Expected security cost‐based FACTS device allocation using hybrid PSO

This paper presents FACTS device allocation for market‐based power systems. The purpose of the FACTS installation is to provide benefit for all entities. Therefore, we use a concept of minimizing expected security cost (ESC), implying to maximize social welfare, to minimize operating reserve, and to minimize load interruption in annual basis. The FACTS device investment cost is also taken into account. The ESC considers operating cost under normal state and contingencies along with their associated probabilities to occur. Annual basis bid data is used to compute ESC as well as the cost data for load interruption and reserve procurement. This paper also considers control actions among multiple FACTS device to achieve minimum ESC. To solve the overall problem, particle swarm optimization is utilized to obtain optimal FACTS device allocation as main problem while sequential quadratic programing is used to solve optimal power flow as suboptimization problem. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Modeling the resonant frequency of compact microstrip antenna by the PSO‐based SVM with the hybrid kernel function

A methodology based on the support vector machine (SVM) combined with a hybrid kernel function (HKF) for accurately modeling the resonant frequencies of the compact microstrip antenna (MSA) is presented and dedicated to reduce the number of samples and simplify the structure when predicting the resonant frequency of the compact MSA by artificial neural network. The parameters of the SVMs and weight coefficients of the HKF are optimized by means of particle swarm optimization algorithm. In addition, two different kernel functions (KFs), namely polynomial KF (a kind of global KF) and Cauchy KF (a kind of local KF), are employed to overcome the disadvantages of traditional KF. The proposed method is validated by the UCI database. The evaluation results show that the HKF can improve the learning ability and generalization ability of the SVM. Furthermore, the resonant frequencies of a planar inverted F‐shaped antenna and an L‐shaped MSA are modeled by the proposed method. Predictive results with high accuracy demonstrate that the particle swarm optimization‐based SVM with the HKF can improve the prediction accuracy for a small dataset. Copyright © 2016 John Wiley & Sons, Ltd.     

基于粒子群TOA室内定位的系统误差修正

到达时间（time of arrival,TOA）的测距易受多径干扰的影响而产生较大的系统误差,造成室内定位时精度变差。针对上述问题,首先分析了TOA定位中系统误差的产生及特点,而后提出一种基于粒子群优化的定位算法。算法利用测距值与所求解位置的空间约束关系建立求解域,而后应用粒子群算法求解,并通过建立关于系统误差的罚函数和适应度函数实现误差修正,并减小粒子搜索空间,加快算法收敛速度。实验表明,利用本文描述的定位算法,可以有效抑制室内定位中测距产生的系统误差,定位精度得到明显提高。     

基于改进BP神经网络的低压配电台区智能电能表误差状态评估模型

针对配电网台区中智能电能表误差估计问题,基于粒子群优化BP神经网络提出智能电能表误差估计方法。所提方法从数据搜集和数据预测、预处理建立智能电能表误差估计模型;针对传统BP神经网络隐含层节点数制定的局限性,提出采用粒子群优化算法对隐含层节点数进行优化,并采用优化得到的隐含层节点数构建BP神经网络结构对训练样本数据进行训练,基于训练得到的BP神经网络对测试样本数据进行计算得到智能电能表误差数据。针对某地区典型配电网台区中智能电网运行误差估计问题,采用所建立的方法进行智能电能表运行误差的评估。仿真算例表明,所建立的模型能够有效评估智能电能表运行误差,相比于传统的评估方法,其评估准确性有显著提升。     

An improved elman neural network controller based on quasi‐ARX neural network for nonlinear systems

An improved Elman neural network (IENN) controller with particle swarm optimization (PSO) is presented for nonlinear systems. The proposed controller is composed of a quasi‐ARX neural network (QARXNN) prediction model and a switching mechanism. The switching mechanism is used to guarantee that the prediction model works well. The primary controller is designed based on IENN using the backpropagation (BP) learning algorithm with PSO. PSO is used to adjust the learning rates in the BP process for improving the learning capability. The adaptive learning rates of the controller are investigated via the Lyapunov stability theorem. The proposed controller performance is verified through numerical simulation. The method is compared with the fuzzy switching and 0/1 switching methods to show its effectiveness in terms of stability, accuracy, and robustness. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于动态改变惯性权值的粒子群算法

粒子群算法的速度更新公式是通过惯性权值来调节前代速度对当代速度的影响。标准粒子群算法的惯性权值是采用线性递减策略,这使得算法极易收敛到局部最优;而且这种方法依赖于最大迭代次数的设定,使得惯性权值的选取具有盲目性。本文提出一种动态改变惯性权值的方法,充分利用目标函数所提供的信息,构造按指数衰减的惯性权值并进行了分析,最后对一标准测试函数进行了仿真。结果表明,所提算法能够得到更好的优化效果,验证了方法的有效性。     

Polynomial chaos‐based macromodeling of multiport systems using an input–output approach

An innovative technique to build stochastic frequency‐domain macromodels of generic linear multiport systems is presented. The proposed method calculates a macromodel of the system transfer function including its statistical properties, making it tailored for variability analysis. The combination of the modeling power of the Vector Fitting algorithm with the Polynomial Chaos expansion applied at an input–output level allows to accurately and efficiently describe the system variability features. Thanks to its versatility and automated order selection, the proposed technique is suitable to be applied to a large range of complex modern electrical systems (e.g., filters and interconnections) and can tackle the case of correlated random variables. The performance in terms of accuracy and computational efficiency of the proposed method is compared with respect to the standard Monte Carlo analysis for two pertinent numerical examples. Copyright © 2014 John Wiley & Sons, Ltd.     

基于混合Copula函数的洪水联合概率计算方法

洪水频率计算是涉水工程规模确定的依据。混合Copula函数比单一Copula函数能够更好地刻画多维洪水随机变量的分布特征。本文以加拿大魁北克省马达瓦斯克河流域的洪水数据为例,利用单一Copula函数构造两种混合Copula函数,采用粒子群算法和遗传算法求解混合Copula函数权重系数,并选择拟合最优的混合Copula函数进行洪水频率计算。结果表明：混合Copula函数的均方误差最大值分别为0.000567和0.000352,均小于单一Copula函数指标值,且由旋转Copula函数构成的混合函数效果更优。变量较大值组合发生时,混合Copula函数联合重现期计算结果小于单一Copula函数计算结果,同现重现期计算结果要大于单一Copula函数计算结果。     

Optimization of Electric Power Leveling Systems by Taper‐Off‐Reflectance Particle Swarm Optimization

It has become desirable to develop energy control technologies for environmental issues such as global warming and exhaustion of fossil fuel. Power fluctuations in large power consumer facilities may cause instability of electric power systems and increase the cost of the electric power facilities and electricity charges. Developing electric power leveling systems (EPLS) to compensate power fluctuations is necessary for future electric power systems. EPLS with an SMES has been proposed as one countermeasure for use in electric power quality improvement. SMES is superior to other energy storage devices in response and storage efficiency. The authors have proposed EPLS based on fuzzy control with SMES. For this practical implementation, optimizing the control gain and SMES capacity is an important issue. This paper proposes a new method for optimization of the EPLS. The proposed algorithm is a novel particle swarm optimization based on taper‐off reflectance (TRPSO). The proposed TRPSO optimizes the design variables of the EPLS efficiently and effectively. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(3): 10–18, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22472     

改进惯性权重的粒子群目标跟踪算法

粒子群优化算法中惯性权重的设置极其重要,直接影响算法性能。提出了一种改进的粒子群优化算法,并应用到目标跟踪领域,提高算法运算效率。首先,设置粒子群优化算法中的参数;其次,结合粒子优化率的概念记录粒子的不同状态,进而调节惯性权重,更新粒子的速度和位置;最后,对目标相似性函数进行优化,实现目标的准确定位。实验结果表明,该方法可以有效应对目标出现部分遮挡的跟踪难题,同时提高目标跟踪效率,具有较好的实时性。     

Bias compensation‐based parameter estimation for output error moving average systems

Identification problems of output error models with moving average noises are considered in this paper. The least‐squares‐based parameter estimation is biased under the colored noises in outputs. Firstly, a bias compensation term is formulated to achieve the bias‐eliminated estimates of the system parameters. Secondly, the bias compensation term is determined by the unknown variance of the noise and the unknown noise model, thus based on the hierarchical identification principle, an unbiased parameter estimation is obtained by interactively estimating noise variance and noise parameters. Finally, the estimated bias compensation term is added to the biased parameter estimates. The simulation examples confirm the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

基于改进粒子群算法的电力系统经济负荷分配

提出了一种应用随机优化理论求解电力系统经济负荷分配的新方法,该方法以电力市场全天购电费用最小为目标函数,将高斯算子和交叉算子引入基本粒子群算法中。针对基本粒子群算法（PSO）的局限性,通过引入新的算子,克服了PSO算法前期精度低、后期收敛速度慢、易于陷入局部最优等缺点,在速度和精度上满足了计算要求。算例结果表明,所提出的方法能有效解决电力市场电力系统经济负荷分配问题。     

Experimental study of a chaos‐based communication system in the presence of unknown transmission delay

In this paper, a chaos‐based communication system in the presence of unknown transmission delay is studied. First, under master/slave configurations, two chaotic Chen systems as the slaves are synchronized with a master one via unidirectional coupling. Then at the transmitter, information of an image is embedded within a slave‐one signal and the resultant signal along with another one which is used to determine the transmission delay is transmitted to the receiver. At the receiver, using the received signals and the slave‐two counterpart signal, the image is recovered. The performance of the proposed method is experimentally illustrated. Copyright © 2009 John Wiley & Sons, Ltd.     

Frequency characteristics of electromagnetic bandgap structure with bow‐tie cells and its optimal design based on particle swarm optimization

The electromagnetic bandgap (EBG) structure, which is widely used in the microwave and millimeter‐wave fields, is a kind of photonic crystal. An EBG structure with bow‐tie cells having different tapered forms is presented in this paper. Furthermore, the EBG structure is optimized by using an improved particle swarm optimization algorithm. The optimal results show that the optimized EBG structure with bow‐tie cells has a good stop‐band performance and large relative bandwidth. The passband ripples of the EBG structure are very small and basically symmetrical. The frequency characteristic of the optimized EBG structure is found to be excellent. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于数据测试和粒子群优化算法的光伏逆变器LVRT特性辨识

为了对光伏逆变器低电压穿越控制精确建模,提出灵敏度分析和实测方案相结合的光伏发电单元低电压穿越控制参数辨识方法。首先对光伏单元待辨识参数进行灵敏度分析,提出辨识所用数据的测试方案;然后利用自适应惯性权重粒子群优化智能算法,结合多组实测数据对光伏并网系统低电压穿越控制参数予以辨识,从多组辨识结果中提取最优值;最后将最优值代入模型中,计算模型输出与实测数据的误差,验证了参数辨识结果的准确性。该方法考虑了逆变器功率等级不同给辨识结果带来的误差,辨识结果准确度较高并且多次辨识结果具有一致性,可用于工程实际计算。     

Optimal analog active filter design using craziness‐based particle swarm optimization algorithm

Because of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness‐based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth‐order Butterworth low‐pass analog active filter and second‐order state variable low‐pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low‐pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Design of PSO‐based fuzzy logic controller for single axis magnetic levitation system

In this paper, a particle swarm optimization (PSO) method is proposed to design an optimal robust fuzzy logic controller (FLC). The objective of this paper is to design a nonlinear optimal robust controller for the single axis magnetic levitation system with high accuracy. PSO algorithm is applied to search globally optimal parameters of FLCs. Three different FLCs are designed. First, proportional derivative (PD)‐like FLC. Second, the FLC is based on the PSO algorithm to find the optimal range of the eight linguistic membership functions (FLC1 with PSO algorithm). Finally, the FLC is based on the PSO algorithm to find the optimal range and shape of the four linguistic membership functions (FLC2 with PSO algorithm). The performances of three different FLCs are compared. Simulation results show that PSO‐based optimal FLCs find the optimal range and shape of the four linguistic membership functions and achieved better performance than the other proposed controllers, minimizing 48 fuzzy rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于粒子群优化算法的VSC-HVDC系统的控制参数优化策略

建立了双端基于电压源型换流器的高压直流输电(VSC-HVDC)系统的小信号模型;利用特征根分析法,基于线性化状态空间模型,将与系统特征根直接相关的振荡模式和衰减模式的罚函数作为目标函数;提出了基于粒子群优化算法的VSC-HVDC系统的控制参数优化策略,对整个系统的控制参数同时进行整体优化。仿真结果验证了小信号模型的正确性;优化后的系统在小扰动、大扰动、潮流反转及故障情况下均具有较高的控制精度,整个系统的稳态与暂态特性均得到较大改善。     

Damping of low‐frequency oscillations using an SVC‐based supplementary controller

In this paper, the optimal tunning problem of parameters of a conventional lead–lag controller (LLC) and a fuzzy logic controller (FLC) based on the static Var compensator (SVC) is considered. The solution is obtained using an improved particle swarm optimization (IPSO) algorithm. The membership functions and scaling factors of the FLC and LLC parameters are optimized using the above‐mentioned technique. The proposed controllers are settled down to an SVC that is installed at the middle of a transmission line connecting a single machine to an infinite bus system. Simulation results show the superiority of the optimized FLC compared to the optimized LLC and also when the SVC is without the supplementary controller under different disturbances and loading conditions. The simulations and analyses are implemented in MATLAB environment. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于有理函数逼近的光耦合隔离放大器宽频域建模方法

目前光耦合隔离放大器频率特性的获取方法多是基于额定参数推算,但经过长时间运行后,内部元件参数因老化发生改变,隔离放大器内部元件不再以额定参数工作,导致基于额定参数得到的频率特性不再准确.针对这一问题,提出一种光耦合隔离放大器宽频域建模方法.在不依赖其元件参数的情况下,通过测量不同频率下隔离放大器传递函数值,基于有理函数...