Search

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

This paper proposes the design of robust repetitive control with time-varying sampling periods. First, it develops a new frequency domain method to design a low-order, stable, robust, and causal IIR repetitive compensator using an optimization method to achieve fast convergence and high tracking accuracy. As such, a new stable and causal repetitive controller can be implemented independently to reduce the design complexity. The comprehensive analysis and comparison study are presented. Then, this paper extends the method to design a robust repetitive controller, which compensates time-varying periodic signals in a known range. A complete series of experiments is successfully carried out to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimization of a linear actuator by the “output space mapping (OSM)” method. The underlying objective of this work lies in the minimization of the time required for the achievement of this optimal design. Indeed, in addition to the sole costs of optimization processes strictly speaking, the time needed for the developpement of the models is taken into account. In the context of OSM, two different finite-element models of the same actuator are used. This paper presents these modeling solutions and considers their corresponding accuracy. Results of this multi-objective optimization method are presented and compared with those obtained by the sequential simplex (SS) method based solely on the fine model. Both approaches give similar results. However, the comparison of their performances clearly shows that the OSM algorithm is an effective technique for reducing the computation time of optimization studies, even in the case of relatively simple electromagnetic structures. Hence, this approach leads to an original and effective optimization methodology. [ABSTRACT FROM AUTHOR]

Automatic differentiation (AD) is introduced as a powerful technique to compute derivatives of functions given in the form of computer programs in high-level programming languages such as FORTRAN, C, or C++. This paper applies AD to compute error-free gradients of electromagnetic device sizing models. Then, the obtained gradients are exploited in optimization to size electromagnetic devices by means of minimizing a cost function with constrained parameters and performances. Often, the electromagnetic devices models have to be described not only by analytical formulas, but also by algorithms. This paper proposes an electromagnetic model of a linear actuator dealing with implicit equations solved by numerical algorithms. The ADOL-C package is considered for automatic differentiation. [ABSTRACT FROM PUBLISHER]

Recent years have witnessed extensive research activity in modeling biological phenomena as well as in developing intervention strategies for such phenomena. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. In this paper, two different intervention strategies, namely direct and indirect, are proposed for the S-system model. In the indirect approach, the prespecified desired values for the target variables are used to compute the reference values for the control inputs, and two control algorithms, namely simple sampled-data control and model predictive control (MPC), are developed for transferring the control variables from their initial values to the computed reference ones. In the direct approach, a MPC algorithm is developed that directly guides the target variables to their desired values. The proposed intervention strategies are applied to the glycolytic–glycogenolytic pathway and the simulation results presented demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM PUBLISHER]

This paper deals with the susceptibility of electronic units to radiated electromagnetic (EM) interference and specifically, it focuses on the parasitic coupling of EM fields with printed circuit board interconnects like microstrip lines. To this purpose, a uniform lossless microstrip line illuminated by an EM plane wave is considered and the voltages at the line terminations are evaluated referring to the Baum–Liu–Tesche equations. Based on this, a new algorithm to identify, frequency-by-frequency, the incidence angles and the polarization of the impinging field that gives rise to the maximum induced voltages at the line terminations is presented. [ABSTRACT FROM AUTHOR]

Coordination of directional overcurrent relays in a multiloop subtransmission or distribution network is formulated as an optimization problem. In this paper, the coordination of directional overcurrent relays is formulated as a mixed-integer nonlinear programming problem and is then solved by a new seeker optimization technique. Based on the act of human searching, in the proposed seeker technique, the search direction and step length are determined in an adaptive way. The proposed method is implemented in three different test cases. The results are compared with previously proposed analytic and evolutionary approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to optimize a generator's profit in an offer-based electricity market cleared by an optimal power flow (OPF) program. A generator's offer is optimized based on its transmission-constrained residual demand derivative (TCRDD), which avoids representing the full network model in the optimization. The TCRDD can be easily calculated based on a solved OPF without changing existing OPF algorithms and programs. As demonstrated with an IEEE 118-bus example, the TCRDD approach is computationally efficient. The TCRDD approach can help market participants to bid into electricity markets, and help market monitors to diagnose bidding behaviors in the presence of transmission constraints. [ABSTRACT FROM PUBLISHER]

This paper presents State-space Model Interpolation of Local Estimates (SMILE), a technique to estimate linear parameter-varying (LPV) state-space models for multiple-input multiple-output (MIMO) systems whose dynamics depends on multiple time-varying parameters, called scheduling parameters. The SMILE technique is based on the interpolation of linear time-invariant models estimated for constant values of the scheduling parameters. As the linear time-invariant models can be either continuous- or discrete-time, both continuous- and discrete-time LPV models can be obtained. The underlying interpolation technique is formulated as a linear least-squares problem that can be efficiently solved. The proposed technique yields homogeneous polynomial LPV models in the multi-simplex that are numerically well-conditioned and therefore suitable for LPV control synthesis. The potential of the SMILE technique is demonstrated by computing a continuous-time interpolating LPV model for an analytic mass-spring-damper system and a discrete-time interpolating LPV model for a mechatronic XY-motion system based on experimental data. [ABSTRACT FROM AUTHOR]

In this paper, a two-time-scale coordination control method to mitigate wind power fluctuations using a battery energy storage system (BESS) is proposed. Two-time-scale maximal power fluctuation restrictions (MPFRs) are set for the combined output of the wind farm and the BESS: the maximal fluctuation of the combined power in any 1- and 30-min time window must be kept within \gamma 1\min\\% and \gamma 30\min \\% , respectively, of the rated power of the wind farm. A flexible first-order low-pass filter (FLF) with an optimization of time constant T_f is developed to limit the power fluctuation under restriction with smaller BESS capacity. Then, a coordination control method is developed on base of the FLF, which contains: 1) PSO-based time constant real-time optimizing algorithm; 2) remaining energy level feedback control; 3) two-time-scale coordination control strategy. Finally, an estimation of BESS capacity and power rating for a wind farm to achieve the MPFRs is presented, and the whole method is tested in a time-domain simulation system. [ABSTRACT FROM AUTHOR]

This paper reports on enhancing the thermal performance of multiple-chip modules (MCMs) that contain a number of chips under natural convection by optimizing the chip placement layout. To attain this goal, an innovative hybrid optimization approach (HOA) incorporating a genetic algorithm (GA) into an algorithm based on a response surface method (RSM) is introduced for improving the performance of both these algorithms. The GA in the proposed HOA is responsible for not only evolving the population toward better fitness value but also, based on the newly evolved populations at each GA generation, for continuously updating the RS mathematical model for better approximation of the chip junction temperature. The sum of the mathematical expressions representing the total system temperature defines the objective of the optimization problems. For each GA generation, a constrained quadratic optimization subproblem is formed, based on the newly updated approximate RS mathematical model as the objective function along with the specified constraints. The solution of the optimization subproblem is sought through a mathematical programming model. As the genetic RS optimization progresses, a sequence of approximate solutions associated with the continuously updated RS mathematical models is constructed. The iterative process continues until convergence of the approximate solutions is attained. To demonstrate the effectiveness of the developed algorithm, several thermal design problems associated with two types of MCMs with equal/unequal power are performed. The obtained results are compared with those derived using two conventional approaches—the GA-based and the RSM-based optimization techniques. Results show that the developed algorithm can provide good optimal solutions with much less computational effort, and the larger the scale of the design problems, the more significant the improvement in the computation cost. [ABSTRACT FROM PUBLISHER]

Augmented Lagrangian variational formulations and alternating optimization have been adopted to solve distributed parameter estimation problems. The alternating direction method of multipliers (ADMM) is one of such formulations/optimization methods. Very recently, the number of applications of the ADMM, or variants of it, to solve inverse problems in image and signal processing has increased at an exponential rate. The reason for this interest is that ADMM decomposes a difficult optimization problem into a sequence of much simpler problems. In this paper, we use the ADMM to reconstruct piecewise-smooth distributed parameters of elliptical partial differential equations from noisy and linear (blurred) observations of the underlying field. The distributed parameters are estimated by solving an inverse problem with total variation (TV) regularization. The proposed instance of the ADMM solves, in each iteration, an \ell2 and a decoupled \ell2 - \ell1 optimization problems. An operator splitting is used to simplify the treatment of the TV regularizer, avoiding its smooth approximation and yielding a simple yet effective ADMM reconstruction method compared with previously proposed approaches. The competitiveness of the proposed method, with respect to the state-of-the-art, is illustrated in simulated 1-D and 2-D elliptical equation problems, which are representative of many real applications. [ABSTRACT FROM AUTHOR]

The linear mixture model is a convenient way to describe image pixels as a linear combination of pure spectra – termed endmembers. The fractional contribution from each endmember is calculated through inversion of the linear model. Despite the simplicity of the model, a nonnegativity constraint that is imposed on the fractions leads to an unmixing problem for which it is hard to find a closed analytical solution. Current solutions to this problem involve iterative algorithms, which are computationally intensive and not appropriate for unmixing large number of pixels. This paper presents an algorithm to build fuzzy membership functions that are equivalent to the least square solution of the fully constrained linear spectral unmixing problem. The efficiency and effectiveness of the proposed solution is demonstrated using both simulated and real data. [ABSTRACT FROM AUTHOR]

A new approach for easy and fast modeling of EMI filter in aircraft application is proposed, in order to be used in an optimization process. A modular description in a user friendly environment allows describing the model and taking into account all the technological parameters of the system, i.e., the control strategy, the inverter model (semiconductors, layout, etc.), the filter, the cables, and the ac machine. The frequency model is automatically built from this description, as well as its gradients according to its inputs. In a second step, by using this model, an optimization of the filter values can be carried out, using various algorithms, to reach the smallest volume. This early design methodology can also be used to investigate the impact of some technological choices of the electrical drive (control strategy or cable characteristics for instance) on the filter volume. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the next-generation multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signal-to-interference-and-noise-ratio) model, which is considered to be a better characterization of interference (but also more difficult to analyze) than disk graph model. The main difficulties of this problem are two-fold. First, SINR is a nonconvex function of transmission powers; an optimization problem in the SINR model is usually a nonconvex program and NP-hard in general. Second, in the SINR model, scheduling feasibility and the maximum allowed flow rate on each link are determined by SINR at the physical layer. To maximize capacity, it is essential to follow a cross-layer approach, but joint optimization at physical (power control), link (scheduling), and network (flow routing) layers with the SINR function is inherently difficult. In this paper, we give a mathematical characterization of the joint relationship among these layers. We devise a solution procedure that provides a (1- \varepsilon ) optimal solution to this complex problem, where \varepsilon is the required accuracy. Our theoretical result offers a performance benchmark for any other algorithms developed for practical implementation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the interaction of power control, scheduling, and flow routing in a CRN. [ABSTRACT FROM AUTHOR]

Distribution systems commonly operate with a radial topology, so all models of optimization problems in these distribution systems should consider radiality in their formulation. This work presents a literature review, a critical analysis, and a proposal for incorporating the radiality constraints in mathematical models of optimization problems for radial distribution systems. The objective is to show that the radiality constraints on such optimization problems can be considered in a simple and efficient way. The reconfiguration and expansion planning problems of distribution systems are used to test and verify the proposed radiality constraints. A generalization of radiality constraints is also examined. [ABSTRACT FROM PUBLISHER]

To handle different types of many-objective optimization problems (MaOPs), many-objective evolutionary algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel matrix and probability model called determinantal point processes (DPPs). Our MaOEA with DPPs (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs with different numbers of objectives. The experimental results demonstrate that MaOEADPPs is competitive. [ABSTRACT FROM AUTHOR]

We propose a unified mathematical model for multilayer-multiframe compressive light field displays that supports both attenuation-based and polarization-based architectures. We show that the light field decomposition of such a display can be cast as a bound constrained nonlinear matrix optimization problem. Efficient light field decomposition algorithms are developed using the limited-memory BFGS (L-BFGS) method for automultiscopic displays with high resolution and high image fidelity. In addition, this framework is the first to support multilayer polarization-based compressive light field displays with time multiplexing. This new architecture significantly reduces artifacts compared with attenuation-based multilayer-multiframe displays; thus, it can allow the requirements regarding the number of layers or the refresh rate to be relaxed. We verify the proposed methods by constructing two 3-layer prototypes using high-speed LCDs, one based on the attenuation architecture and one based on the polarization architecture. Moreover, an efficient CUDA-based program is implemented. Our displays can produce images with higher spatial resolution with thinner form factors compared with traditional automultiscopic displays in both simulations and experiments. [ABSTRACT FROM AUTHOR]

Quantitative methods such as model-based predictive control are known to facilitate the design of strategies to manipulate biological systems. This study develops a sparse-grid-based adaptive model predictive control (MPC) strategy to direct HL60 cellular differentiation. Sparse-grid sampling and interpolation support a computationally efficient adaptive MPC scheme in which multiple data-consistent regions of the model parameter space are identified and used to calculate a control compromise. The algorithm is evaluated in silico with structural model mismatch. Simulations demonstrate how the multiscenario control strategy more effectively manages the mismatch compared to a single scenario approach. Furthermore, the controller is evaluated in vitro to differentiate HL60 cells in both normal and perturbed environments. The controller-derived input sequence successfully achieves and sustains the specified target level of granulocytes when implemented in the laboratory. The results and analysis given here imply that adoption of this experiment planning technique to direct cell differentiation within more complex tissue engineered constructs will require the use of a reasonably accurate mathematical model and an extension of this algorithm to multiobjective controller design. [ABSTRACT FROM PUBLISHER]

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm. [ABSTRACT FROM PUBLISHER]

The convolutional perfectly-matched-layer (CPML) absorbing boundary condition is fully capable of handling near-field wave absorption that usually combines near-grazing wave incidence with wave evanescence. The appropriate choice of the various CPML parameters to realize this potential for any given simulation problem is a challenging task that is typically achieved through exhaustive and time-consuming searches that involve large numbers of full-scale simulations. The presented work here uses a previously developed predictive system of equations that accurately determines numerical reflections off the PML interface and embeds it into a global optimization routine that reliably computes the required optimum CPML parameters. This predictive system of equations has also been extended and validated for the M24 and FV24 integral-based high-order FDTD algorithms. With this approach, the task of selecting optimum CPML parameters that would usually take several days of intense computations can now be accomplished within a few minutes on an average personal computer. [ABSTRACT FROM AUTHOR]