面向电力市场的梯级水电站联合优化调度研究

分析了电力市场环境下梯级水电站优化调度的特点，提出了面向电力市场的梯级水电站联合优化调度模型；对遗传算法进行了改进，提出了基于改进遗传算法模型求解的方法。工程实例证明，该方法科学可行，对电力市场环境下梯级水电站运行调度有较高的参考价值。     

基于改进遗传算法的表贴式永磁同步电机优化设计

针对有约束条件的电机优化问题,建立了带惩罚项的优化目标函数,在简单遗传算法基础上采取保留最佳个体策略,结合模式搜索法形成改进遗传算法,基于磁路法分析了表贴式永磁同步电机在id=0控制方式下重要性能指标的计算方法,并分别采用简单遗传算法和改进遗传算法对一台分数槽集中绕组表贴式永磁同步电机进行了效率优化。优化结果证明改进遗传算法更易得到更高的目标函数值,且优化初期优势明显,收敛更快,优化效率更高,证明了改进遗传算法在优化电机目标函数时的有效性。     

改进遗传算法在无功优化中的应用

本文提出了一种应用于电力系统无功优化问题的改进遗传算法。该算法在一般遗传算法的基础上，对编码方式、遗传算子以及终止判据等方面作了改进。通过对IEEE14节点系统的计算分析表明，该算法优于一般遗传算法。     

遗传算法在电力系统无功优化中的应用综述

遗传算法是一种通过在整个解空间多渠道同时搜索以找到全局最优解的寻优方法，已经在许多复杂优化问题上被证明是一种相当有效的方法。为此，就遗传算法在电力系统无功优化中的应用进行了介绍，并提出了遗传算法在大规模电力系统无功优化计算中的改进措施。     

基于内点法和遗传算法无功优化的比较及应用

无功优化是电力系统实现电压和无功功率最优控制和调度的基础，使用者对无功优化程序功能的需求不同，采用的无功优化算法也不尽相同，内点法和遗传算法是其中较具代表性且差异较大的2种，对基于内点法无功优化的数学模型，遗传算法的适应度函数和收敛判据进行改进，在IEEE6节点系统进行校验，并分析其在湖北恩施电网无功优化控制方面的应用，结果表明，内点法无功优化的结果比改进遗传算法无功优化的结果差，且处理离散变量不方便，但计算速度快于改进遗传算法，随问题规模增大，内点法的这一优势更明显。     

一种基于实时遗传算法的神经元控制

遗传算法是一种并行、全局优化的有效方法。由于遗传算法的进化过程缓慢，通常使用于离线，很难用于在线的实时优化和控制。该文对遗传算法进行了改进，提出了一种实时遗传算法在线自适应调整神经元增益的控制方法，并对造纸过程的定量水分控制进行了仿真实验，结果表明了该方法的有效性。     

基于改进遗传算法的配电网无功优化

针对配电网的特征,建立了综合考虑全年网损、电压品质和补偿设备投资的无功优化数学模型。对传统遗传算法的遗传算子和终止判据等方面进行了改进,提出了一种配电网无功优化的改进遗传算法,使其计算效率和全局寻优能力均有提高。采用本文提出的改进遗传算法,通过实例计算,结果表明其优化效果优于传统遗传算法。     

基于遗传算法的配电网无功优化探讨

首先介绍了无功优化的定义及在配电网中的作用,然后介绍了经过改进的遗传算法在配电网无功补偿优化方面的应用,并且与传统遗传算法相比,改进的遗传算法解决了传统遗传算法过早收敛的问题,同时存在局部最优、计算复杂的缺点。     

遗传算法在水电站优化调度中的实用研究

根据水电站优化调度问题的实际特点，用基于十进制的遗传算法，加入最优保存和局部搜索两种收敛策略对问题进行了改进。并用五强溪电站的实际例子进行了模拟计算，与未经改进的遗传算法进行了比较，取得了比较满意的结果。     

基于改进遗传算法的无源滤波器设计

在配电网中装设无源滤波器抑制谐波和进行基波无功补偿的设计中，保证基波无功补偿容量的前提下，最关键的是对滤波方案中的LC元件参数的优化，LC元件参数的优化是一个多约束、非线性的规划问题，应用改进遗传算法处理滤波器LC参数优化的规划问题，不但可以找到全局最优解，而且明显提高了算法的寻优速度，最后通过对具体工程算例的计算，表明了应用遗传算法设计滤波方案的优越性和对遗传算法改进的有效性。     

遗传算法及其改进

本文首先对遗传算法的来源,基本原理,数学机理,特点及其应用进行了论述。这了提高遗传算法的收敛性能,同时考虑到交叉率和变异率的选取问题,本文简要介绍了一种基于个体适应度值的自适应调整交叉率和变异率的自适应遗传算法。     

Unit Commitment solution using an optimized genetic system

This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process and therefore greater economy. The proposed methodology resulted in a better performance with faster operation by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start-up and shut-down ramps, have also been considered. A number of important parameters (standard and new parameters) of the UC problem have been identified. The proposed method is implemented and tested using a C# program. The tests are carried out using two systems including 10 and 20 units during a scheduling period of 24 h. The results are finally compared with those obtained from genetic schemes in other similar investigations through which the effectiveness of the proposed scheme is affirmed.     

Robust fuzzy logic power system stabilizer based on evolution and learning

A robust fuzzy logic power system stabilizer (FLPSS) based on evolution and learning is proposed in this paper. A hybrid algorithm that combines learning and evolution is developed whereby each one complements other’s strength. Parameters of FLPSS are encoded in chromosome (individual) of genetic algorithm (GA) population. Population of FLPSS in GA learns to stabilize electromechanical oscillations in power system at an operating point, as the best fitness becomes large steady value during successive generations. Operating region of FLPSS is enlarged by learning more operating points over the operating domain. Best FLPSS drawn from last generation is saved as designed FLPSS. Effectiveness of the proposed method is validated on a single machine infinite bus (SMIB) power system. Promising optimal stabilizing performance with designed FLPSS for considered power system is obtained at wide range of operating points.     

BFOA based design of PID controller for two area Load Frequency Control with nonlinearities

This paper presents an application of the novel artificial intelligent search technique to find the parameters optimization of nonlinear Load Frequency Controller (LFC) considering Proportional Integral Derivative controller (PID) for a power system. A two area non reheat thermal system is considered to be equipped with PID controller. Bacterial Foraging Optimization Algorithm (BFOA) is employed to search for optimal controller parameters to minimize the time domain objective function. The performance of the proposed technique has been evaluated with the performance of the conventional Ziegler Nichols (ZN) and Genetic Algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PID controller. By comparison with the conventional technique and GA, the effectiveness of the proposed BFOA is validated over different operating conditions, and system parameters variations.     

一种改进的变种群规模遗传算法

针对标准遗传算法存在早收敛和进化后期搜索效率低下等缺点,在变种群规模遗传算法（GAVaPS）基础上,提出了一种改进的变种群规模遗传算法。一方面,修改了种群中个体“寿命”的计算方法,用个体寿命限制个体的生存期,实现对种群数的调控;另一方面,通过改进遗传算子,解决了遗传算子的无向性问题和早收敛现象,同时提高了算法的搜索效率。实验数据表明,该算法具有比标准遗传算法更好的性能。     

基于改进遗传算法的配电网无功优化

遗传算法是近年被广泛应用于配电网无功优化的一种新型的优化算法，本文在基本遗传算法（SGA）的基础上，提出基于定制的初始种群的形成方法以保证个体的多样性，提出反映个体分布疏密情况的个体分布散度，设计出随个体分布散度成反比，随最优个体相对保留代数成指数上升的自适应变异率，为配电网无功优化提供了一种新的算法，算例表明本文提出的算法优化效果好，在精度上以及收敛速度上都具有较大的提高。     

Reliability design of distribution systems using modified genetic algorithms

This investigation presents an evolutionary algorithm to reliably design a distribution system. Adopted reliability indices include failure rate and interruption duration, which are both commonly used in distribution systems. The total cost including the apparatus investment cost and the system interruption cost, is the objective function to be minimized. Reliability constraints on a load point are addressed to ensure the adequacy of the power supply of the load point. The evolutionary optimization algorithm is a modified genetic algorithm (GA), which uses binary and floating-point representations. The proposed GA is compared with the conventional generalized reduced gradient (GRG) method. A secondary substation of the Taiwan Power Company is used in an example of the method's application. The discussed method is simple and effective and is useful for expanding the existing systems and constructing new systems.     

Optimum distribution system harmonic filter design using a genetic algorithm

This paper presents a technique for the anticipation of harmonic distortion as well as selection and placement of filters on distribution systems which have multiple harmonic sources and constantly changing characteristics caused by compensating capacitor switching and load impedance changes. The purpose is to predict and reduce harmonic voltage distortion for all buses in the system to comply with IEEE Standard 519. A genetic algorithm is used in this technique and shows itself an effective tool in optimizing a multivariable objective function.     

DG integrated multistage distribution system expansion planning

In this paper, a framework is presented to solve the problem of multistage distribution system expansion planning in which installation and/or reinforcement of substations, feeders and distributed generation units are taken into consideration as possible solutions for system capacity expansion. The proposed formulation considers investment, operation, and outage costs of the system. The expansion methodology is based on pseudo-dynamic procedure. A combined genetic algorithm (GA) and optimal power flow (OPF) is developed as an optimization tool to solve the problem. The performance of the proposed approach is assessed and illustrated by numerical studies on a typical distribution system.     

A harmony-genetic based heuristic approach toward economic dispatching combined heat and power

The Combined Heat and Power Economic Dispatch (CHPED) problem seeks to determine the heat and power production to minimize the system production costs and satisfy the heat–power demands and capacity constraints. This study examines the combined heat and power dispatching needs of cogeneration plants, and investigates the performance of an evolutionary computing approach which is based on both genetic algorithm (GA) and harmony search (HS). Experimental results were conducted for an extensive comparison with GA and HS to confirm the superior performance of this hybrid approach in cost minimization and computation times. The output results indicate that the proposed algorithm is capable of managing the CHPED problem and yields high-quality solutions.