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Decomposition and Security



Mathematical decomposition is often used to address network flows. For the Science of Security community, decomposition is a useful method of dealing with cyber physical systems issues, metrics, and compositionality. The work cited here was presented in 2015.

R. M. Savola, P. Savolainen, A. Evesti, H. Abie and M. Sihvonen, “Risk-Driven Security Metrics Development for an E-Health IoT Application,” Information Security for South Africa (ISSA), 2015, Johannesburg, 2015, pp. 1-6. doi: 10.1109/ISSA.2015.7335061
Abstract: Security and privacy for e-health Internet-of-Things applications is a challenge arising due to the novelty and openness of the solutions. We analyze the security risks of an envisioned e-health application for elderly persons' day-to-day support and chronic disease self-care, from the perspectives of the service provider and end-user. In addition, we propose initial heuristics for security objective decomposition aimed at security metrics definition. Systematically defined and managed security metrics enable higher effectiveness of security controls, enabling informed risk-driven security decision-making.
Keywords: Internet of Things; data privacy; decision making; diseases; geriatrics; health care; risk management; security of data; chronic disease self-care; e-health Internet-of-Things applications; e-health IoT application; elderly person day-to-day support; privacy; risk-driven security decision-making; risk-driven security metrics development; security controls; security objective decomposition; Artificial intelligence; Android; risk analysis; security effectiveness; security metrics (ID#: 16-10929)


O. Jane, H. G. İlk and E. Elbaşı, “A Comparative Study on Chaotic Map Approaches for Transform Domain Watermarking Algorithm,” Telecommunications and Signal Processing (TSP), 2015 38th International Conference on, Prague, 2015, pp. 1-5. doi: 10.1109/TSP.2015.7296451
Abstract: Watermarking is identified as a major technology to achieve copyright protection and multimedia security. In this study, combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) via Lower-and-Upper (LU) Decomposition is used as a transform domain watermarking algorithm with a comparative study on chaotic map approaches as Logistic Map (LM), Asymmetric Tent Map (ATM), and Arnold's Cat Map (ACM). As quality metrics, Similarity Ratio (SR) values for ACM are greater by approximately 20% than that of LM and ATM despite nearly same Peak Signal-to-Noise Ratios (PSNRs) after attacks. This study expands the application areas of watermarking with the algorithm consisting DWT, SVD, and LU with chaotic maps together.
Keywords: discrete wavelet transforms; image watermarking; singular value decomposition; ACM; ATM; Arnold's cat map; DWT; LM; LU decomposition; PSNR; SR values; SVD; asymmetric tent map; chaotic map approach; copyright protection; discrete wavelet transform; logistic map; lower-and-upper decomposition; multimedia security; peak signal-to-noise ratios; quality metrics; similarity ratio values; transform domain watermarking algorithm; Discrete wavelet transforms; Logistics; Matrix decomposition; Robustness; Watermarking (ID#: 16-10930) 


S. Edward Jero, P. Ramu and S. Ramakrishnan, “Steganography in Arrhythmic Electrocardiogram Signal,” Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, Milan, 2015, pp. 1409-1412. doi: 10.1109/EMBC.2015.7318633
Abstract: Security and privacy of patient data is a vital requirement during exchange/storage of medical information over communication network. Steganography method hides patient data into a cover signal to prevent unauthenticated accesses during data transfer. This study evaluates the performance of ECG steganography to ensure secured transmission of patient data where an abnormal ECG signal is used as cover signal. The novelty of this work is to hide patient data into two dimensional matrix of an abnormal ECG signal using Discrete Wavelet Transform and Singular Value Decomposition based steganography method. A 2D ECG is constructed according to Tompkins QRS detection algorithm. The missed R peaks are computed using RR interval during 2D conversion. The abnormal ECG signals are obtained from the MIT-BIH arrhythmia database. Metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback-Leibler distance and Bit Error Rate are used to evaluate the performance of the proposed approach.
Keywords: data privacy; discrete wavelet transforms; diseases; electrocardiography; medical signal processing; security of data; singular value decomposition; steganography; 2D abnormal ECG signal matrix; ECG steganography; Kullback-Leibler distance; MIT-BIH arrhythmia database; Tompkins QRS detection algorithm; arrhythmic electrocardiogram signal; bit error rate; cover signal; data security; data transfer; discrete wavelet transform; medical information; percentage residual difference; steganography method; Bit error rate; Discrete wavelet transforms; Electrocardiography; Matrix decomposition; Measurement; Watermarking (ID#: 16-10931)


X. Li, W. Wang, A. Razi and T. Li, “Nonconvex Low-Rank Sparse Factorization for Image Segmentation,” 2015 11th International Conference on Computational Intelligence and Security (CIS), Shenzhen, 2015, pp. 227-230. doi: 10.1109/CIS.2015.63
Abstract: In this paper, we present a new color image segmentation model based on nonconvex low-rank and nonconvex sparse (NLRSR) factorization of the feature matrix. The main difference between our model and the recently developed methods like the sparse subspace clustering (SSC) and low-rank representation (LRR) based subspace clustering is that they use the data matrix as the dictionary while we learn a dictionary. In order to better cater to the low-rankness of the dictionary and the sparsity of the represent coefficients, we use the nonconvex penalty functions rather than the convex ones. The variable splitting technique and the alternative minimization method are applied for solving the proposed NLRSR model. The sparse representation coefficient matrix is utilized to construct an affinity matrix and then the normalized cut (Ncut) is applied to obtain the segmentation result. Experimental results show our method can achieve visually better segmentation results than the SSC and LRR method. Objective metrics further confirms this.
Keywords: concave programming; image colour analysis; image representation; image segmentation; matrix decomposition; minimisation; pattern clustering; sparse matrices; LRR based subspace clustering method; NLRSR factorization; Ncut; SSC method; affinity matrix; color image segmentation model; feature matrix; low-rank representation based subspace clustering; nonconvex low-rank sparse factorization; nonconvex penalty functions; normalized cut; sparse representation coefficient matrix; sparse subspace clustering; variable splitting technique; Computational modeling; Dictionaries; Feature extraction; Image segmentation; Measurement; Sparse matrices; Yttrium; Image segmentation; data clustering; low-rank representation; sparse representation (ID#: 16-10932)


Y. Jarraya, A. Shameli-Sendi, M. Pourzandi and M. Cheriet, “Multistage OCDO: Scalable Security Provisioning Optimization in SDN-Based Cloud,” Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on, New York City, NY, 2015, pp. 572-579. doi: 10.1109/CLOUD.2015.82
Abstract: Cloud computing is increasingly changing the landscape of computing, however, one of the main issues that is refraining potential customers from adopting the cloud is the security. Network functions virtualization together with software-defined networking can be used to efficiently coordinate different network security functionality in the network. To squeeze the best out of network capabilities, there is need for algorithms for optimal placement of the security functionality in the cloud infrastructure. However, due to the large number of flows to be considered and complexity of interactions in these networks, the classical placement algorithms are not scalable. To address this issue, we elaborate an optimization framework, namely OCDO, that provides adequate and scalable network security provisioning and deployment in the cloud. Our approach is based on an innovative multistage approach that combines together decomposition and segmentation techniques to the problem of security functions placement while coping with the complexity and the scalability of such an optimization problem. We present the results of multiple scenarios to assess the efficiency and the adequacy of our framework. We also describe our prototype implementation of the framework integrated into an open source cloud framework, i.e. Open stack.
Keywords: cloud computing; optimisation; security of data; SDN-based cloud;  innovative multistage approach; multistage OCDO; scalable network security provisioning; scalable security provisioning optimization framework; security functionality; software-defined networking; Bandwidth; Complexity theory; Network topology; Optimization; Security; Servers; Topology; Cloud; Decomposition; OpenStack; SDN; Security Provisioning; Segmentation (ID#: 16-10933)


I. Jeon, E. E. Papalexakis, U. Kang and C. Faloutsos, “HaTen2: Billion-Scale Tensor Decompositions,” Data Engineering (ICDE), 2015 IEEE 31st International Conference on, Seoul, 2015, pp. 1047-1058. doi: 10.1109/ICDE.2015.7113355
Abstract: How can we find useful patterns and anomalies in large scale real-world data with multiple attributes? For example, network intrusion logs, with (source-ip, target-ip, port-number, timestamp)? Tensors are suitable for modeling these multi-dimensional data, and widely used for the analysis of social networks, web data, network traffic, and in many other settings. However, current tensor decomposition methods do not scale for tensors with millions and billions of rows, columns and 'fibers', that often appear in real datasets. In this paper, we propose HaTen2, a scalable distributed suite of tensor decomposition algorithms running on the MapReduce platform. By carefully reordering the operations, and exploiting the sparsity of real world tensors, HaTen2 dramatically reduces the intermediate data, and the number of jobs. As a result, using HaTen2, we analyze big real-world tensors that cannot be handled by the current state of the art, and discover hidden concepts.
Keywords: Internet; security of data; tensors; HaTen2; MapReduce platform; Web data; billion-scale tensor decompositions; columns; fibers; large scale real-world data; modeling; multidimensional data; network intrusion logs; network traffic; rows; scalable distributed suite; social networks; tensor decomposition algorithms; tensor decomposition methods; Algorithm design and analysis; Computer science; Data models; Matrix converters; Matrix decomposition; Scalability; Tensile stress (ID#: 16-10934)


A. Haidar, A. YarKhan, C. Cao, P. Luszczek, S. Tomov and J. Dongarra, “Flexible Linear Algebra Development and Scheduling with Cholesky Factorization,” High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conference on Embedded Software and Systems (ICESS), 2015 IEEE 17th International Conference on, New York, NY, 2015, pp. 861-864. doi: 10.1109/HPCC-CSS-ICESS.2015.285
Abstract: Modern high performance computing environments are composed of networks of compute nodes that often contain a variety of heterogeneous compute resources, such as multicore CPUs and GPUs. One challenge faced by domain scientists is how to efficiently use all these distributed, heterogeneous resources. In order to use the GPUs effectively, the workload parallelism needs to be much greater than the parallelism for a multicore-CPU. Additionally, effectively using distributed memory nodes brings out another level of complexity where the work load must be carefully partitioned over the nodes. In this work we are using a lightweight runtime environment to handle many of the complexities in such distributed, heterogeneous systems. The runtime environment uses task-superscalar concepts to enable the developer to write serial code while providing parallel execution. The task-programming model allows the developer to write resource-specialization code, so that each resource gets the appropriate sized workload-grain. Our task-programming abstraction enables the developer to write a single algorithm that will execute efficiently across the distributed heterogeneous machine. We demonstrate the effectiveness of our approach with performance results for dense linear algebra applications, specifically the Cholesky factorization.
Keywords: distributed memory systems; graphics processing units; mathematics computing; matrix decomposition; parallel processing; resource allocation; scheduling; Cholesky factorization; GPU; compute nodes; distributed heterogeneous machine; distributed memory nodes; distributed resources; flexible linear algebra development; flexible linear algebra scheduling; heterogeneous compute resources; high performance computing environments; multicore-CPU; parallel execution; resource-specialization code; serial code; task-programming abstraction; task-programming model; task-superscalar concept; workload parallelism; Graphics processing units; Hardware; Linear algebra; Multicore processing; Parallel processing; Runtime; Scalability; accelerator-based distributed memory computers; heterogeneous HPC computing; superscalar dataflow scheduling (ID#: 16-10935)


L. Kuang; L. Yang; J. Feng; M. Dong, “Secure Tensor Decomposition Using Fully Homomorphic Encryption Scheme,” in IEEE Transactions on Cloud Computing, vol. PP, no. 99, pp. 1-1, 2015. doi: 10.1109/TCC.2015.2511769
Abstract: As the rapidly growing volume of data are beyond the capabilities of many computing infrastructures, to securely process them on cloud has become a preferred solution which can both utilize the powerful capabilities provided by cloud and protect data privacy. This paper presents an approach to securely decompose a tensor, a mathematical model widely used in data-intensive applications, to a core tensor multiplied with a certain number of truncated orthogonal bases. The unstructured, semi-structured, and structured data are represented as low-order sub-tensors which are then encrypted using the fully homomorphic encryption scheme. A unified high-order cipher tensor model is constructed by collecting all the cipher sub-tensors and embedding them to a base tensor space. The cipher tensor is decomposed through a proposed secure algorithm, in which the square root operations are eliminated during the Lanczos procedure. Theoretical analyses of the algorithm in terms of time complexity, memory usage, decomposition accuracy, and data security are provided. Experimental results demonstrate that the approach can securely decompose a tensor. With the advancement of fully homomorphic encryption scheme, it can be expected that the secure tensor decomposition approach has the potential to be applied on cloud for privacy-preserving data processing.
Keywords: Ciphers; Cloud computing; Encryption; Matrix decomposition; Symmetric matrices; Tensile stress; Cloud; Fully Homomorphic Encryption; Lanczos Method; Tensor Decomposition (ID#: 16-10936)


M. Nick; O. Alizadeh-Mousavi; R. Cherkaoui; M. Paolone, “Security Constrained Unit Commitment with Dynamic Thermal Line Rating,” in IEEE Transactions on Power Systems , vol. 31, no. 3, pp. 2014-2025, May 2016. doi: 10.1109/TPWRS.2015.2445826
Abstract: The integration of the dynamic line rating (DLR) of overhead transmission lines (OTLs) in power systems security constrained unit commitment (SCUC) potentially enhances the overall system security as well as its technical/economic performances. This paper proposes a scalable and computationally efficient approach aimed at integrating the DLR in SCUC problem. The paper analyzes the case of the SCUC with AC load flow constraints. The AC-optimal power flow (AC-OPF) is linearized and incorporated into the problem. The proposed multi-period formulation takes into account a realistic model to represent the different terms appearing in the Heat-Balance Equation (HBE) of the OTL conductors. In order to include the HBE in the OPF, a relaxation is proposed for the heat gain associated to resistive losses while the inclusion of linear approximations are investigated for both convection and radiation heat losses. A decomposition process relying on the Benders decomposition is used in order to breakdown the problem and incorporate a set of contingencies representing both generators and line outages. The effects of different linearization, as well as time step discretization of HBE, are investigated. The scalability of the proposed method is verified using IEEE 118-bus test system.
Keywords: Conductors; Heating; Mathematical model; Reactive power; Security; Wind speed; AC optimal power flow; Benders decomposition; Heat Balance Equation (HBE); convex formulation (ID#: 16-10937)


H. Ye; Z. Li, “Robust Security-Constrained Unit Commitment and Dispatch with Recourse Cost Requirement,” in IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3527-3536, Sept. 2016. doi: 10.1109/TPWRS.2015.2493162
Abstract: With increasing renewable energy resources, price- sensitive loads, and electric-vehicle charging stations in the power grid, uncertainties on both power generation and consumption sides become critical factors in the Security-Constrained Unit Commitment (SCUC) problem. Recently, worst scenario based robust optimization approaches are employed to consider uncertainties. This paper proposes a non-conservative robust SCUC model and an effective solution approach. The contributions of this paper are three-fold. First, the commitment and dispatch solution obtained in this paper can be directly used in day-ahead market as it overcomes two issues, conservativeness and absence of robust dispatch, which are the two largest obstacles to applying robust SCUC in real markets. Secondly, a new concept recourse cost requirement, similar to reserve requirement, is proposed to define the upper bound of re-dispatch cost when uncertainties are revealed. Thirdly, a novel decomposition approach is proposed to effectively address the well-known computational challenge in robust approaches. Simulation results on the IEEE 118-bus system validate the effectiveness of the proposed novel model and solution approach.
Keywords: Computational modeling; Load modeling; Optimization; Renewable energy sources; Robustness; Stochastic processes; Uncertainty; Recourse cost; redispatch; renewable energy; robust optimization; robust security-constrained unit commitment; uncertainty (ID#: 16-10938)


Y. Wang; H. Zhong; Q. Xia; D. S. Kirschen; C. Kang, “An Approach for Integrated Generation and Transmission Maintenance Scheduling Considering N-1 Contingencies,” in IEEE Transactions on Power Systems, vol. 31, no.  3, pp. 2225-2233, May 2016. doi: 10.1109/TPWRS.2015.2453115
Abstract: This paper presents an approach for integrated generation and transmission maintenance scheduling model (IMS) that takes into consideration N-1 contingencies. The objective is to maximize the maintenance preference of facility owners while satisfying N-1 security and other constraints. To achieve this goal, Benders decomposition is employed to decompose the problem into a master problem and several sub-problems. A Relaxation Induced (RI) algorithm is proposed to efficiently solve the large mixed integer programming (MIP) master problem. This algorithm is based on the solution of the linear relaxed problem. It is demonstrated that the proposed algorithm can efficiently reach a near-optimal solution that is usually satisfactory. If this near-optimal solution is not acceptable, it is used as the initial solution to fast start the solution of the original IMS problem. The performance of the proposed method is demonstrated using a modified version of the IEEE 30-bus system and a model of the power system of a Chinese province. Case studies show that the proposed algorithm can improve the computational efficiency by more than an order of magnitude.
Keywords: Computational modeling; Indexes; Linear programming; Maintenance engineering; Power transmission lines; Schedules; Security; Benders decomposition; N-1 security; generation maintenance scheduling; mixed integer programming; relaxation induced; transmission maintenance scheduling (ID#: 16-10939)


S. Teimourzadeh; F. Aminifar, “MILP Formulation for Transmission Expansion Planning with Short-Circuit Level Constraints,” in IEEE Transactions on Power Systems, vol. 31, no. 4, pp. 3109-3918, July 2016. doi: 10.1109/TPWRS.2015.2473663
Abstract: This paper deals with the short-circuit level constrained transmission expansion planning (TEP) problem through a mixed-integer linear programming (MILP) approach. The proposed framework is outlined by a master problem and three subproblems based on the Benders decomposition technique. The master problem incorporates the optimal investment planning model. System security and short-circuit level constraints are examined by subproblems I and II, respectively. In case of any violation, infeasibility cuts are derived to reflect the appropriate modification in the master problem solution. The short-circuit study is inherently a nonlinear analysis and hard to concurrently be tackled in power system studies. To overcome this difficulty, a linear approximation is developed for the short-circuit analysis which not only mitigates the computational burden of the problem, but even is efficient for taking the advantage of decomposed schemes. Subproblem III examines optimality of the investment solution from the operation point of view and, through optimality cuts, steers the master problem toward the optimal solution. The proposed model is tested on the IEEE 24-bus reliability test system and its effectiveness is assured by comprehensive simulation studies.
Keywords: Impedance; Indexes; Investment; Linear programming; Planning; Power transmission lines; Security; Benders decomposition method; mixed-integer programming (MILP); transmission expansion planning (TEP) (ID#: 16-10940)


P. Henneaux; P. E. Labeau; J. C. Maun; L. Haarla, “A Two-Level Probabilistic Risk Assessment of Cascading Outages,” in IEEE Transactions on Power Systems, vol. 31, no. 3,  pp. 2393-2403, 2015. doi: 10.1109/TPWRS.2015.2439214
Abstract: Cascading outages in power systems can lead to major power disruptions and blackouts and involve a large number of different mechanisms. The typical development of a cascading outage can be split in two phases with different dominant cascading mechanisms. As a power system is usually operated in N-1  security, an initiating contingency cannot entail a fast collapse of the grid. However, it can trigger a thermal transient, increasing significantly the likelihood of additional contingencies, in a “slow cascade.” The loss of additional elements can then trigger an electrical instability. This is the origin of the subsequent “fast cascade,” where a rapid succession of events can lead to a major power disruption. Several models of probabilistic simulations exist, but they tend to focus either on the slow cascade or on the fast cascade, according to mechanisms considered, and rarely on both. We propose in this paper a decomposition of the analysis in two levels, able to combine probabilistic simulations for the slow and the fast cascades. These two levels correspond to these two typical phases of a cascading outage. Models are developed for each of these phases. A simplification of the overall methodology is applied to two test systems to illustrate the concept.
Keywords: Computational modeling; Load modeling; Power system dynamics; Power system stability; Probabilistic logic; Steady-state; Transient analysis; Blackout; Monte Carlo methods; cascading failure; power system reliability; power system security; risk analysis (ID#: 16-10941)


H. Zhang; H. Xing; J. Cheng; A. Nallanathan; V. Leung, “Secure Resource Allocation for OFDMA Two-Way Relay Wireless Sensor Networks Without and with Cooperative Jamming,” in IEEE Transactions on Industrial Informatics, vol. PP, no. 99, pp. 1-1, 2015. doi: 10.1109/TII.2015.2489610
Abstract: We consider secure resource allocations for orthogonal frequency division multiple access two-way relay wireless sensor networks. The joint problem of subcarrier assignment, subcarrier pairing and power allocations is formulated under scenarios of using and not using cooperative jamming to maximize the secrecy sum rate subject to limited power budget at the relay station and orthogonal subcarrier allocation policies. The optimization problems are shown to be mixed integer programming and non-convex. For the scenario without cooperative jamming, we propose an asymptotically optimal algorithm based on the dual decomposition method, and a suboptimal algorithm with lower complexity. For the scenario with cooperative jamming, the resulting optimization problem is non-convex, and we propose a heuristic algorithm based on alternating optimization. Finally, the proposed schemes are evaluated by simulations and compared to the existing schemes.
Keywords: Communication system security; Jamming; Relays; Resource management; Sensors; Wireless communication; Wireless sensor networks; Cooperative jamming; OFDMA; physical layer security; secure resource allocation; wireless sensor network (ID#: 16-10942)


C. J. Neill; R. S. Sangwan; N. H. Kilicay-Ergin, “A Prescriptive Approach to Quality-Focused System Architecture,” in IEEE Systems Journal, vol. PP, no. 99, pp. 1-12, 2015. doi: 10.1109/JSYST.2015.2423259
Abstract: The most critical requirements for the lifetime value of a system are its nonfunctional requirements (NFRs) such as reliability, security, maintainability, changeability, etc. These are collectively known as the “ilities,” and they are typically not addressed in system design until the functional architecture has been completed. In this paper, we propose the use of quality-based design that modifies this standard process so that those NFRs, which actually reflect the true business needs, are addressed first. This is accomplished through a combination of quality attribute workshops, to elicit and refine quality-based mission objectives, and attribute-driven design, where design heuristics, termed tactics, can be employed in the decomposition of the system. This ensures that the final system better reflects and embodies those architecturally significant requirements rather than having them addressed secondarily. This is an important change since the “ilities” are systemic properties (properties of the system as a whole) rather than properties of individual components or subsystems. Consequently, they are difficult to address in an architecture that has already been decomposed with respect to required functionality. To illustrate the proposed approach, we provide an example based upon the Department of Defense Pre-positioned Expeditionary Assistance Kit.
Keywords: Computer architecture; Conferences; Reliability; Security; Software; Systems engineering and theory; Unified modeling language; Attribute-driven design (ADD); disaster assistance system; quality attribute workshops; system architecting (ID#: 16-10943)


Y. Han; P. Shen; X. Zhao; J. M. Guerrero, “Control Strategies for Islanded Microgrid Using Enhanced Hierarchical Control Structure with Multiple Current-Loop Damping Schemes,” in IEEE Transactions on Smart Grid, vol. PP, no. 99, pp. 1-1, 2015. doi: 10.1109/TSG.2015.2477698
Abstract: In this paper, the modeling, controller design, and stability analysis of the islanded microgrid (MG) using enhanced hierarchical control structure with multiple current loop damping schemes is proposed. The islanded MG consists of the parallel-connected voltage source inverters using inductor-capacitor-inductor (LCL) output filters, and the proposed control structure includes the primary control with additional phase-shift loop, the secondary control for voltage amplitude and frequency restoration, the virtual impedance loops which contain virtual positive- and negative-sequence impedance loops at fundamental frequency and virtual variable harmonic impedance loop at harmonic frequencies, and the inner voltage and current loop controllers. A small-signal model for the primary and secondary controls with additional phase-shift loop is presented, which shows an over-damped feature from eigenvalue analysis of the state matrix. The moving average filter-based sequence decomposition method is proposed to extract the fundamental positive and negative sequences and harmonic components. The multiple inner current loop damping scheme is presented, including the virtual positive, virtual negative, and variable harmonic sequence impedance loops for reactive and harmonic power sharing purposes, and the proposed active damping scheme using capacitor current feedback loop of the LCL filter, which shows enhanced damping characteristics and improved inner-loop stability. Finally, the experimental results are provided to validate the feasibility of the proposed approach.
Keywords: Damping; Frequency control; Harmonic analysis; Impedance; Inverters; Power system harmonics; Voltage control; Active damping (AD); droop control; microgrid (MG); phase-shift control; power sharing; secondary control; small-signal model; virtual impedance; voltage control (ID#: 16-10944)


V. Kekatos; G. B. Giannakis; R. Baldick, “Online Energy Price Matrix Factorization for Power Grid Topology Tracking,” in IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1239-1248, May 2016. doi: 10.1109/TSG.2015.2469098
Abstract: Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment. But it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time energy prices are typically calculated as the Lagrange multipliers of network-constrained economic dispatch; that is, via a linear program (LP) typically solved every 5 min. Since the grid Laplacian matrix is a parameter of this LP, someone apart from the system operator could try inferring this topology-related matrix upon observing successive LP dual outcomes. It is first shown that the matrix of spatio-temporal prices can be factored as the product of the inverse Laplacian times a sparse matrix. Leveraging results from sparse matrix decompositions, topology recovery schemes with complementary strengths are subsequently formulated. Solvers scalable to high-dimensional and streaming market data are devised. Numerical validation using synthetic and real-load data on the IEEE 30-bus grid provide useful input for current and future market designs.
Keywords: Laplace equations; Network topology; Power grids; Real-time systems; Sparse matrices; Topology; Transmission line matrix methods; Alternating direction method of multipliers (ADMM); compressive sensing; economic dispatch; graph Laplacian; locational marginal prices (LMPs); online convex optimization (ID#: 16-10945)


X. Gonzalez, J. M. Ramirez and G. Caicedo, “An Alternative Method for Multiarea State Estimation Based on OCD,” Power & Energy Society General Meeting, 2015 IEEE, Denver, CO, 2015, pp. 1-5, 2015. doi: 10.1109/PESGM.2015.7286194
Abstract: The State Estimation (SE) constitutes the main core of the online security analysis, such that the development of suitable strategies to improve state estimators is one of the main aims in the transition toward smart control centers and transmission systems. This paper focuses on an alternative method for multiarea state estimation (MASE) based on Optimality Condition Decomposition (OCD). The state estimation problem is addressed through a decentralized optimization scheme with minimum information exchange among subsystems. The proposed method is applied to an equivalent of the Mexican power grid of 190-buses, which has been split into two and three subsystems. Results indicate that the proposed strategy is a reliable alternative.
Keywords: power grids; power system security; power system state estimation; power transmission control; Mexican power grid; OCD; alternative method; decentralized optimization scheme; multiarea state estimation; online security analysis; optimality condition decomposition; smart control centers; subsystem information exchange; transmission systems; Area measurement; Art; Power measurement; Power system reliability; Reliability; State estimation; Decentralized scheme; Decomposition methods; Lagrangian relaxation; Multiarea state estimation; Non-linear optimization; Optimality Condition Decomposition (ID#: 16-10946)


M. H. Amini, R. Jaddivada, S. Mishra and O. Karabasoglu, “Distributed Security Constrained Economic Dispatch,” Smart Grid Technologies - Asia (ISGT ASIA), 2015 IEEE Innovative, Bangkok, 2015, pp. 1-6. doi: 10.1109/ISGT-Asia.2015.7387167
Abstract: In this paper, we investigate two decomposition methods for their convergence rate which are used to solve security constrained economic dispatch (SCED): 1) Lagrangian Relaxation (LR), and 2) Augmented Lagrangian Relaxation (ALR). First, the centralized SCED problem is posed for a 6-bus test network and then it is decomposed into subproblems using both of the methods. In order to model the tie-line between decomposed areas of the test network, a novel method is proposed. The advantages and drawbacks of each method are discussed in terms of accuracy and information privacy. We show that there is a tradeoff between the information privacy and the convergence rate. It has been found that ALR converges faster compared to LR, due to the large amount of shared data.
Keywords: load dispatching; power system economics; power system security; 6-bus test network; ALR; Augmented lagrangian relaxation; LR; Lagrangian relaxation; centralized SCED problem; convergence rate; decomposition methods; distributed security constrained economic dispatch; information privacy; tie-line modelling; Economics; Generators; Linear programming; Load flow; Optimization; Security; DC power flow; Decomposition theory; Distributed optimization; Lagrangian Relaxation; Security constrained economic dispatch (ID#: 16-10947)


N. A. Daher, I. Mougharbel, M. Saad, H. Y. Kanaan and D. Asber, “Pilot Buses Selection Based on Reduced Jacobian Matrix,” Smart Energy Grid Engineering (SEGE), 2015 IEEE International Conference on, Oshawa, ON, 2015, pp. 1-7. doi: 10.1109/SEGE.2015.7324611
Abstract: The non-supervised insertion of renewable energy sources into electric power networks causes fluctuations that may lead to voltage instability. The simple and coordinated secondary voltage control systems are used to avoid this instability. To obtain maximum regulation performance with optimized number of controllers, an appropriate selection of pilot buses is suggested. In this paper new algorithm is proposed to select optimal pilot buses. This method is based on the singular decomposition of the reduced Jacobian matrix with the voltage security margin index. To evaluate the efficiency of this algorithm, a comparison with the Bifurcation, Clustering with Node-Partitioning Around Medoids and the Hybrid algorithms is proposed. The simulation results show that the proposed algorithm gives optimal pilot buses according to the selection criteria (explained later in the paper).
Keywords: optimal control; optimisation; power system stability; voltage control; bifurcation algorithms; clustering algorithms; hybrid algorithms; maximum regulation performance; nodepartitioning around medoids; nonsupervised renewable energy source insertion electric power network; optimal pilot bus selection; reduced Jacobian matrix; singular decomposition; voltage security margin index; Algorithm design and analysis; Bifurcation; Clustering algorithms; Controllability; Generators; Robustness; Voltage control; Pilot buses selection; Power Network Stability; Renewable energy; Secondary Voltage Control (ID#: 16-10948)


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