基于改进微分进化算法的负荷模型参数辨识

为了提高电力系统中负荷模型的精确度,提出了一种改进的微分进化算法(IDE)以辨识负荷模型参数。采用不依赖于优化问题的控制参数自适应调整机制,同时考虑搜索速度和搜索精度,使算法摆脱后期易于陷入局部极值点的束缚,克服了微分进化算法参数调整困难的不足,提高了算法的寻优能力。将改进算法应用于静态负荷模型参数辨识的工程实例并与其他算法对比的结果表明,改进DE算法的全局搜索能力强,搜索精度高。     

一种求解电力经济负荷分配问题的改进微分进化算法

针对电力系统经济负荷分配(economic dispatch,ED)这一典型的非凸、非线性、组合优化问题,提出一种改进的微分进化(improved differential evolution,IDE)算法。微分进化(differential evolution,DE)算法虽有简单、搜索效率高的优点,但是仍然有局部最优的问题。该文在对DE算法搜索机理进行分析的基础上,针对DE算法参数难于动态调整的问题,提出不依赖于优化问题的控制参数自适应调整机制,并根据动态监视群体适应度方差的变化,增加个体迁移策略,进一步提高DE算法的全局寻优能力和鲁棒性。运用该算法对IEEE3机、40机及69机300节点标准测试用例进行计算,并考虑机组的爬坡约束、出力限制区约束、非光滑费用函数曲线等非线性特性,将其计算结果与遗传算法(genetic algorithm,GA)及粒子群算法(particle swarm optimization,PSO)进行比较,分析表明该方法是可行的、有效的。     

基于改进微分进化算法的电力系统无功优化

提出了一种改进的微分进化算法求解电力系统无功优化问题。在进化过程中，该算法根据进化情况采用动态参数调整机制提高算法的搜索效率，并且对种群重叠状况进行实时监视，对重叠个体利用混沌搜索策略来进一步提高算法的全局寻优能力。通过对IEEE 6、IEEE 30、IEEE 118标准测试系统及某地区实际系统的无功优化问题计算及结果分析表明，文中提出的改进微分进化算法高效、且全局寻优能力强。     

基于AEPSO-BPNN的光伏阵列多场景参数辨识

针对光伏阵列内部机理较为复杂、参数难以快速准确辨识的问题,提出了一种自适应进化粒子群算法优化BP神经网络（AEPSO-BPNN）的模型建立和参数辨识方法。通过引入自适应、进化和重构等改进策略,可以提高粒子群算法的收敛性能,并将其对BP神经网络的初始权值和阈值进行优化,使神经网络算法在迭代后期不易陷入局部最优解,以提高参数辨识的精确度和速度。根据光伏阵列的实测输出电流和理论计算电流的差值,并考虑环境变化对内部参数的影响,构造均方根误差函数作为算法的适应度函数,从而将复杂的多参数辨识问题转化为带约束条件的非线性多变量最优化问题。最后采用多场景法,验证算法在不同光照强度和温度下的适用性和效果,并与其他算法进行对比,仿真结果表明该算法在误差、收敛速度和运行时间上有较大优势。     

On some parameter estimation algorithms for the nonlinear exponential autoregressive model

Modeling an exponential autoregressive (ExpAR) time series is the basis of solving the corresponding prediction and control problems. This paper investigates the hierarchical parameter estimation methods for the ExpAR model. By the hierarchical identification principle, the original nonlinear optimization problem is transformed into the combination of a linear and nonlinear optimization problem, and then, we derive a hierarchical least squares and stochastic gradient (LS‐SG) algorithm. Given the difficulty of determining the step‐size in the hierarchical LS‐SG algorithm, an approach is proposed to obtain the optimal step‐size. To improve the parameter estimation accuracy, the multi‐innovation identification theory is employed to develop a hierarchical least squares and multi‐innovation stochastic gradient algorithm for the ExpAR model. Two simulation examples are provided to test the effectiveness of the proposed algorithms.     

基于改进差分进化算法的配电网无功优化

采用改进差分进化算法(Improved Differential Evolution Algorithm,IDEA)求解配电网无功优化问题。该算法引入基于反学习的种群初始化方法,使算法得到的初始种群具有多样性,能够充分提取搜索空间的信息;引入高斯扰动机制到交叉操作中,提高了在维尺度上的种群多样性;在进化过程中融入人工蜂群搜索思想,引入蜂群加速进化与侦查操作策略,使算法能快速跳出局部最优,避免了早熟问题。建立了配电网无功优化数学模型,并采用IDE算法对IEEE30节点系统求解该模型,并与基本DE算法进行对比,仿真结果证明了所提IDE算法具有更佳的性能,能够有效的求解配电网无功优化的问题。     

考虑VSC系统谐波稳定条件下的LCL滤波器参数优化设计方法

可再生能源发电系统通常在逆变器出口处接入LCL滤波器以抑制高频谐波,然而在LCL滤波器的参数设计过程中,不合理的参数设计会加剧系统中的谐波不稳定问题。因此,通过结合LCL滤波器的外特性需求,并且综合考虑系统稳定性对滤波器的约束条件,建立以谐波衰减率最大、电感成本最小以及系统稳定性最好为目标函数的LCL滤波器参数多目标优化模型。在模型的求解过程中,针对单目标优化算法和传统的多目标优化算法存在设计目标单一、收敛性差和优化效果不佳等问题,提出采用MOEA/D多目标优化算法来求解。该算法利用权重向量的进化导向性以及子问题邻域的优势共享特征,保证了算法对最优解的寻优能力以及在目标空间上的均匀分布,以获得理想的pareto最优前沿,并进一步采用模糊隶属度函数定量分析其优化效果。最后,利用Matlab/Simulink软件对光伏并网系统中的电压源型逆变器(Voltage Source Converter, VSC)进行仿真。仿真结果充分表明所设计方案的有效性和适用性。     

Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering

This paper discusses the state and parameter estimation problem for a class of Hammerstein state space systems with time delay. Both the process and the measurement noises are considered in the system. On the basis of the observable canonical state space form and the key term separation, a pseudolinear regressive identification model is obtained. For the unknown states in the information vector, the Kalman filter is used to search for the optimal state estimates. A Kalman filter–based least squares iterative and a recursive least squares algorithms are proposed. Extending the information vector to include the latest information terms, which are missed for the time delay, the Kalman filter–based recursive extended least squares algorithm is derived to obtain the estimates of the unknown time delay, parameters, and states. The numerical simulation results are given to illustrate the effectiveness of the proposed algorithms.     

Optimal analog‐to‐digital transformation of fractional‐order Butterworth filter using binomial series expansion with Al‐Alaoui operator

This paper deals with the optimal analog‐to‐digital transformation of fractional‐order Butterworth filter (FOBF) in terms of infinite impulse response templates. The fractional‐order transfer function of the analog FOBF is transformed into its digital counterpart by employing the Binomial series expansion of different truncation orders, based on the Al‐Alaoui operator. This nonoptimal solution is then treated as an initial point for a local search optimizer such as the Nelder–Mead simplex (NMS) algorithm and also injected as a super‐fit individual in the initial population of a global search constrained evolutionary optimization algorithm (CEOA). Design stability and minimum‐phase response constraints are formulated for the super‐fit scheme. Both the techniques demonstrate good modeling performance; however, the super‐fit CEOA can markedly outperform the NMS method as the problem dimensionality increases.     

Dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants

This paper proposes dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed‐integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), multi‐population DEEPSO (MP‐DEEPSO), and brain storm optimization have been applied so far. When optimal operational planning of numbers of energy plants is calculated simultaneously in a data center, a challenge is to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for the challenge is speeding up by parallel and distributed computing. It utilizes numbers of processes and countermeasures for various faults of the distributed processes should be considered. Moreover, successive calculation at every control interval is required for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality is required even if some of the calculation results cannot be returned from distributed processes. It is verified that total energy cost by the proposed dependable multi‐population improved brain storm optimization with differential evolution strategy based method is lower than those by the compared methods, and higher quality of solutions can be kept even with high fault probabilities.     

基于类电磁机制算法的综合能源系统多目标优化调度

针对综合能源系统低碳经济调度的优化问题,引入P2G设备提升系统的弃风消纳空间与低碳经济性,以运行成本与碳排放量为优化目标,建立含多种供能设备的综合能源系统多目标优化调度模型。模型的求解采用改进的类电磁机制算法,针对综合能源系统调度问题约束较为复杂,算法易陷入局部最优的问题,引入立方混沌初始化、差分进化策略和自适应权重因子,增强了算法的全局搜索能力与收敛性。结果表明,提出的IELM算法在全局最优解方面、收敛效率方面和操作适应性具有明显优势,P2G装置能有效提升系统的弃风消纳率与低碳经济成本。     

MPC‐based approximate dual controller by information matrix maximization

This paper proposes a method to approximate a dual controller by a computationally feasible algorithm. Dual control that optimally solves the problem of simultaneous control and identification of a system with uncertain parameters is known to be both analytically and computationally unsolvable. This paper proposes a multiple‐step active control algorithm that gives a suboptimal but tractable solution to the original dual control problem. The algorithm is based on model predictive control (MPC) and approximates persistent system excitation in terms of the increase of the lowest eigenvalue of the parameter estimate information matrix. The problem is formulated as a two‐phase optimization problem, where first an MPC solution is found and then the lowest eigenvalue of the information matrix is maximized in the next step within a given permitted input perturbation. Unlike similar methods, the proposed algorithm predicts the information matrix for more than one step of control, which makes it possible to uniformly excite the parameter space. The use of MPC in the first design phase instead of a cautious controller is justified by showing unfavorable properties of cautious control. The advantage of the multiple‐step prediction over single‐step prediction is shown by examples and simulations. The proposed algorithm is analyzed in terms of convergence and complexity, and stability issues are addressed. The formal proofs are included in the Appendix. Copyright © 2012 John Wiley & Sons, Ltd.     

负荷模型参数辨识的粒子群优化法及其与基因算法比较

粒子群优化法(PS算法)具有全局性能好、搜索效率高等优点。文中应用该算法进行电力系统负荷模型的参数辨识，并将其与模拟进化算法进行比较，发现PS算法在计算时间、全局性方面均有比较明显的优势。讨论了PS算法中用以调节全局搜索和局部搜索关系的权重ω与搜索效率之间的关系，并给出了适用于电力系统负荷参数辨识的ω值。提出了一种利用PS算法的收敛快速性来提高全局性能的工程实用方法，并对工程实例进行辨识，收到了良好效果。     

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

In this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.     

电力系统经济负荷分配的量子粒子群算法

本文首次将量子粒子群算法用于电力系统经济负荷分配中。该算法是以粒子群中粒子的收敛特性为基础,依据量子物理理论提出的,改变了传统粒子群算法的搜索策略,可使粒子在整个可行解空间中搜索寻求全局最优解。同时该算法的进化方程中不需要速度向量,而且进化方程的形式更简单,参数较少且容易控制。对两个算例进行仿真测试,证实该算法可有效解决经济负荷分配问题;性能对比显示,该算法求得的解优于已有的改进粒子群算法及其它优化算法所求得的解。本文为量子粒子群算法用于经济负荷分配的实用化研究奠定了必要的理论基础。     

改进Tabu优化算法在地区电网故障诊断的应用

基于优化算法的电网故障诊断是将电力系统故障诊断描述为优化问题后用优化方法求解。提出了基于改进Tabu搜索算法的电力系统故障诊断的新方法。以覆盖集理论为基础, 把电力系统警报处理问题表示为0-1整数规划问题, 并引入了一种新的评估指标,确保了全局最优解,并极大地满足了电网实时性的要求。最后用一个全新算例来证明计算结果的可靠性与快速性, 结果表明以改进TS为基础的方法能够满足地区电网故障诊断的要求。     

A robust optimal design for strictly positive realness in recursive parameter adaptation

This paper provides an optimization‐based approach to assure the strict positive real (SPR) condition in a set of recursive parameter adaptation algorithms (PAA). The developed algorithms and tools enable a multiobjective formulation of the SPR problem, creating new controls of the stability and parameter convergence in PAAs. In addition to assuring the SPR condition for global stability in PAAs, we provide an algorithmic solution for uniform convergence under performance constraints in PAAs. Several new aspects of parameter convergence were observed with the adoption of the algorithm in a narrow‐band identification problem. The proposed algorithm is verified in simulation and experiments on a precision motion control platform in advanced manufacturing.     

基于随机聚焦粒子群算法的多机系统PSS参数优化

随机聚焦粒子群算法（SFPSO）是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的群体智能优化算法。通过采用SFPSO算法,对多机系统的PSS参数进行优化。该方法是以最优控制原理为基础,综合考虑PSS与励磁系统的性能,将PSS 参数优化协调转化为带有不等式约束的优化问题,控制目标为系统输出按照最小误差跟踪给定值的能力。通过仿真测试以及不同算法优化结果的对比,表明基于SFPSO算法优化的PSS在不同的干扰下都具有良好的性能,能够抑制低频振荡,并保持系统稳定,同时证明了SFPSO算法的有效性和优越性。     

一种综合负荷模型参数辨识的混沌优化策略

针对等值于静态负荷和感应电动机负荷的电力系统综合负荷模型的参数辨识,提出了一种高精度的混沌优化算法,该方法利用混沌运动的随机性、规律性和遍历性的特点来寻优,具有全局优化的特点。此混沌优化方法无须优化问题具有连续性和可微性,它按自身的规律进行搜索,克服了传统辨识方法对初值要求高,鲁棒性差,容易陷于局部极值点的缺陷。文中采用了2种混沌映射,3步混沌搜索,并引入随机数来增强遍历性和加快收敛速度。实际算例的结果证实了该算法对综合负荷模型参数辨识的有效性和准确性。     

基于MDPSO的永磁直驱风力发电机参数辨识

针对永磁直驱风力发电机的多参数辨识问题以及传统参数辨识方法的收敛精度差、收敛速度慢等问题,提出了引入平均最优位置变量的自适应空间搜索向量的改进粒子群算法(MDPSO),对永磁直驱风力发电机参数辨识。根据永磁直驱风力发电机定子电压电流模型,进行pade近似并降阶处理后进行离散化,建立直驱风力发电机辨识模型;引入自适应空间搜索向量和平均最优位置变量改进粒子群算法;应用提出的MDPSO辨识直驱风力发电机定子绕组的电阻、电感和磁链等参数。算例仿真结果表明,提出的辨识算法具有精度高、计算速度快、稳定性高等特点,从而验证了建立的直驱风力发电机辨识模型及辨识算法的有效性。