敏感设备电压暂降故障水平的多不确定性评估

敏感设备电压暂降故障水平评估的最大困难在于各影响因素的不确定性和评估建模。根据相关因素的内涵和外延不确定特征,对电压暂降强度、设备电压耐受能力、设备故障状态的不确定性进行研究,建立相应的数学模型。在此基础上,基于模糊安全事件隶属函数的建立原则与模糊事件概率计算模型,构建能同时反映各影响因素不确定性的敏感设备电压暂降故障水平评估模型,并对相应算法和评估过程进行较详细的研究。对个人计算机进行评估,结果证明该方法的正确性和可行性,该方法不需专家经验或主观假设,能客观地反映实际情况,并可方便地推广应用于相关领域。

计及智能电网多不确定性的多目标储能优化

针对风力发电与电动汽车大规模地接入电网后电力系统的随机性特性愈发明显的问题,综合考虑风力发电、负荷与电动汽车的不确定性,建立了智能电网多不确定模型,并利用点估计法求解随机潮流,构造了以运行成本最小、供电可靠性最大及储能投资最小为目标函数的储能优化模型,之后利用引入非线性递减惯性权重的改进粒子群算法,在随机潮流结果的基础上对储能装置24 h的出力进行寻优计算,得出最优储能调度方案,从而抑制系统的不确定性。最后进行了算例分析,结果表明两点估计法能有效求解包含多不确定性的潮流方程,验证了所提方案的合理性。

多不确定性战术导弹鲁棒控制器设计研究

研究了包含多种不确定性的战术导弹鲁棒控制器设计问题;首先介绍了μ分析与μ综合理论,给出了通过D-K迭代设计鲁棒控制器的方法;进而通过分析系统中存在的多种不确定性及其对受控系统的影响、建立包含多种不确定的被控对象、引入合适的评价权函,得到了扩展的广义受控系统,利用Matlab鲁棒控制工具箱设计了战术导弹俯仰通道鲁棒控制器;最后通过数值仿真,验证了设计的鲁棒控制器满足系统设计的性能指标要求,并且对系统中存在的多种不确定性和扰动具有较强的鲁棒性。

多不确定性信息的多目标电网规划研究

随着社会发展对电力系统要求的不断提高。目前,我国电网规划已经由传统的单一结构逐步发展为多目标、多不确定性信息的多重结构。虽然这在很大程度上促进我国社会经济的发展,但是也给电网规划人员带来了巨大的挑战。本文主要从多目标、多不确定性信息的角度出发,在建立弹性电网规划模型的基础上,探索电网规划综合评价指标体系和决策方法,以此来为今后电网规划工作提供一定的参考依据。

Stability analysis of time-varying systems via parameter-dependent homogeneous Lyapunov functions

This paper considers the stability analysis of linear continuous-time systems, and that the dynamic matrices are affected by uncertain time-varying parameters, which are assumed to be bounded, continuously differentiable, with bounded rates of variation. First, sufficient conditions of stability for time-varying systems are given by the commonly used parameter-dependent quadratic Lyapunov function. Moreover, the use of homogeneous polynomial Lyapunov functions for the stability analysis of the linear system subject to the time-varying parametric uncertainty is introduced. Sufficient conditions to determine the sought after Lyapunov function is derived via a suitable paramenterization of polynomial homogeneous forms. A numerical example is given to illustrate that the stability conditions are less conservative than similar tests in the literature.

Relationship of Uncertainty Between Polygon Segment and Line Segment for Spatial Data in GIS

The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.

Uncertain Modal Analysis of Unmanned Aircraft Composite Landing Gear

Through taking uncertain mechanical parameters of composites into consideration,this paper carries out uncertain modal analysis for an unmanned aircraft landing gear.By describing correlated multi-dimensional mechanical parameters as a convex polyhedral model,the modal analysis problem of a composite landing gear is transferred into a linear fractional programming(LFR)eigenvalue solution problem.As a consequent,the extreme-point algorithm is proposed to estimate lower and upper bounds of eigenvalues,namely the exact results of eigenvalues can be easily obtained at the extreme-point locations of the convex polyhedral model.The simulation results show that the proposed model and algorithm can play an important role in the eigenvalue solution problem and possess valuable engineering significance.It will be a powerful and effective tool for further vibration analysis for the landing gear.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Strong/weak limits and strong/weak differentials of multi-variable indeterminate forms. II: Supplementary annotations and analysis

Supplementary annotations on special forms 1to 4, discussion on the general characteristics of K(t) and K(t, t), and analyses on two noticeable limits are presented in this part. It is demonstrated that strong and weak parabolic transforms can be employed to change the standard form of a multi-variable indeterminate form into xmK type, hence to derive the standard formulae of the limit and the differential.

Risk reduction in Sechahun iron ore deposit by geological boundary modification using multiple indicator Kriging

Uncertainty on the geological contacts and the block volumes of the models along boundaries is often a major part of the global uncertainty of reserve estimation.This work introduces a geostatistical technique that has been developed and tested in an iron ore deposit at Bafq mining district,in central Iran,and that,based on a probability criterion,helps to objectively model the geometry of this iron ore deposit.The main problem in reserve estimation of this ore body is its geometrical modeling and uncertainty in geological boundaries.This work deals with the geostatistical method of multiple indicator kriging,which is used to determine the real boundaries of ore body in different categories.This approach has potential to improve project performance and decrease operational risk.For this purpose,the ore body is separated into two categories including rich iron zone(w(Fe)>45%)and poor iron zone(20%<w(Fe)<45%).It significantly benefits to decrease the risk of reserve evaluation in the deposit.This case study also highlights the value of multiple indicator kriging as a tool for estimates the position of grade boundaries within the deposit.Comparison of the resultant probability maps with the real ore/waste contacts on the extracted levels shows that the first indicator model could separate the whole ore body(poor plus rich)from the waste zone by probability of more than 0.35,which concludes the total reserve of 53 million tons.The second indicator model applied to separate the rich and poor domains and the results show that the blocks with the estimated probability of equal to or more than 0.4 lay within the rich ore zone consisting of 15.8 million tons reserve.

On multistage flexible comprehensive evaluation of complicated system

The paper analyzed characters of complicated system and discussed the reason of comprehensive evaluation, realization of flexible comprehensive evaluation was researched from prospect of dynamic measure selection of evaluation, balance of functionality and harmony, uncertainty factor. In the end, multistage flexible comprehensive evaluation of complicated system was applied to performance evaluation of firm.

Uncertainty through Polynomial Chaos： A Sensor Sensitivity and Correlation Analysis in EEG Problems

The author studies the effect of uncertain conductivity on the electroencephalography （EEG） forward problem. A three-layer spherical head model with different and random layer conductivities is considered. Polynomial Chaos （PC） is used to model the randomness. The author performs a sensitivity and correlation analysis of EEG sensors influenced by uncertain conductivity. The author addressed the sensitivity analysis at three stages： dipole location and moment averaged out, only the dipole moment averaged out, and both fixed. On average, the author observes the least influenced electrodes along the great longitudinal fissure. Also, sensors located closer to a dipole source, are of greater influence to a change in conductivity. The highly influenced sensors were on average located temporal. This was also the case in the correlation analysis. Sensors in the temporal parts of the brain are highly correlated. Whereas the sensors in the occipital and lower frontal region, though they are close together, are not so highly correlated as in the temporal regions. This study clearly shows that intrinsic sensor correlation exists, and therefore cannot be discarded, especially in the inverse problem. In the latter it makes it possible not to specify the conductivities. It also offers an easy but rigorous modeling of the stochastic propagation of uncertain conductivity to sensorial potentials （e.g., making it suited for research on optimal placing of these sensors）.

Improving the simulation of terrestrial water storage anomalies over China using a Bayesian model averaging ensemble approach

The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.

RANDOM SETS: A UNIFIED FRAMEWORK FOR MULTISOURCE INFORMATION FUSION

The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and so on. However,the information is modeled and fused traditionally in particular,name some of the known theories: evidential,fuzzy sets,possibilistic,rough sets or conditional events,etc. For several years,researchers have explored the unification of theories enabling the fusion of multisource information and have finally considered random set theory as a powerful mathematical tool. This paper attempts to overall review the close relationships between random set theory and other theories,and introduce recent research results which present how different types of information can be dealt with in this unified framework. Finally,some possible future directions are discussed.

Impact of Departure Time Uncertainty on Runway Scheduling

The flight departure process is affected by various uncertain factors,such as flight delays,scheduling delays and taxi time etc. A reliable and robust departure sequence is very important to the safe and efficient operation for airports. An optimal scheduling model for multi-runway departure considering the arrival aircraft crossing departure runway is developed. A genetic algorithm encoding flight numbers is designed to find a near-optimal solution. After that,further establish a multi-objective dynamic scheduling model and design a hybrid algorithm to solve it,and compare and analyze the results of the two models. A quantitative analysis of departure time based on the kernel density estimation is performed,and Monte Carlo simulations are carried out to explore the impact of flight departure time’s uncertainty on departure scheduling. The results based on historical data from Guangzhou Baiyun Airport are presented,showing the advantage of the proposed model and algorithm.

Robust Stability Analysis of a Kind of Combined Integrating Control System

This paper offeres an exact study on the robust stability of a kind of combined integrating control system, and the robust stability belongs to the analysis of a kind of quasi-polynomial with two independent time delays. The parameters of stable space under time delay uncertainty are fixed after Rekasius transformation, and then a new cluster treatment of characteristic roots (CTCR) procedure is adopted to determine the stable space. By this strategy we find that the unstable space is not continuous and both Karitonov vertices theory and Edge theory are unable to be extended to quasi-polynomial under time delay uncertainty.

From Digital Analogs Through Recursive Machines to Quantum Computer

The report examines the evolution of computers from digital analogs through non-yon Neumann machines to quantum computers, which are also digital analogs. In the 60 years of digital analogs successfully developed at the Institute of Electromechanics of the USSR in Leningrad. An important stage in the development of non-classical multiprocessor machine performance and reliability has been the development of recursive machines, which was carried out at the Institute of Cybernetics led V.M.Glushkov and the Leningrad Institute of Aviation Instrumentation. The general approach to the synthesis is carried out through linguo- combinatorial modeling with structured uncertainty.

Leader-Following Consensus for Discrete-Time Multi-Agent Systems with Parameter Uncertainties Based on the Event-Triggered Strategy

In this paper, the leader-following consensus for discrete-time nmlti-agent systems with parameter uncertainties is investigated based on the event-triggered strategy. And the parameter un- certainty is assmned to be norm-bounded. A consensus protocol is designed based on the event-triggered strategy to make the multi-agent systems achieve consensus without continuous communication among agents. Each agent only needs to observe its own state to determine its own triggering instants under the triggering function in this paper. In addition, a sufficient condition for the existence of the event- triggered consensus protocol is derived and presented in terms of the linear matrix inequality. Finally, a numerical example is given to illustrate to efficiency of the event-triggered consensus protocol proposed in this paper.

ROBUST H_∞ FILTERING FOR UNCERTAIN DISCRETE-TIME STATE-DELAYED SYSTEMS: A PARAMETER-DEPENDENT LYAPUNOV APPROACH

This paper is concerned with the problem of robust H∞ filtering for linear discrete-time systems with multiple state delays and polytopic uncertain parameters. Attention is focused on the design of full-order, reduced-order and zeroth-order robust H∞ filters on the basis of a recently published parameter-dependent Lyapunov stability result. Sufficient conditions for the existence of such filters are formulated in terms of linear matrix inequalities, upon which admissible filters can be obtained from convex optimization problems. The proposed methodology has been shown, via a numerical example, to be much less conservative than previous filter design methods in the quadratic framework.

A novel method of Newton iteration-based interval analysis for multidisciplinary systems

A Newton iteration-based interval uncertainty analysis method(NI-IUAM) is proposed to analyze the propagating effect of interval uncertainty in multidisciplinary systems. NI-IUAM decomposes one multidisciplinary system into single disciplines and utilizes a Newton iteration equation to obtain the upper and lower bounds of coupled state variables at each iterative step.NI-IUAM only needs to determine the bounds of uncertain parameters and does not require specific distribution formats. In this way, NI-IUAM may greatly reduce the necessity for raw data. In addition, NI-IUAM can accelerate the convergence process as a result of the super-linear convergence of Newton iteration. The applicability of the proposed method is discussed, in particular that solutions obtained in each discipline must be compatible in multidisciplinary systems. The validity and efficiency of NI-IUAM is demonstrated by both numerical and engineering examples.