MMC control optimization approach to facilitate DC-side interoperability in MTDC networks

2022 ◽  
Vol 203 ◽  
pp. 107639
Author(s):  
Fisnik Loku ◽  
Matthias Quester ◽  
Christina Brantl ◽  
Antonello Monti
Keyword(s):  
Optimization Approach ◽  
Control Optimization

scholarly journals Fundamentals of Force-Controlled Friction Riveting: Part II—Joint Global Mechanical Performance and Energy Efficiency

Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2489 ◽  
Author(s):  
Gonçalo Pina Cipriano ◽  
Lucian Blaga ◽  
Jorge dos Santos ◽  
Pedro Vilaça ◽  
Sergio Amancio-Filho
Keyword(s):  
Energy Efficiency ◽  
Mechanical Response ◽  
Mechanical Performance ◽  
Tensile Force ◽  
Optimization Approach ◽  
Additional Energy ◽  
Efficient Manner ◽  
Control Optimization ◽  
Friction Parameters ◽  
And Control

The present work investigates the correlation between energy efficiency and global mechanical performance of hybrid aluminum alloy AA2024 (polyetherimide joints), produced by force-controlled friction riveting. The combinations of parameters followed a central composite design of experiments. Joint formation was correlated with mechanical performance via a volumetric ratio (0.28–0.66 a.u.), with a proposed improvement yielding higher accuracy. Global mechanical performance and ultimate tensile force varied considerably across the range of parameters (1096–9668 N). An energy efficiency threshold was established at 90 J, until which, energy input displayed good linear correlations with volumetric ratio and mechanical performance (R-sq of 0.87 and 0.86, respectively). Additional energy did not significantly contribute toward increasing mechanical performance. Friction parameters (i.e., force and time) displayed the most significant contributions to mechanical performance (32.0% and 21.4%, respectively), given their effects on heat development. For the investigated ranges, forging parameters did not have a significant contribution. A correlation between friction parameters was established to maximize mechanical response while minimizing energy usage. The knowledge from Parts I and II of this investigation allows the production of friction riveted connections in an energy efficient manner and control optimization approach, introduced for the first time in friction riveting.

scholarly journals An Anti-Jamming Hierarchical Optimization Approach in Relay Communication System via Stackelberg Game

2019 ◽  
Vol 9 (16) ◽  
pp. 3348 ◽  
Author(s):  
Zhibin Feng ◽  
Guochun Ren ◽  
Jin Chen ◽  
Chaohui Chen ◽  
Xiaoqin Yang ◽  
...  
Keyword(s):  
Communication System ◽  
Stackelberg Game ◽  
Learning Algorithm ◽  
Stackelberg Equilibrium ◽  
Hierarchical Optimization ◽  
Optimization Approach ◽  
Selection Probability ◽  
Control Optimization ◽  
Relay Communication ◽  
Feedback Error

In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user’s strategy selection probability and the jammer’s regret. We compare the user’s and jammer’s utility under the proposed algorithm with a random selection algorithm to verify the algorithm’s superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.

scholarly journals Geometric Enclosing Networks

2018 ◽  
Author(s):  
Trung Le ◽  
Hung Vu ◽  
Tu Dinh Nguyen ◽  
Dinh Phung
Keyword(s):  
Feature Space ◽  
Training Model ◽  
Geometric Interpretation ◽  
Training Data ◽  
Modern World ◽  
Optimization Approach ◽  
Research Attention ◽  
Control Optimization ◽  
Current State ◽  
Real World Datasets

Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optimization approach to address this problem. Orthogonal to current state-of-the-art density-based approaches, most notably VAE and GAN, we present a fresh new idea that borrows the principle of minimal enclosing ball to train a generator G\left(\bz\right) in such a way that both training and generated data, after being mapped to the feature space, are enclosed in the same sphere. We develop theory to guarantee that the mapping is bijective so that its inverse from feature space to data space results in expressive nonlinear contours to describe the data manifold, hence ensuring data generated are also lying on the data manifold learned from training data. Our model enjoys a nice geometric interpretation, hence termed Geometric Enclosing Networks (GEN), and possesses some key advantages over its rivals, namely simple and easy-to-control optimization formulation, avoidance of mode collapsing and efficiently learn data manifold representation in a completely unsupervised manner. We conducted extensive experiments on synthesis and real-world datasets to illustrate the behaviors, strength and weakness of our proposed GEN, in particular its ability to handle multi-modal data and quality of generated data.

Development and Application of a Dynamic Decomposition Strategy for the Optimal Synthesis/Design and Operational/Control of a SOFC Based APU Under Transient Conditions

2005 ◽  
Author(s):  
Diego Rancruel ◽  
Michael von Spakovsky
Keyword(s):  
Life Cycle ◽  
System Level ◽  
System Response ◽  
Optimization Approach ◽  
Total System ◽  
Operational Control ◽  
Dynamic Synthesis ◽  
Control Optimization ◽  
Synthesis Design ◽  
Total Life

A typical approach to the synthesis/design optimization of energy systems is to only use steady state operation and high efficiency (or low total life cycle cost) at full load as the basis for the synthesis/design. Transient operation is left as a secondary task to be solved by system and control engineers once the synthesis/design is fixed. However, transient regimes may happen quite often and the system response to them is a critical factor in determining the system feasibility. Therefore, it is important to consider the system dynamics in the creative process of developing the system. A dynamic optimization approach developed by the authors and called Dynamic Iterative Local-Global Optimization (DILGO) is applied to the dynamic synthesis/design and operational/control optimization of a solid oxide fuel cell based auxiliary power unit. The approach is based on a decomposed optimization of individual units (components and sub-systems), which simultaneously takes into account the interactions between all the units which make up the overall system. The approach was developed to support and enhance current engineering synthesis/design practices, producing improvements in the initial synthesis/design state of the system and its components at all stages of the process and allowing for any degree of detail (from the simple to the complex) at the unit (component or sub-system) level. The total system is decomposed into three sub-systems: stack sub-system (SS), fuel processing sub-system (FPS), and the work and air recovery sub-system (WRAS). Mixed discrete, continuous, and dynamic operational decision variables are considered. Detailed thermodynamic, kinetic, geometric, physical, and cost models are developed for the dynamic system using advanced state-of-the-art tools. DILGO is then applied to the dynamic synthesis/design and operational/control optimization of the system using total life cycle costs as the objective function. Results for this system and component optimization are presented and discussed.

scholarly journals Stochastic optimization strategies applied to the OLYMPUS benchmark

2019 ◽  
Vol 24 (6) ◽  
pp. 1943-1958 ◽  
Author(s):  
V. L. S. Silva ◽  
M. A. Cardoso ◽  
D. F. B. Oliveira ◽  
R. J. de Moraes
Keyword(s):  
Stochastic Optimization ◽  
Net Present Value ◽  
Computational Cost ◽  
Real Life ◽  
Sequential Optimization ◽  
Optimization Approach ◽  
Well Placement ◽  
Well Control ◽  
Field Development ◽  
Control Optimization

AbstractIn this work, we discuss the application of stochastic optimization approaches to the OLYMPUS case, a benchmark challenge which seeks the evaluation of different techniques applied to well control and field development optimization. For that matter, three exercises have been proposed, namely, (i) well control optimization; (ii) field development optimization; and (iii) joint optimization. All applications were performed considering the so-called OLYMPUS case, a synthetic reservoir model with geological uncertainty provided by TNO (Fonseca 2018). Firstly, in the well control exercise, we successfully applied an ensemble-based approximate gradient method in a robust optimization formulation. Secondly, we solve the field development exercise using a genetic algorithm framework designed with special features for the problem of interest. Finally, in order to evaluate further gains, a sequential optimization approach was employed, in which we run one more well control optimization based on the optimal well locations. Even though we utilize relatively well-known techniques in our studies, we describe the necessary adaptations to the algorithms that enable their successful applications to real-life scenarios. Significant gains in the expected net present value are obtained: in exercise (i) a gain of 7% with respect to reactive control; for exercise (ii) a gain of 660% with respect to a initial well placement based on an engineering approach; and for (iii) an extra gain of 3% due to an additional well control optimization after the well placement optimization. All these gains are obtained with an affordable computational cost via the extensive utilization of high-performance computing (HPC) infrastructure. We also apply a scenario reduction technique to exercise (i), with similar gains obtained in the full ensemble optimization, however, with substantially inferior computational cost. In conclusion, we demonstrate how the state-of-the-art optimization technology available in the model-based reservoir management literature can be successfully applied to field development optimization via the conscious utilization of HPC facilities.

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