Bayesian estimation of dynamic finite mixtures

The paper introduces an algorithm for estimation of dynamic mixture models. A new feature of the proposed algorithm is the ability to consider a dynamic form not only for component models but also for the pointer model, which describes the activities of the mixture components in time. The pointer model is represented by a table of transition probabilities that stochastically control the switching between the active components in dependence on the last active one. This feature brings the mixture model closer to real multi‐modal systems. It can also serve for a prediction of the future behavior of the modeled system. Copyright © 2011 John Wiley & Sons, Ltd.     

Mixed‐data multi‐modelling for fault detection and isolation

Early recognition/isolation of a faulty behaviour of a dynamic system is the main task of a fault detection and isolation (FDI). FDI methods based on adaptive probabilistic models with multiple modes represent a theoretically well justified way of solution. Their use is severely restricted by an inherent computational complexity. The complexity problem is addressed here by employing an efficient quasi‐Bayes estimation algorithm. It is directly applicable to the mixture of components created as products of factors belonging to the exponential family. It opens a novel way to deal adaptively with mixed continuous–discrete, dynamically related data. The presented theory and algorithmization are illustrated by a simple simulation example. Copyright © 2001 John Wiley & Sons, Ltd.     

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.     

新技术对发输电系统元件可靠性模型的影响研究综述

随着电网建设的不断完善,发输电新技术的不断涌现,对电力系统可靠性评估的准确性和全面性提出了新的要求。从发输电系统可靠性元件的建模入手,分析了发输电元件模型多态化、计及因素多样化的发展趋势;重点讨论了包括特高压直流输电、柔性直流输电、计及储能装置的风力发电等新型发输电设备,以及FACTS元件、电动汽车在内的元件概率可靠性模型的建立方法、对发输电系统可靠性的影响和存在的不足,最后论述了需要进一步深化研究的方向。     

Estimation and prediction with ARMMAX model: a mixture of ARMAX models with common ARX part

Bayesian parameter estimation and prediction of a linear‐in‐parameters model with coloured noise is addressed, based on a novel mixture model called ARMMAX. ARMMAX is a finite mixture with its ARMAX components having a common ARX part. It assumes that the common ARX part describes a fixed deterministic input–output relationship and allows for varying characteristics of the driving coloured noise. The ARMMAX model with fixed MA parts is estimated by a specific version of recursive Quasi‐Bayes (ARMMAX‐QB) algorithm. It rests on a classical Bayesian solution that requires no restrictions on MA part allowing it to be even at the stability boundary. For on‐line use, the ARMMAX model offers flexibility with respect to varying characteristics of the model noise. The flexibility gained is paid by a slight increase of the computational burden when compared with the single ARMAX with known MA part, which is, in this respect, close to recursive least‐squares estimation. For off‐line use, the ARMMAX model offers the possibility to estimate the unknown MA part in a novel way. Exploiting the natural parallelism of the ARMMAX model, a robust, derivative free multi‐directional search (MDS) is selected to deal with extensive data sets for which universal optimization tools are too cumbersome. The paper motivates the modelling approach, describes algorithmic ingredients and illustrates the resulting algorithm using a simple example. Copyright © 2003 John Wiley & Sons, Ltd.     

Recursive approximation by ARX model: A tool for grey box modelling

The presented procedure computes approximate probabilistic models of complex dynamic phenomena recursively with respect to an increasing amount of observed evidence. Measured, fictitious as well as simulated data can be used in combination for obtaining a reasonably conservative approximate model. Thus information from a number of sources can be systematically merged using a refinement of the recently proposed method of Bayesian pooling of imprecise opinions from a variety of experts. It can be applied recursively as the number of treated items grows. The procedure provides (i) a new tool needed for grey as well as black box modelling, (ii) a novel adaptation of probabilistic models and (iii) an approximation of a given model by a simpler one. The general procedure is applied to the autoregressive model with exogenous variables (AM). This example illustrates the adopted approach and conmbutes to the solution of the following tasks: (i) estimation of an appropriate model structure; (ii) incorporation of prior knowledge into the initial conditions of recursive least squares; (iii) construction of a reference for an advanced forgetting technique; (iv) approximation of a complex analytic/simulation model by an ARX model. The behaviour of the procedure is illustrated on typical examples.     

Local Discontinuous Galerkin Methods for Moment Models in Device Simulations: Formulation and One Dimensional Results

We report our preliminary work in applying the local discontinuous Galerkin (LDG) finite element method to solve time dependent and steady state moment models, such as the hydrodynamic (HD) models and the energy transport (ET) models, for semiconductor device simulations, in which both the first derivative convection terms and second derivative diffusion (heat conduction) terms exist and are discretized by the discontinuous Galerkin (DG) method and the LDG method respectively. The potential equation for the electric field is also discretized by the LDG method, thus the numerical tool is based on a unified discontinuous Galerkin methodology for different components and is hence potentially viable for efficient h-p adaptivity and parallel implementation. One dimensional n⁺-n-n⁺ diode is simulated in this paper using the HD and ET models and comparison is made with earlier finite difference Essentially Non-Oscillatory (ENO) simulation results.     

电力系统负荷建模和算法的研究及进展

为了给研究人员提供负荷建模研究及在电力系统分析中具有的重要意义较全面的认识,分析了现有的负荷建模方法,系统地阐述了负荷模型及算法上的进展,它包括在静态负荷方面通过采用连续动态方法,根据负荷电压的变化实时修正负荷模型参数,在临界状态下通过分段多项式模型描述加以改进;在负荷建模方面分析了负荷动特性描述、负荷时变性、模型参数的分散性、负荷模型无功分析和负荷的非线性、变结构特性方面问题解决的进展。结果表明,通过采用上述解决方法,使得负荷模型的精确性和适应性得到了较大程度的提高,从而满足复杂大电力系统的动态行为和安全稳定分析的要求。     

风载作用下输电铁塔的动力响应

介绍1种评估风载作用下耦合输电线路铁塔动态特性的方法.为此,建立了输电塔线体系在风载作用下动力响应的评估标准,并利用有限元模拟进行分析,结果表明,该能量评估标准能有效地检测结构动态特性.模拟结果表明,能量标准对大规模输电铁塔在强风载作用下的性能评估是非常有效的.     

Load modeling at electric power distribution substations using dynamic load parameters estimation

This paper is focused on electric power distribution substations load modeling using dynamic load parameters estimation. The load parameters are estimated using two models: the exponential and the ZIP load models. Since the load bus voltage and parameters are known one can determine the active and reactive power injections of this bus and include these pseudo-measurements in the state estimation in order to improve observability and estimation accuracy. The dynamic parameter estimation is developed using the weighted least squares method in a recursive form and the tests are carried out based on actual measurements. It is shown that the estimated parameters (for both load models) at a distribution substation are valid, since the obtained active and reactive power residuals are very close to zero.     

基于物理背景的微网总体模型

微网是集分布式电源、负荷和储能设备的小型电网,通过公共耦合点与大电网相连,与大电网之间的功率可双向流动,需对微网进行建模以研究微网接入。从微网基本元件出发,分析了微网负荷、电源元件模型与传统电力系统元件模型之间的联系,借鉴负荷建模理论,提出了微网整体建模的思路,将微网中静态负荷与外部表现为静态特性的电源等效为等效静态模型,推荐采用幂函数模型;将风力发电机、电动机等动态元件等效为等效电机模型,并从理论上推导了等效电机的通用模型。     

Lecture speech recognition using discrete‐mixture HMMs

Most of the state‐of‐the‐art speech recognition systems use continuous‐mixture hidden Markov models (CMHMMs) as acoustic models. On the other hand, it is well known that discrete hidden Markov model (DHMM) systems show poor performance because they are affected by quantization distortion. In this paper, we present an efficient acoustic modeling based on discrete distribution for large‐vocabulary continuous speech recognition (LVCSR). In our previous work, we proposed the maximum a posteriori (MAP) estimation of discrete‐mixture hidden Markov model (DMHMM) parameters and showed that the DMHMM system performed better in noisy conditions than the conventional CMHMM system. However, we conducted the recognition experiments on a read/speech task in which the vocabulary size was only 5k. In addition, the DMHMM was not effective in clean condition in that work. In this paper, we have developed a DMHMM‐based LVCSR system and evaluated the system on a more difficult task under clean condition. In Japan, a large‐scale spontaneous speech database ‘Corpus of Spontaneous Japanese’ has been used as the common evaluation database for spontaneous speech and we used it for our experiments. From the results, it was seen that the DMHMM system showed almost the same performance as the CMHMM system. Moreover, performance improvement could be achieved by a histogram equalization method. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

线性连续系统动态负荷模型的参数辨识与应用

目前在电力负荷建模中非机理负荷模型较多地采用离散系统模型或差分方程形式的模型,而电力系统数字仿真中更需要连续系统的模型。研究了作用函数不含导数项和含有导数项两种情况下的线性连续系统微分方程形式的负荷建模方法,介绍了模型参数辨识的直接优化算法,该模型对于描述线性动态负荷或小扰动下的非线性动态负荷的行为,具有一定程度的普适性。介绍了该动态负荷模型与电力系统分析综合程序PSASP的自定义连接方法。通过动模实验数据建模和仿真计算,验证了模型参数辨识算法以及模型与电力系统分析综合程序接口的有效性。     

Statistical Representation of Distribution System Loads Using Gaussian Mixture Model

This paper presents a probabilistic approach for statistical modeling of the loads in distribution networks. In a distribution network, the probability density functions (pdfs) of loads at different buses show a number of variations and cannot be represented by any specific distribution. The approach presented in this paper represents all the load pdfs through Gaussian mixture model (GMM). The expectation maximization (EM) algorithm is used to obtain the parameters of the mixture components. The performance of the method is demonstrated on a 95-bus generic distribution network model.      

低功耗设备常用储能元件的Peukert模型适用性分析

Peukert模型已被广泛用于铅酸电池、锂电池等储能元件的状态估计。然而,现有研究缺乏对低功耗设备常用储能元件和小电流工况的考虑,并且忽略了储能元件之间的横向对比分析。基于此,在小电流工况下,通过恒阻放电实验拟合建立了超级电容、碱性电池和锂亚电池的Peukert模型,验证并对比分析了Peukert模型对这三类储能元件的适用性。实验结果表明,在目标工况下,Peukert模型对这三类储能元件均适用,其平均相对误差分别为5.891%、2.898%和2.931%,Peukert模型用于碱性电池和锂亚电池状态估计的准确性比超级电容高。     

基于阿基米德Copula和拉丁超立方采样的概率最优潮流计算

提出一种考虑随机变量相关性的概率最优潮流算法。选用广义lambda分布拟合最优潮流模型中的随机变量,建立逆累积分布函数;基于Clayton、Gumbel、Frank、Joe生成元,构筑4种部分嵌套式阿基米德Copula模型对随机变量的相关性结构建模;选取Kendall秩相关系数描述随机变量的相关性,采用相关系数匹配法求取Copula模型的参数;基于生成元的拉普拉斯逆变换,将阿基米德Copula与拉丁超立方采样相结合,生成相关的随机样本用于概率最优潮流计算。对某地区10个风电场风速样本的建模和分析,验证了广义lambda分布和部分嵌套式阿基米德Copula模型的有效性。基于IEEE 118节点系统对2种拉丁超立方采样法进行了对比。     

基于正交级数的光伏电源输出功率概率模型

目前光伏输出功率概率建模中,参数分析方法需预先假设参数分布,非参数分析方法中常用的核密度估计的带宽值选取方法不统一,在此情况下提出一种非参数方法——正交级数密度估计,对数据分布不附加任何假设,基于正交级数理论直接建立光伏电源输出功率的概率模型。利用江西南昌及浙江嘉兴两地的光伏电源输出功率历史实测数据进行仿真,结合拟合优度检验和误差分析,验证了所提模型的准确性和有效性。对于不同时段、不同地区光伏电源输出功率的随机特性,所提方法具有良好的适用性,在样本容量变化时模型保持稳定性。     

Extension of the tuning constant in the Huber's function for robust modeling of piezoelectric systems

This paper proposes a new modeling approach that is experimentally validated on piezoelectric systems in order to provide a black‐box pseudolinear model for complex systems control. Most of the time, one uses physical based approaches. However, sometimes complex phenomena occur in the system due to atypical changes of the process behavior, output noise or some hard nonlinearities. Therefore, we adopt identification methods to achieve the modeling task. The microdisplacements of the piezoelectric systems generate atypical data named outliers, leading to large estimated prediction errors. Since these errors disturb the classical normal probability density function, we choose here, as corrupted distribution model, the gross error model (GEM). In order to deal more efficiently with the outliers, we use the Huber's function, as mixed L₂/L₁ norms in which the tuning threshold named scaling factor is extended. From this function, a cost function also named PREC as parameterized robust estimation criterion is established. The identification is performed by choosing an Output Error model structure. In order to express the asymptotic covariance matrix of the robust estimator, we present a L finite Taylor's expansion to linearize the gradient and the hessian of the PREC. Experimental results are presented and discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

大型风电场建模综述

风电场建模是研究风电场接入电网运行和控制的基础。随着我国千万千瓦级风电场陆续投入电网运行,建立描述大型风电场的稳态和动态行为的数学模型是一个关键课题。对现有风电场建模研究成果从风速—功率关系模型、稳态潮流计算模型、动态模型和暂态模型四方面进行分析和总结;并针对近期出现的大型风电场连锁切机事故,提出了风电场连锁动态过程建模的概念和必要性。通过对现有风电场建模方法的综述,旨在指出目前大规模风电场建模存在的问题和需要进一步研究的方向。