利用卡森准则的正弦调频信号参数估计方法

针对现有的正弦调频(SFM)信号参数估计算法运算量大、受调频系数范围限制等缺点,提出了一种利用卡森准则的SFM信号参数估计算法。首先分析信号及其瞬时频率的频谱特征,推导了信号载频和调制频率的估计表达式;然后通过带宽测量,依据卡森准则提取了信号的调制指数。该算法不受调制指数取值的制约,且运算量仅为相同条件下WVD分布的1%。仿真表明,当SNR≥6dB时,参数均方根误差小于?10dB。     

基于GLRT和APES的MIMO雷达参数估计新算法

为了获得高精度的多输入多输出（multiple-input multiple-output,MIMO）雷达的目标角度估计和幅度估计,本文基于幅度相位估计（amplitude and phase estimation,APES）算法和广义似然比检验（generalized-likelihood ratio test,GLRT）算法,提出了一种使用似然比的相位幅度估计（GAPES）新算法。在接收端通过GLRT算法估计出目标角度,然后用APES算法修正幅度,实现了在强干扰环境下提高角度和幅度分辨率。仿真结果表明了该算法的有效性。     

一种新的分布式电力系统状态估计算法

随着电力系统的发展,区域电网互联,形成了规模更大的系统。为了适应这一趋势,电力系统状态估计应采用分布式算法。在配置少量PMU的基础上,将分区后的边界等式约束条件通过拉格朗日乘子计入整体目标函数,将分布式状态估计的问题转化为一个带等式约束的最优化问题,实现了电力系统状态估计的分布式计算。该算法不仅提高了状态估计的速度,而且可在不必改动原有状态估计模块的基础上,很容易地加入等式约束的修正模块。最后通过IEEE14节点和IEEE30节点系统的模拟仿真,验证了该算法的有效性和优越性。     

基于全相位频谱分析的正弦信号高精度参数估计方法

对传统频谱分析的输入数据截断方式进行了改进,提出了全相位频谱分析,理论证明全相位频谱分析幅度谱是传统FFT频谱分析幅度谱的平方,可以很大程度上减小频谱泄露,同时,给出了其实现框图.另外.全相位频谱分析方法具有良好的相位分析性能,不受频偏影响,在信号是非整数倍频率采样情况下,由该方法分析的相位与信号真实相位误差极小,无需校正.提出基于全相位相位差法估计正弦信号幅值、频率和相位的新算法,对传统相位差法其序列的取法进行了改进,对单频余弦信号进行非整周期采样,分别用全相位相位差法和离散频谱综合相位差校正法对信号的频率、幅值和相位进行了校正.仿真结果证明该算法参数估计精度高于现有算法,具有较好的实用价值.在无噪情况下参数估计近似为无偏估计,尤其对相位的估计,误差达到0.001%.     

一种新的三相状态估计算法

提出了一种新的三相状态估计算法 ,并详细地阐述了一种三相状态估计的处理策略。     

WLAV state estimation for power system containing multitype FACTS devices

This paper presents the state estimation of power system in which not only the bus voltages but also the states of the flexible AC transmission system (FACTS) are considered as the state variables. By using the rectangular form of state variables and equivalent measurement techniques, a linear measurement model with constraints of FACTS device is obtained. The predictor–corrector interior point method based on the weighted least absolute value criterion is developed for solving the optimization problem. Simulation results on the modified IEEE 14‐bus and 118‐bus test systems are provided. The numerical results indicate the effectiveness of the proposed approach. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

How to exploit external model of data for parameter estimation?

Any cooperation in multiple‐participant decision making (DM) relies on an exchange of individual knowledge pieces and aims. A general methodology of their rational exploitation without calling for an objective mediator is still missing. Desired methodology is proposed for an important particular case, when a participant, performing Bayesian parameter estimation, is offered a model relating the observable data to their past history. The designed solution is based on the so‐called fully probabilistic design (FPD) of DM strategies. The result reduces to an ‘ordinary’ Bayesian estimation if the offered model is the sample probability density function (pdf), i.e. if it provides additional observations. Copyright © 2005 John Wiley & Sons, Ltd.     

Modified algorithm of the revised simplex method applied to power system state estimation

A modification of the revised Simplex method applied to a weighted least-absolute-value state estimation is considered. A large basis matrix is replaced by a much smaller submatrix, so that memory requirements are considerably reduced. The algorithm is presented in detail, together with results of simulation studies which confirmed the good performance of the algorithm.     

A complete decomposition and coordination algorithm for large‐scale hydrothermal optimal power flow problems

This paper presents a complete decomposition and coordination algorithm to solve large‐scale hydrothermal optimal power flow (HTOPF) problems. Based on the approximate Newton directions method, which decouples the first‐order Karush–Kuhn–Tucker conditions of the original problem, an HTOPF problem with cascaded hydro plants is decomposed into a thermal plant subproblem with independent optimal power flow solutions for each time period and a hydro plant subproblem combined with fixed and variable heads and cascaded plants issues. In order to verify the effectiveness of the proposed algorithm, numerical tests are performed on three large‐scale test systems with up to 3120 buses and 7 531 915 primal–dual variables over 168 time periods. Test results show that the proposed algorithm gives excellent performances in convergence and stability. It not only reduces memory usage significantly but also decreases CPU time by about 65–75%. With parallel computing, it is capable of achieving 10–20 times or even 1000 times speed without loss of optimality. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Scalar controlled induction motor drive speed estimation by adaptive sliding window search of the power signal

This work reports an improved estimation reliability wide range rotor speed estimation algorithm for scalar controlled wound rotor induction machines using spectral search of the stator power signal. An adaptive sliding window speed estimation method is proposed that monitors the value of supply frequency to identify the optimum boundaries of the power signal narrowband maximised by a tracked speed dependent power harmonic, while adjusting to nominal slip variation with supply frequency change characteristic of scalar controlled machines. Analytical expressions are derived that allow direct correlation of the surveyed spectral window boundaries to supply frequency. Once identified, the optimal power spectral narrowband enables supply frequency independent speed evaluation. The proposed method is underpinned by a dichotomous search based frequency tracking technique enabling improvement in the attainable estimation rate. The presented technique’s performance and limitations are assessed in steady-state and transient operation real-time experiments on two different scalar controlled 7.5 kW machines. The scheme is shown to provide competent real-time speed estimation in a wide operating speed range for lower dynamics transients and steady-state operation.     

Pattern search algorithm based automatic online parameter estimation for AGC with effects of wind power

The increasing portion of wind energy in the power system puts forward stability challenges to the power grid. With introduction of wind generating systems, maintaining constant frequency became one of the important problems taking stability into account. The system could be maintained with constant frequency with the help of controllers. The controller gains are usually designed with fixed values for various scenarios of power system which considerably vague due to existing system complexity. Also, the usage of conventional techniques consumes a lot of computational time and does not possesses accurate control gain parameters. To address the aforementioned challenges, an automatic online gain estimation algorithm by using pattern search optimization technique has been proposed in this article. This method computes the parameters on its own based on variable wind power output and controller gains will adjust automatically to achieve the best desired performance. The proposed method was tested in a modified IEEE 39 bus system for scenarios like system with 3% and 10% GRC (Generator Rate Constraint) with 10% and 20% SLP (Step Load Perturbation). Following that the system with proposed method was investigated with variable wind power and results demonstrate significant better results which comply with IEEE standards.     

A study on adaptive noise canceller using linear phase filter

In this paper, we propose a new adaptive noise canceller using a linear phase filter. The linear model in which an arbitrary signal is defined by the output signal of a linear system at the white signal input is used. The noise is suppressed by the estimated linear system and the signal‐to‐noise ratio is improved. At this point, to minimize the distortion of the signal due to the nonlinearity of the phase shift, the linear phase filter has been newly introduced. The transfer function of the linear system is an arbitrary minimum phase rational transfer function that has poles and zeros. It has the feature of not being specified for all pole models. The adaptive algorithm is a gradient‐based algorithm with few computational complexities. The features of the proposed adaptive noise canceller are that the inverse filter of the adaptive filter is stable, the convergence of the algorithm is guaranteed, the distortion of the signal is minimal, and there is no restriction on the transfer function of the linear system. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 178(1): 50–55, 2012; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21116     

Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

This paper provides a modified model reference adaptive control (MRAC) scheme to achieve better transient control performance for systems with unknown unmatched dynamics, where an adaptive law with guaranteed convergence is introduced. We first revisit the standard MRAC system and analyze the tracking error bound by using L₂‐norm and Cauchy‐Schwartz inequality. Based on this analysis, we suggest a feasible way to compensate the undesired transient dynamics induced by the gradient descent–based adaptive laws subject to sluggish convergence or even parameter drift. Then, a modified adaptive law with an alternative leakage term containing the parameter estimation error is developed. With this adaptive law, the convergence of both the estimation error and tracking error can be proved simultaneously. This enhanced convergence property can contribute to deriving smoother control signal and improved control response. Moreover, this paper provides a simple and numerically feasible approach to online verify the well‐known persistent excitation condition by testing the positive definiteness of an introduced auxiliary matrix. Comparative simulations based on a benchmark 3‐DOF helicopter model are given to validate the effectiveness of the proposed MRAC approach and show the improved performance over several other MRAC schemes.     

A digital signal processing algorithm for detecting arcing faultson power distribution feeders

Signal processing hardware and software that can be used to improve the detection of certain power system faults using computer relays are discussed. Integrated systems and architectures for monitoring several fault-sensitive parameters have been investigated. A suggested architecture utilizing several processors is presented. Several fault-sensitive parameters for the detection of arcing faults are presented. A detection methodology based on these parameters is described, and a partial solution to the problem of directionality is discussed. The use of a knowledge-based environment to modify protection criteria is suggested     

基于三次相位变换的多项式相位信号参数估计

分析了多分量三阶多项式相位信号交叉项对三次相位变换参数估计方法带来的影响,提出了一种改进三次相位变换的多项式相位信号参数快速估计方法,利用Radon变换对三次相位变换结果进行优化,消除交叉项影响,并对利用该方法进行三阶多项式相位信号进行参数估计的关键问题做了研究。仿真结果表明,该方法在低信噪比条件下对多分量三阶多项式相位信号估计结果较为精确。     

改进的Prony算法在多正弦信号频率估计中的应用研究

针对噪声背景下经典Prony算法对多正弦信号频率估计受限的问题,提出一种抗噪性强的改进Prony算法。通过建立一种新的累积关系,提出的算法可以在低信噪比情况下,仅利用适当数量的新序列值线性重构稀疏和,从而较为精确地估计出信号的频率。对提出的算法与经典Prony算法的性能作出仿真实验对比。实验结果表明,在信号中嵌入噪声时,经典Prony算法在估计信号频率时失去效用,而新算法依旧可以有效估计信号频率。所以,提出的算法抗噪能力较强,性能相对稳定,精度较高,在多正弦信号频率估计中表现出更好的实用性。     

A recursive technique for tracking the feasible parameter set in bounded error estimation

In this paper, a new recursive algorithm is proposed for tracking parameter changes of a time‐varying linear system. Since a bounded error approach is adopted for both modeling the measurement noise and the parameter change process, the problem addressed amounts to the design of a procedure for updating an estimate of the feasible parameter set. The approximating regions considered are in the form of outbounding orthotopes. The novelty of the approach lies in the use of a selection technique which keeps track only of a special subset of the constraints defining the feasible set. These inequalities represent the binding constraints of suitable linear programs of limited size. The devised algorithm is tested on several numerical examples, showing remarkable performance both in computational burden, which is comparable to that of classical recursive estimation algorithms like recursive least squares (RLS) and quality of the set estimate as compared to alternative techniques available in the literature.     

最优潮流算法综述

最优潮流是一个典型的非线性优化问题,且由于约束的复杂性使得其计算复杂,难度较大。虽然人们已经提出了许多种方法,并且在部分场合有所应用,但是要大规模实用化,满足电力系统的运行要求还有不少问题要解决。此文总结了现今有关最优潮流的几个方面,从优化方法和所遇到的新问题出发,对主要的优化方进行了介绍和简要的分析,以供从事无功优化的人员参考,同时还对最优潮流的进一步发展做了一些探讨。     

具有自适应性的实时睡眠信号处理算法研究

在人体睡眠实时监测中,采集到的胸腔微动信号是一种包含呼吸、心跳信息的复杂混叠信号,识别和分离该混叠信号对分析睡眠质量具有重要意义。提出一种具有自适应性的胸腔微动信号实时分离和提取算法,使用两次基于固定"筛"数量的聚类经验模态分解(EEMD)进行信号分离,结合相关性分析方法进行信号识别,利用希尔伯特-黄变换(HHT)进行信号时频分析。进行了仿真分析和实验研究,结果均表明,该算法具有很强的自适应性,能够准确、高效的完成睡眠信号的实时处理。     

电力系统频率响应的改进模型与参数估计

近年来,多种因素导致电力系统频率大波动事故时有发生。而目前对电力系统调频能力预估误差较大,有必要采用电力系统频率响应(SFR)模型计算频率的动态响应。文中分析了SFR经典模型的不足之处,据此改进了SFR模型结构,其中考虑了等值调速器的动态特性。进而提出了SFR改进模型的参数估计方法,先直接计算获得部分参数,然后加入保证稳态一致性的参数约束条件,最后辨识获得其余参数。通过仿真算例对频率响应进行了计算,验证了SFR改进模型结构与参数估计方法的有效性。结果表明,SFR改进模型能够有效表征系统频率响应的主要指标,其精度显著高于SFR经典模型。