ACO based speed control of SRM fed by photovoltaic system

This paper proposes a speed control of Switched Reluctance Motor (SRM) supplied by Photovoltaic (PV) system. The proposed design of the speed controller is formulated as an optimization problem. Ant Colony Optimization (ACO) algorithm is employed to search for the optimal Proportional Integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of Genetic Algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature, and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.     

Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.     

Multi-objective optimization of a plain fin-and-tube heat exchanger using genetic algorithm

In the present paper, a plate fin-and-tube heat exchanger (PFTHE) is considered for optimization with air and water as working fluid, four geometric variables are taken as parameters for optimization, a Genetic Algorithm (GA) was used to search for the optimal structure sizes of the PFTHE, the maximum total heat transfer rate and the minimum total pressure drop are taken as objective functions in GA, respectively. Performance of the optimized result was evaluated and correspondingly the total heat transfer rate, the total pressure drop, the heat transfer coefficient and the local Nusselt number, j-factor and friction factor ξ are calculated respectively. Results show that the total heat transfer rate of the optimized heat exchanger increased by about 2.1–9.2% comparing with the original one, the heat transfer coefficient increased by about 8.2–14.7% and the total pressure drop decreased by about 4.4–8% in the range of Re = 1200–14000.     

Hardware‐oriented ant colony optimization and its application to dynamic route guidance problem

Ant colony optimization (ACO) is based on the behavior of food gathering of ants and it is a powerful search tool particularly when applying it to combinatorial optimization problems. However, ACO has the inherent problem of substantial processing time, because it requires a lot of repetitive calculations. In this article, we propose novel hardware‐oriented ACO (H‐ACO) to reduce processing time. H‐ACO adopts new integer arithmetic instead of conventional floating point arithmetic in the optimization process and it achieves high‐speed processing while keeping the quality of solutions. In addition, H‐ACO reduces the hardware resources, accelerates the clock frequency, and improves the latency, if it is implemented as a dedicated hardware. Furthermore, we also propose a new route guidance algorithm which combines H‐ACO with Dijkstra's algorithm and it can be applied to a dynamic route guidance problem. Experiments using actual map data demonstrate the validity of the proposed route guidance algorithm. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Soft computing‐based approach for optimal design of on‐chip comparator and folded‐cascode op‐amp using colliding bodies optimization

This paper presents an efficient approach for the optimal designs of two analog circuits, namely complementary metal oxide semiconductor) two‐stage comparator with p‐channel metal oxide semiconductor input driver and n‐channel input and folded‐cascode operational amplifier using a recently proposed meta‐heuristic‐based optimization algorithm named as colliding bodies optimization (CBO). It is a multi‐agent algorithm that does not depend upon any internal control parameter, making the algorithm extremely simple. The main objective of this paper is to optimize the metal oxide semiconductor (MOS) transistors' sizes using CBO in order to reduce the areas occupied by the circuits and to get better performance parameters of the circuits. Simulation Program with Integrated Circuit Emphasis simulation has been carried out by using the optimal values of MOS transistors' sizes and other design parameters to validate that CBO‐based design is satisfying the desired specifications. Simulation results demonstrate that the design specifications are closely met and the required functionalities are achieved. The simulation results also confirm that the CBO‐based approach is superior to the other algorithms in terms of MOS area and performance parameters like gain, power dissipation, etc., for the examples considered. Copyright © 2016 John Wiley & Sons, Ltd.     

基于粒子群算法的故障测试集优化

为加速测试进程和减少测试开销,数字集成电路在生成测试矢量后必须进行故障测试集的优化。文中利用粒子群优化算法生成最小完备测试集,根据故障测试集优化问题的具体特点,构造粒子的表达方式和编码规则,建立粒子群的速度一位置模型;同时为提高优化效率,引入混沌优化算法来初始化粒子群。实验结果表明,在测试生成后,该方法能在较短的时间内生成最小完备测试集,验证了它的实用性和有效性。     

Optimal placement of switches in a radial distribution network for reliability improvement

This paper proposes differential search (DS) algorithm to solve distribution system reliability optimization problem. Optimum number and location of remote control switch (RCS) have been found, in order to enhance reliability of a radial distribution system. A multi-objective formulation has been considered with a view to enhance reliability at a compromised cost. DS algorithm utilizes the Brownian-like random-walk movement used by an organism to migrate. Simulation results obtained by DS algorithm have been compared with that of particle swarm optimization (PSO), differential evolutionary algorithm (DE), genetic algorithm (GA), ant colony optimization (ACO) and gravitational search algorithm (GSA). Results show that DS algorithm provides considerably superior performance, in terms of quality of solution obtained and computational efficiency.     

Electrode-spacer contour optimization by ANN aided genetic algorithm

Optimization of contours of three dimensional electrode-spacer arrangements used in gas insulated systems (GIS) has been carried out by ANN aided genetic algorithm (GA) to obtain desirable electric stress distribution along the insulator surface. Two cases have been studied and reported in detail. Multilayer feed-forward neural networks with error back-propagation algorithm have been applied to accelerate the execution of GA loop. The training and the test data have been prepared by means of electric field calculations using indirect boundary element method (BEM). The results show that optimized contours have been obtained with acceptable degree of accuracy with the help of GA aided by trained ANN.     

Reliability and costs optimization for distribution networksexpansion using an evolutionary algorithm

This paper presents a multiobjective optimization methodology, using an evolutionary algorithm, for finding out the best distribution network reliability while simultaneously minimizing the system expansion costs. A nonlinear mixed integer optimization model, achieving the optimal sizing and location of future feeders (reserve feeders and operation feeders) and substations, has been used. The proposed methodology has been tested intensively for distribution systems with dimensions that are significantly larger than the ones frequently found in the papers about this issue. Furthermore, this methodology is general since it is suitable for the multiobjective optimization of n objectives simultaneously. The algorithm can determine the set of optimal nondominated solutions, allowing the planner to obtain the optimal locations and sizes of the reserve feeders that achieve the best system reliability with the lowest expansion costs. The model and the algorithm have been applied intensively to real life power systems showing its potential of applicability to large distribution networks in practice     

Canonical piecewise-linear analysis: Generalized breakpoint hopping algorithm

As an extension to the breakpoint hopping algorithm developed in reference 1, the algorithm presented in this paper efficiently solves the d.c. problem for finding the d.c. operating point(s) and for tracing the driving-point and transfer characteristics of an extremely broad class of non-linear resistive circuits. In particular, bipolar and MOS transistor circuits are included. A user-friendly C program has been written to implement this algorithm where the input format for describing the circuit is compatible with the SPICE program.     

BDD‐based synthesis for mixed CMOS/PTL logic

Power consumption and performance are two important design constraints for logic synthesis in design automation. In this paper, we propose an efficient synthesis algorithm to minimize power dissipation and optimize performance of the given digital circuits by constructing a binary decision diagram (BDD) whose nodes can be implemented by CMOS logics and pass‐transistor logics (PTL) in a cell library. For BDD mapped circuits, the conventional synthesis algorithms need three cells: the CMOS cell, PTL cell, and CMOS remapping pattern. In the proposed synthesis algorithm, we first refine the cell library structure to two kinds of cells: PTL and CMOS cells. Next, a new algorithm is presented to select the suitable cells so that the areas and power dissipation can be decreased when the logic functions of the given digital circuits are mapped into BDD. The efficiency of this algorithm has been shown in the experimental results. Copyright © 2010 John Wiley & Sons, Ltd.     

Genetic algorithm optimization of broadband microstrip antenna

Genetic algorithm (GA) is utilized to design microstrip patch antenna shapes for broad bandwidth. A new project based on GA and high frequency simulation software (HFSS) is proposed to perform optimization. Reasonable agreement between simulated results and measured results of the GA-optimized design is obtained. The optimized patch design exhibits a three-fold enhancement in bandwidth when contrasted with a standard square microstrip antenna, showing the validity of this project.     

温州电网梯级水电站优化调度实践

建立由温州电网属下的梯级水电站群优化调度的数学模型,采用基于浮点数编码的改进遗传算法对该模型进行了优化计算,该算法设计编程简单,计算量小,收敛速度快.利用三插溪水库的入库径流实测值进行了仿真实验,实验结果表明优化调度比常规调度经济效益超过5%,说明了遗传算法用于梯级水电站群优化调度既可行又有效.     

基于遗传粒子群混合算法的机组组合优化

节能发电调度的目标是实现能耗量最小,合理安排机组发电计划则更为至关重要。在参考文献的基础上,提出了一种用于机组组合优化的遗传粒子群混合优化算法。先用遗传算法求解机组组合,再用粒子群优化算法求解负荷经济分配。按照节能调度思路对遗传算法进行了改进,提高了优化性能。给出了10机算例系统优化结果,验证了该混合算法的可行性和有效性。     

Optimum VAR sizing and allocation using particle swarm optimization

This paper proposes a solution technique for finding the optimum location and sizing of the shunt compensation devices in transmission systems. The objective of the formulation is to improve the voltage stability of the system while maintaining acceptable voltage profile. The problem can be formulated as an integer nonlinear optimization problem. The newly developed evolutionary technique particle swarm optimization (PSO) is used to solve this problem. Case studies with the Ward–Hale 6-bus, IEEE 14-bus and 30-bus systems are presented to illustrate the applicability of the algorithm. A comparison with the genetic algorithm (GA) is performed to show the quality of the solutions obtained by PSO.     

基于改进遗传算法的配电网无功优化

针对传统遗传算法在电网无功优化领域应用中存在的不足,结合配电网的特征,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。同时应用自适应遗传算法对传统遗传算法的遗传算子和终止判据等进行了改进,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。实例计算表明,其优化效果优于传统遗传算法。     

基于细菌觅食算法优化的电力变压器故障诊断技术

针对支持向量机(SVM)分类性能受参数影响,且最优参数难以获取这一问题,提出一种基于细菌觅食算法(BFA)的电力变压器故障诊断模型的参数寻优方法。该方法以电力变压器油中特征气体含量作为状态评价样本,通过BFA寻找全局最优SVM参数解,构建k-折平均分类准确率目标函数,建立变压器故障诊断模型。仿真结果表明,BFA对SVM最优参数的选取较遗传算法(GA)、粒子群算法(PSO)更迅速,且优化后的SVM电力变压器故障诊断模型具有更高的精确度;利用BFA优化方法建立的SVM电力变压器状态诊断模型,对IEC三比值法中无法判断的数据也可进行精确诊断。最后,通过实例分析,验证了方法的有效性。     

Genetic algorithm-based optimal design of shunt compensators in the presence of harmonics

This paper presents a genetic algorithm (GA)-based approach for the optimal design of passive shunt compensators when a nonideal, nonsinusoidal voltage source supplies a linear load. In contrast to traditional optimization techniques, the proposed GA has the following combined advantages: it allows both topological structure and component sizes of a compensator of any complexity to be optimized by introducing variable-length chromosomes. It is easy to change constraints or apply new ones using a death penalty or an adaptive penalty scheme for handling constraints. No preliminary calculations are necessary to identify resonant conditions in advance. Simulation results on an example system show the way and the extent to which the various apparent power components and other significant quantities are affected by compensation. An almost unity power factor is possible using a Foster form compensating circuit.     

基于搜寻者优化算法的燃料电池建模

基于连续空间群体智能的搜寻者优化算法(SOA),提出了一种新的优化燃料电池模型的方法,并将其用于质子交换膜燃料电池(PEMFC)的极化曲线模型.以得到最优参数.实验结果表明.这种优化建模方法使数学模型和实验数据之间达到了较高的拟合精度,对于优化建模具有明显的优越性.     

An optimal design method for efficiency of permanent magnet motors

In optimal design of a permanent magnet (PM) motor, many design variables are required to consider some device properties. These variables are, for example, the shape of core and magnet, the tooth length, the number of turns, and the winding radius. Moreover, many restrictions must be considered in practical PM motor design. These restrictions are, for example, the slot space factor and the cogging torque. However, the optimization problem, which has many design variables by the finite element method (FEM), has not been reported. In this paper, the efficiency of PM motor under the considerations given above is optimized by using FEM and optimization algorithm. In this problem, an objective function has many local minima and it is difficult to calculate its gradient. For these reasons, the genetic algorithm (GA) and simulated annealing method (SA), which are stochastic methods, are used for optimization method, because of the possibility of global range search and because gradient calculation is not required. Adding to both optimization methods, in this paper, SA combined with GA is used for one of the optimization methods. It is found that the solutions optimized by these methods are reasonable from an engineering point of view. © 2001 Scripta Technica, Electr Eng Jpn, 138(3): 72–79, 2002