进

介绍了遗传算法的基本原理 ,并针对简单遗传算法在PID控制中存在的问题进行了分析 ,提出在不同情况下采用不同的变异概率的方法 ,并分别进行了实验仿真。仿真结果表明 ,用改进的遗传算法优化PID参数 ,可以提高优化性能 ,对控制系统具有良好的控制精度、动态性能和鲁棒性     

Tracking control of induction motor using fuzzy phase plane controller with improved genetic algorithm

This paper proposes a fuzzy phase plane controller (FPPC) using an improved genetic algorithm (IGA) for the optimal position/speed tracking control of an induction motor. The proposed optimal algorithm (IGA) is equipped with an improved evolutionary direction operator (IEDO) to enhance the traditional genetic algorithm (GA). An application example was considered to compare the proposed IGA with the GA. Computational results show that the proposed IGA is more efficient than the GA.Fuzzy membership functions, phase plane theory and the proposed IGA are employed to design the proposed controller (FPPC) for the optimal position/speed tracking control of an induction motor. The proposed FPPC has the merits of rapid response, simple designed fuzzy logic control and an explicitly designed phase plane theory. Simulated and experimental results reveal that the proposed FPPC is superior in the optimal position/speed tracking control to conventional PI controllers.     

基于遗传算法的模糊控制器规则优化

模糊控制器设计的关键问题就是模糊控制规则的选择。基于遗传算法的模糊控制规则表优化,是为模糊控制提供一种更加方便、有效的查表法。为了提高模糊控制器的性能,提出了基于遗传算法的模糊控制器规则表的优化方法,并进行了仿真实验。仿真结果表明,在模糊控制中采用遗传算法使控制系统最终达到所要求的控制效果,证明了该方法的可行性和有效性。     

遗传算法优化的RBF神经网络控制器

为了消除神经网络参数初值对控制器性能的影响,提出了一种改进遗传算法优化的RBF神经网络控制器.该方法设计了基于性能指标的适应度函数,自适应的交叉概率、变异概率,引入移民的遗传算法,保证了得到的控制器为最优参数控制器.该方法可用于非线性对象的控制器设计,仿真结果说明了该方法的有效性.     

遗传算法在模糊控制器参数寻优中的应用研究

介绍遗传算法的基本原理及实现步骤，并运用模糊控制器的参数寻优中，对复杂被控对象实现了最优控制，模糊控制规则采用基于解析表达式的模糊数模型，用遗传算法对模糊控制器中的量化因子和比例因子及偏差加权因子进行编码，确定适应度函数，在复制，交叉，变异的进化过程中获得全局最优点，改善模糊控制性能。在Matlab环境中实现遗传算法各算子的编程，并以二阶系统为例，对常规模糊控制器与经过遗传处寻优后的模糊控制器进行了仿真比较，其结果表明采用遗传算法寻优能获得较好的控制效果，该方法具有良好的动静态特性。     

基于遗传算法的小波神经网络控制器设计

针对小波神经网络常用的误差反传算法存在着易陷入局部极小点和对初值参数要求较高的缺点,结合遗传算法自适应全局优化搜索能力与小波神经网络良好的时频局部特性,提出了一种有效的学习训练途径.该方法首先应用遗传算法确定网络的初始参数,然后转入纯小波神经网络进行训练,大大加快了网络的收敛速度.网络训练时采用共轭梯度学习算法并对此算法进行了改进,有效的克服了梯度学习算法容易陷入局部极小的缺点.通过二阶倒立摆的控制仿真和实物控制,验证了算法的有效性.     

A synchronous generator fuzzy excitation controller optimallydesigned with a genetic algorithm

Design of a fuzzy logic power system controller with satisfactory performance is not an easy task. The difficulties come from two aspects. First, design of a fuzzy logic controller mainly uses the experience of the human experts. To acquire enough heuristic knowledge from the domain experts and to represent this kind of knowledge appropriately with a set of fuzzy rules present difficulties. Second, it is difficult to appropriately tune the parameters used in the fuzzy logic controller. These parameters are commonly determined by a “trial and error” method which is rather time consuming. In this paper, genetic algorithm is introduced to design an optimal fuzzy logic controller. The proposed method has been used to design an optimal fuzzy logic excitation controller for a generating unit. Test results with the fuzzy logic controller show very satisfactory results     

Independent signal separation using genetic algorithm

Growing multimedia systems require more efficient signal separation methods to preserve quality of voice or music recording in a noisy environment. Some signal separation methods are based on minimizing the dependence measure among input signals to separate the noise component since the noise component is usually independent of the other signals. Under such circumstances, we have developed a new method to separate independent signal components which directly minimizes the Kullback–Leibler divergence by a genetic algorithm. In this paper, we have improved the method in its separation performance and processing speed. The simulation results show that the proposed method is effective in separating the independent signals. © 2000 Scripta Technica, Electr Eng Jpn, 131(4): 52–57, 2000     

采用共生进化算法的模糊控制器参数寻优方法

为解决模糊控制器自动优化设计中对结构和参数的学习和寻优搜索缓慢，以及模糊规则基维数爆炸问题，采用共生进化遗传算法结合分级模糊建模的思想相结合，进行模糊控制器的自动设计。提出了模糊规则分类形成子种群、多种群并行进化的遗传算法。此方法可以极大的减小编码规模，提高参数寻优的搜索速度。仿真结果表明，该方法寻优搜索速度快，设计出的控制器控制效果好。     

UPFC控制器IP设计

针对统一潮流控制器(UPFC)的物理模型控制系统实现时要求频率跟踪电网、幅值和相位连续可调的关键技术问题,采用现场可编程门阵列(FPGA)的设计方法,给出了UPFC物理模型控制系统的FPGA解决方案.阐述了其软硬件设计思想和方法,利用Altera公司的EDA开发工具Quartus II,采用开关损耗最小脉宽调制(PWM)方法设计了一个基于Avalon总线接口的统一潮流控制器的IP核,给出了UPFC IP核的功能及其结构,对调制波幅值和相位设置、调制波寻址、数据查找及幅值计算等子模块进行了详细描述,并利用Simulator和嵌入式逻辑分析仪SignalTap II进行了仿真验证.UPFC的FPGA解决方案使其整体性能大幅度提高.     

Unit commitment solution methodology using genetic algorithm

Solution methodology of unit commitment (UC) using genetic algorithms (GA) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem specific operators are proposed for the satisfaction of time dependent constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented     

Project scheduling using a genetic algorithm with adaptable changing genetic operators

Genetic algorithms (GA) have been widely used to solve planning problems. However, they require one to determine the optimal values of many genetic parameters, such as population sizes, crossover probability, mutation probability, and so on. To make matters worse, the most suitable combination of parameters for one problem is not always optimal for others. Therefore, these parameters should be tuned whenever the problem changes. In this paper, we propose an adaptable GA mechanism that has autonomic parameter tuning for the composition of generic operators. This mechanism raises questions concerning the probability of genetic operators that acted effectively, that is, the probability that one operator created better individuals than the other operators. It also successively adjusts the combinations of genetic parameters suitable for the target problem. We applied the adaptable GA mechanism to a project scheduling model (PSM) and evaluated it with manual tuning methods. © 1998 Scripta Technica, Electr Eng Jpn, 124(2): 36–42, 1998     

基于遗传算法的抗几何攻击图像水印方案

现有的大部分鲁棒图像水印算法很难抵抗几何攻击,其原因是同步信息被破坏。因此只要在提取水印前使同步信息得到恢复,水印即可成功提取。本文采用仿射不变特征点匹配方法估计图像所经历的几何变换参数,其中特征点匹配通过遗传算法实现。水印嵌入过程是自适应的,且实现了盲提取。实验表明,本文提出的方法对多数攻击,尤其对几何攻击有很好的鲁棒性。     

电力系统无功优化的二次变异遗传算法

在自适应遗传算法的基础上引入优良个体池和二次变异操作,提出了用于电力系统无功优化和电压控制的二次变异遗传算法。该方法建立一个与群体规模等大的优良个体池,用于保存个体编码、适应度等详细数据。每计算完一代,将该代的个体与优良个体池中的个体进行生存竞争,因此优良个体池中保留了历代计算的优良个体,下一代的群体从优良个体池中选择。考虑到遗传操作后存在大量相同个体,检出重复个体进行二次变异,产生邻近的个体,避免了重复计算而且增强了算法的局部搜索能力,加快了算法的收敛速度。该方法和自适应遗传方法用IEEE30节点系统为例计算,结果表明:使用二次变异自适应遗传算法优化的网损平均值更低,寻优性能更好,优化的网损值集中在小的区间。     

Dynamic transmission planning using a constrained genetic algorithm

A new, accurate and efficient model for long range transmission planning is developed in this paper. The model applies a constrained genetic algorithm where the system stability constraints, in terms of upper and lower limits on the system bus voltage magnitudes and swing angles, are easily included. The accurate ac load flow equations are also included in the algorithm. An accurate cost function for the transmission system is formulated where both fixed and variable costs for all planned facilities are included, in addition to the cost of energy losses. The cost function is then minimized, subject to system constraints, using a constrained genetic algorithm. The model is capable of handling both static mode of planning, as well as the dynamic mode of planning accurately and not as a series of statically build-up plans. The IEEE 6-bus test system is used to test and justify the applicability of the new developed model.     

Magnetometer calibration improvement using wavelet and genetic algorithm

Three‐axis magnetometer error and measurement noise influence the accuracy of magnetic measurements. Genetic algorithm (GA) is proposed to calibrate the magnetometer error, and wavelet is proposed for noise cancellation. The noise of a Mag3300 magnetometer and a DM magnetometer were tested within a horizontal barrel shield equipment. Five kinds of wavelet analysis and two kinds of wavelet package were used for noise cancellation, and the performance of different wavelets was compared. Noise of the Mag3300 magnetometer and DM magnetometer were suppressed from 29.6 and 2.3466 to 3.7 and 1.0789 nT, respectively. The scalar error of the Mag3300 magnetometer was tested using a two‐dimensional nonmagnetic rotation equipment and a GSM‐19T proton magnetometer. Scalar calibration performances of the unscented Kalman filter (UKF), the two‐step algorithm, and GA were compared. Experimental results show that GA provides less error intensity (about 370 and 70 nT) than UKF and the two‐step algorithm. In addition, the influence of wavelet on scalar calibration using UKF, the two‐step algorithm, and GA was analyzed. Results show that wavelet improved the scalar calibration performance. Mean error of the Mag3300 magnetometer scalar was 795.5 nT. When combined with the wavelet package, the error was suppressed to −22.3, 3.3, and −0.4 nT using UKF, the two‐step algorithm, and GA, respectively. The results suggest an effective way for magnetometer calibration using GA and wavelet. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

采用GA提高模糊控制器适应性的方法及应用研究

对于工业控制中广泛存在的多容量、大惯性、大延迟的被控对象,模糊控制是一种较有效的方法,模糊控制中隶属函数的正确选择是模糊控制器设计的关键,针对传统的获取隶属度函数方法的不足,本文设计了一种基于遗传算法的模糊控制器,系统采用遗传算法优化模糊控制器的隶属函数及其量化因子和比例因子的初值.仿真结果表明,采用该算法设计的模糊控制器较传统的PID控制器与模糊控制器具有鲁棒性强、超调量小的特点.这种改进型的模糊控制器具有良好的控制性能,体现了遗传算法在参数寻优方面的优越性.