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interval optimal power flow applied to distribution networks under uncertainty of loads and renewable resources

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Journal of Modern Power Systems and Clean Energy 2019年第1期7卷 139-150页

作者： Pengwei CHEN Xiangning XIAO Xuhui WANG North China Electric Power University Hefei University of Technology

Optimal power flow(OPF) has been used for energy dispatching in active distribution networks.To satisfy constraints fully and achieve strict operational bounds under the uncertainties from loads and sources, this paper derives an interval optimal power flow(I-OPF)method employing affine arithmetic and interval Taylor expansion.An enhanced I-OPF method based on successive linear approximation and second-order cone programming is developed to improve solution accuracy.The proposed methods are benchmarked against Monte Carlo simulation(MCS) and stochastic OPF.Tests on a modified IEEE 33-bus system and a real 113-bus distribution network validate the effectiveness and applicability of the proposed methods.

关键词： Active distribution network Optimal power flow interval uncertainty Affine arithmetic Second-order cone programming

interval Estimation for Aggregate Queries on Incomplete Data

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Journal of Computer Science & Technology 2019年第6期34卷 1203-1216页

作者： An-Zhen Zhang Jian-Zhong Li Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

Incomplete data has been a longstanding issue in the database community, and the subject is yet poorly handled by both theories and practices. One common way to cope with missing values is to complete their imputation (filling in) as a preprocessing step before analyses. Unfortunately, not a single imputation method could impute all missing values correctly in all cases. Users could hardly trust the query result on such complete data without any confidence guarantee. In this paper, we propose to directly estimate the aggregate query result on incomplete data, rather than to impute the missing values. An interval estimation, composed of the upper and the lower bound of aggregate query results among all possible interpretations of missing values, is presented to the end users. The ground-truth aggregate result is guaranteed to be among the interval. We believe that decision support applications could benefit significantly from the estimation, since they can tolerate inexact answers, as long as there are clearly defined semantics and guarantees associated with the results. Our main techniques are parameter-free and do not assume prior knowledge about the distribution and missingness mechanisms. Experimental results are consistent with the theoretical results and suggest that the estimation is invaluable to better assess the results of aggregate queries on incomplete data.

关键词： incomplete data aggregate query interval estimation data quality

interval interpolating wavelets with robust boundary filters

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Science China(Technological Sciences) 2000年第3期43卷 287-296页

作者：水鹏朗保铮 State Key Laboratory for Radar Signal Processing Xidian University Xi'an 710071 China

This paper studies the mathematical mechanism of the generalized interpolating filters and gives the corresponding parametric representations. On the basis of the parametric representations, a novel criterion and algorithm are proposed to design interval interpolating wavelets with robust boundary filters. Furthermore, some examples are designed and their performances in data compression are investigated. Both the theoretical analysis and the experimental results show that such an interval interpolating wavelet can effectively suppress the boundary effect.

关键词： generalized interpolating filter interval interpolating wavelet boundary effect robustness.

Parameter Optimization of interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 247-257页

作者： Jiajun Wang Tufan Kumbasar the School of Automation Hangzhou Dianzi University IEEE the Control and Automation Engineering Department Faculty of Electrical and Electronics EngineeringIstanbul Technical University

interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

关键词： Big bang-big crunch (BBBC) interval type-2 fuzzy neural networks (IT2FNNs) parameter optimization particle swarm optimization (PSO)

A Direct Method of interval Banzhaf Values of interval Cooperative Games

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Journal of Systems Science and Systems Engineering 2019年第3期28卷 382-391页

作者： Kairong Liang Dengfeng Li School of Economics and Management Fuzhou UniversityFuzhou 350108China School of Management and Economics University of Electronic Science and Technology of ChinaChengdu 611731China

The goal of this paper is to develop a direct method for computing interval Banzhaf values for a class of interval cooperative games.In this method,it is proven that the Banzhaf values of the associated cooperative games of interval cooperative games are monotonic and non-decreasing functions of coalitions,payoffs under weaker coalition monotonicity-like conditions.So the interval Banzhaf values can be explicitly obtained through using only the lower and upper bounds of the coalitions'interval payoffs,respectively.The proposed method does not employ interval subtractions and hereby can effectively avoid the irrational issues such as irreversibility and uncertainty enlargement.We prove some important and desirable properties of the interval Banzhaf values and illustrate the applicability and validity of the proposed method with a numerical example.

关键词： interval cooperative game Banzhaf value group decision cooperative game

interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

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Journal of Systems Engineering and Electronics 2015年第1期26卷 96-102页

作者： Naiming Xie Sifeng Liu College of Economics and Management Nanjing University of Aeronautics and Astronautics Institute of Grey System Studies Nanjing University of Aeronautics and Astronautics

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

关键词： grey number grey system theory interval discrete grey forecasting model non-homogeneous exponential sequence

Multi-objective interval prediction of wind power based on conditional copula function

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Journal of Modern Power Systems and Clean Energy 2019年第4期7卷 802-812页

作者： Gang ZHANG Zhixuan LI Kaoshe ZHANG Lei ZHANG Xia HUA Yongqing WANG Institute of Water Resources and Hydro-Electric Engineering Xi’an University of TechnologyXi’an 710048China State Grid Shaanxi Baoji Electric Power Company Baoji 721000China State Grid Gansu Electric Power Company Gansu Electric Power Research InstituteLanzhou 730050China State Grid Shaanxi Electric Power Company Shaanxi Electric Power Research InstituteXi’an 710054China

interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind power.However,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction accuracy.Therefore,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical examples.Finally,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.

关键词： Wind power prediction interval prediction Conditional copula function Empirical distribution function Multi-objective optimization algorithm

A PCA-Based Approach for Structural Dynamics Model Updating with interval Uncertainty

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Acta Mechanica Solida Sinica 2019年第1期32卷 105-119页

作者： Xueqian Chen Zhanpeng Shen Xin'en Liu Institute of Systems Engineering China Academy of Engineering Physics(CAEP)Mianyang 621999China Shock and Vibration of Engineering Materials and Structures Key Laboratory of Sichuan Province Mianyang 621999China

Uncertainty widely exists in engineering structures,making it necessary to update the finite element(FE)modeling with uncertainty.An updating approach for the FE model of structural dynamics with interval uncertain parameters is proposed in this work.Firstly,the correlations between the updating parameters and the output qualities of interest are uncorrelated by the principal component analysis(PCA),and the massive samples of major principal components are obtained using the Monte Carlo simulation.Then,the massive samples of updating parameters and output qualities are obtained by reversing transformation in the PCA,and the new samples of updating parameters are re-chosen by combining the massive samples with the experimental intervals of the qualities of interest.Next,the 95%CI of the updating parameters is estimated by the nonparametrie kernel density estimation approach,which are regarded as the intervals of updating parameters.Lastly,the proposed approach is validated in simple rectangular plates and the transport mirror system.The updating results evidently demonstrate the feasibility and reliability of the proposed approach.

关键词： PCA-based approach interval uncertainty Model updating Kernel density estimation

Sealing reliability modeling of aviation seal based on interval uncertainty method and multidimensional response surface

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Chinese Journal of Aeronautics 2019年第9期32卷 2188-2198页

作者： Baopeng LIAO Bo SUN Yu LI Meichen YAN Yi REN Qiang FENG Dezhen YANG Kun ZHOU School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China Southwest Technology and Engineering Research Institute Chongqing 400039China

Many variables affect the sealing performance, and their distribution characteristics are difficult to obtain with probabilistic methods owing to the high cost involved. Numerous problems in engineering are similar due to the appearance of small-sample parameters. In this study, the sealing reliability of an aviation seal was defined as the research object, and an interval uncertainty method and multidimensional response surface were proposed to calculate the sealing reliability.Based on this, we first analyzed the failure mechanism of the aviation seal and established a leakage rate model. Then, based on the non-probabilistic interval model, an interval uncertainty method was proposed to construct the analytical model. With reference to the limit state equation from the structural reliability theory, the multidimensional response surface was used for fast calculation.Then, we chose the single-cylinder gas steering gear used in aircraft as the case study, its sealing reliability in working and non-working statuses were calculated, and the results were verified with the actual maintenance records. By analyzing the sensitivity of some variables, we can improve the sealing reliability of the aviation seal by improving the surface roughness only if the cost allows.Finally, we consider that the method proposed in this study realizes the application of smallsample uncertainty analysis in reliability analysis, and could provide a feasible way to solve the similar problems in engineering with multidimensional and small-sample parameters.

关键词： Aviation seal interval uncertainty method Multidimensional response surface Sealing reliability Small-sample parameters