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Optimal Topology Control With Physical Power Flow Constraints and N-1 Contingency Criterion

IEEE TRANSACTIONS ON POWER SYSTEMS 2015年第6期30卷 3063-3071页

作者： Poyrazoglu, Gokturk Oh, HyungSeon SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

In this paper, a novel solution method is proposed for the optimal topology control problem in AC framework with N-1 reliability. The resultant new topology of the proposed method is guaranteed to yield a better objective function value than the global solution of the problem with the original topology. In order to prove our statement, semi-definite programming relaxation is formulated to find a lower bound to the objective function value of the problem. The proposed method is favorable for parallelization to increase the computational efficiency, and the parallel computing flowchart is presented. Computational time of the algorithm for IEEE test cases is reported. An in-house power market simulator is developed to simulate a market environment complete with optimal topology control mechanism. Simulations held by participating human subjects are analyzed under the subtitles of the total operating cost, real power losses, and locational marginal price (LMP) variance. A simple example is illustrated to show that the resultant network topology may increase the real power losses while still providing a lower operating system cost than that of the original topology.

关键词： AC optimal power flow locational marginal pricing optimal topology control semi-definite programming transmission switching

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Adaptive Constraint Propagation for semi-Supervised Kernel Matrix Learning

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NEURAL PROCESSING LETTERS 2015年第1期41卷 107-123页

作者： Jian, Meng Jung, Cheolkon Shen, Yanbo Jiao, Licheng Liu, Juan Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

We propose adaptive constraint propagation (ACP) for semi-supervised kernel matrix learning (SS-KML). SS-KML aims to learn a kernel matrix from the given samples which contains just a little supervised information such as class labels or pairwise constraints. Recently, for effective SS-KML, constraint propagation methods by semi-definite programming are being actively studied, and the representative works are pairwise constraint propagation (PCP) and kernel propagation (KP). They have used hard constraints in their frameworks for constraint propagation and achieved outstanding classification performance. However, a small set of the hard constraints sometimes cannot cover all the samples of the full data set, and thus lead to large distortions of global discriminative data structure in the learned kernel matrix. It has a negative influence on the classification performance. To deal with this problem, we provide two adaptive fidelity terms to satisfy the requirement that two must-link samples become close and two cannot-link samples become far apart. Then, we build a new framework based on them to adaptively propagate the constraints. Experimental results demonstrate that ACP outperforms state-of-the-art SS-KML methods such as PCP and KP in terms of both effectiveness and efficiency.

关键词： Adaptive constraint propagation Pairwise constraint semi-definite programming semi-supervised kernel matrix learning semi-supervised classification

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Limiting the parameter search space for dynamic models with rational kinetics using semi-definite programming

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IFAC Proceedings Volumes 2010年第6期43卷 150-155页

作者： Dirk Fey Eric Bullinger Systems and Modeling Research Unit Institute Montefiore University of Liège Belgium

Estimation of kinetic parameters is a key step in modelling, as direct measurements are often expensive, time-consuming or even infeasible. The class of dynamic models in polynomial form is particularly relevant in systems biology and biochemical engineering, as those models naturally arise from modelling biochemical reactions using for instance mass action, Michaelis-Menten or Hill kinetics. Often the parameters are not uniquely identifiable for a given model structure and measurement set. Thus the question of which parameters are consistent or inconsistent with the data arises naturally. Here we present a method capable of proving inconsistency of entire parameter regions with the data. Based on the polynomial representation of the system, we formulate a feasibility problem that can be solved efficiently by semi-definite programming. The feasibility problem allows us to check consistency of entire parameter regions by using upper and lower bounds on the parameters. This drastically limits the search space for subsequent parameter estimation methods. In contrast to similar approaches in the literature, the here presented approach does not require a steady state assumption. Measurements at discrete time points are used, but neither regular sampling intervals, nor a time discretisation of the system is required. Measurement uncertainties are dealt with using upper and lower bounds on the measured states.

关键词： identification parameter estimation model invalidation sum of squares semi-definite programming

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Nonsmooth Optimization for Optimal Power Flow over Transmission Networks

Nonsmooth Optimization for Optimal Power Flow over Transmiss...

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3rd IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Shi, Y. Tuan, H. D. Su, S. W. Tam, H. H. M. Univ Technol Fac Engn & Informat Technol Sydney NSW Australia

ISBN: (纸本)9781479975914

The optimal power flow (OPF) of a power transmission network is a NP-hard optimization problem with nonlinear equality and inequality constraints on the bus voltages. The existing nonlinear solvers often fail in yielding a feasible solution. semi-definite relaxation (SDR) could provide an optimal solution only when the optimal solution of the relaxed semi-definite program (SDP) is of rank-one, which does not hold in general. Otherwise, the solution found by SDR is infeasible. Very recently, high-order semi-definite relaxation has been used to find the global optimal solution but such approach leads to an explosive growth of the variable dimension and so could be applied to test OPF with very small networks with 2, 3 and 5 buses, where there are only 2, 3 and 5 voltages variables. In this paper, we adapt our previously developed nonsmooth optimization algorithm to address this difficult OPF problem, which is an iterative process to generate a sequence of improved solution that converges to an optimal solution. Each its iteration calls a SDP of a moderate dimension. Preliminary simulations for difficult OPF problems of networks with a large number of buses are provided to show the efficiency of our approach.

关键词： Optimal Power Flow Transmission Networks semi-definite programming Rank-one Constraint Nonsmooth Optimization

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Estimation of Uncertain ARX models with Ellipsoidal Parameter Variability

Estimation of Uncertain ARX models with Ellipsoidal Paramete...

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European Control Conference (ECC)

作者： Mohammadi, Adeleh Diehl, Moritz Zanon, Mario

ISBN: (纸本)9783952426937

Traditional parameter estimation techniques deliver estimates for a given set of parameters, but do not in general provide an estimate of the parameter variability for systems with time-varying parameters. Accurate knowledge of the parameter variability becomes crucial in many contexts, e.g. robust control techniques. This paper proposes a method for joint parameter and variability estimation (PVE) which is based on optimizing a convex cost function subject to a linear matrix inequality (LMI) constraint. The method is described and compared to the Least Squares (LSQ) method for a linear AutoRegressive eXogenous (ARX) model with ellipsoidal parameter variability. The two techniques have been tested numerically in a simulation scenario. Simulation results indicate that PVE is more precise at characterizing the variability of estimated parameters.

关键词： Parameter Estimation Least Squares Method semi-definite programming Linear ARX model Variability

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Atomic-Norm for Joint Data Recovery and Narrow-Band Interference Mitigation in OFDM Systems

Atomic-Norm for Joint Data Recovery and Narrow-Band Interfer...

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IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications

作者： Hanan Al-Tous Imad Barhumi Naofal Al-Dhahir College of Engineering UAE University Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas

ISBN: (纸本)9781509032556

In this paper, a novel approach is proposed to jointly recover the transmitted signal and mitigate narrow-band interference (NBI) in OFDM systems using a compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as sparse atoms and then to solve a joint compressive sensing (JCS) problem. The recovery problem is formulated as a weighted optimization problem of two atomic norms and then solved using convex programming. The solution aims to recover the transmitted signal and NBI jointly. The off-grid problem of NBI signal is avoided using the atomic norm. The bit error rate (BER) performance of our proposed JCS recovery approach outperforms the BER performance of the conventional CS-based approach as demonstrated by simulation results.

关键词： OFDM NBI Atomic norm Compressive sensing semi-definite programming

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semi-definite programming-based method for security-constrained unit commitment with operational and optimal power flow constraints

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IET GENERATION TRANSMISSION & DISTRIBUTION 2009年第2期3卷 182-197页

作者： Bai, X. Wei, H. Guangxi Univ Coll Elect Engn Nanning Guangxi Peoples R China

Considering the economics and securities for the operation of a power system, a semi-definite programming (SDP) model for the security-constrained unit commitment (SCUC) problem is described here, which is directly solved by the interior-point method for SDP within the polynomial times. The proposed method is promising for the SCUC problems because of its excellent convergence and the ability of handling the non-covex integer variables. No model decomposition and initial relaxation are needed when applying the SDP-based method. When the solution contains minor mismatches in the integer variables, a simple rounding strategy is used to correct the non-integer into integer efficiently. Different test cases from 6 to 118 buses over a 24 h horizon are presented. Extensive numerical simulations have shown that the proposed method is capable of obtaining the optimal UC schedules without any network and bus voltage violations, and minimising the operation cost as well.

关键词： time 24 h power system operation power system security semi-definite programming optimal power flow constraints load flow power system analysis computing interior-point method security-constrained unit commitment Power engineering computing power system economics power generation scheduling Power system management, operation and economics Optimisation techniques model decomposition dynamic programming noncovex integer variables

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An efficient algorithm for linear semi-infinite programming over positive polynomials

An efficient algorithm for linear semi-infinite programming ...

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12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management (ISORA 2015)

作者： Meiling Xu Zongwei Luo South University of Science and Technology China

This paper describes an efficient implementation of a form of linear semi-infinite programming (LSIP). We look at maximizing (minimizing) a linear function over a set of constraints formed by positive trigonometric polynomials. Previous studies about LSIP are formulated using semi-definite programming (SDP), this is typically done by using the Kalman Yakubovich Popov (KYP) lemma or using a trace operation involving a Grammian matrix, which can be computationally expensive. The proposed algorithm is based on simplex method that directly solves the LSIP without any parameterization. Numerical results show that the proposed LISP algorithm is significantly more efficient than existing SDP solvers using KYP lemma and Grammian matrix, in both execution time and memory.

关键词： Linear semi-infinite programming positive trigonometric polynomials. semi-definite programming simplex method Simplex method efficient algorithm programming Polynomial Service Discovery Protocol Lemma linear function execution time

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semi-definite programming Algorithms for Sensor Network Node Localization With Uncertainties in Anchor Positions and/or Propagation Speed

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第2期57卷 752-763页

作者： Lui, Kenneth Wing Kin Ma, Wing-Kin So, H. C. Chan, Frankie Kit Wing City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China

Finding the positions of nodes in an ad hoe wireless sensor network (WSN) with the use of the incomplete and noisy distance measurements between nodes as well as anchor position information is currently an important and challenging research topic. However, most WSN localization studies have considered that the anchor positions and the signal propagation speed are perfectly known which is not a valid assumption in the underwater and underground scenarios. In this paper, semi-definite programming (SDP) algorithms are devised for node localization in the presence of these uncertainties. The corresponding Cramer-Rao lower bound (CRLB) is also produced. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional SDP method and CRLB.

关键词： Node localization range measurements semi-definite programming sensor networks

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Safe Approximations for Distributionally Robust Joint Chance Constrained Program

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2015年第1期32卷

作者： Wu, Chenchen Xu, Dachuan Zhang, Jiawei Tianjin Univ Technol Coll Sci Tianjin 300384 Peoples R China Beijing Univ Technol Dept Informat & Operat Res Beijing 100124 Peoples R China NYU Stern Sch Business New York NY USA

In this paper, we present a bilinear second-order cone programming safe approximation for the distributionally robust chance constrained program (DRCCP), assuming that the support of the uncertain parameters, and the first and second marginal moments of the probability with respect to the interval constraint imposed on the sum of the uncertain parameters are given. If we further know the covariance matrix, we can obtain a bilinear semi-definite programming safe approximation. Preliminary numerical tests indicate that the proposed models are competitive.

关键词： Second-order cone programming semi-definite programming safe approximation distributionally robust optimization chance constrained program

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