56,551 research outputs found
An internal model approach to (optimal) frequency regulation in power grids with time-varying voltages
This paper studies the problem of frequency regulation in power grids under
unknown and possible time-varying load changes, while minimizing the generation
costs. We formulate this problem as an output agreement problem for
distribution networks and address it using incremental passivity and
distributed internal-model-based controllers. Incremental passivity enables a
systematic approach to study convergence to the steady state with zero
frequency deviation and to design the controller in the presence of
time-varying voltages, whereas the internal-model principle is applied to
tackle the uncertain nature of the loads.Comment: 16 pages. Abridged version appeared in the Proceedings of the 21st
International Symposium on Mathematical Theory of Networks and Systems, MTNS
2014, Groningen, the Netherlands. Submitted in December 201
Optimal pricing control in distribution networks with time-varying supply and demand
This paper studies the problem of optimal flow control in dynamic inventory
systems. A dynamic optimal distribution problem, including time-varying supply
and demand, capacity constraints on the transportation lines, and convex flow
cost functions of Legendre-type, is formalized and solved. The time-varying
optimal flow is characterized in terms of the time-varying dual variables of a
corresponding network optimization problem. A dynamic feedback controller is
proposed that regulates the flows asymptotically to the optimal flows and
achieves in addition a balancing of all storage levels.Comment: Submitted to 21st International Symposium on Mathematical Theory of
Networks and Systems (MTNS) in December 201
Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data
The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations
Influence and optimization of the electrodes position in a piezoelectric energy harvesting flag
Fluttering piezoelectric plates may harvest energy from a fluid flow by
converting the plate's mechanical deformation into electric energy in an output
circuit. This work focuses on the influence of the arrangement of the
piezoelectric electrodes along the plate's surface on the energy harvesting
efficiency of the system, using a combination of experiments and numerical
simulations. A weakly non-linear model of a plate in axial flow, equipped with
a discrete number of piezoelectric patches is derived and confronted to
experimental results. Numerical simulations are then used to optimize the
position and dimensions of the piezoelectric electrodes. These optimal
configurations can be understood physically in the limit of small and large
electromechanical coupling.Comment: To appear in Journal of Sound and Vibratio
Stochastic MPC Design for a Two-Component Granulation Process
We address the issue of control of a stochastic two-component granulation
process in pharmaceutical applications through using Stochastic Model
Predictive Control (SMPC) and model reduction to obtain the desired particle
distribution. We first use the method of moments to reduce the governing
integro-differential equation down to a nonlinear ordinary differential
equation (ODE). This reduced-order model is employed in the SMPC formulation.
The probabilistic constraints in this formulation keep the variance of
particles' drug concentration in an admissible range. To solve the resulting
stochastic optimization problem, we first employ polynomial chaos expansion to
obtain the Probability Distribution Function (PDF) of the future state
variables using the uncertain variables' distributions. As a result, the
original stochastic optimization problem for a particulate system is converted
to a deterministic dynamic optimization. This approximation lessens the
computation burden of the controller and makes its real time application
possible.Comment: American control Conference, May, 201
Control and modeling of a CELSS (Controlled Ecological Life Support System)
Research topics that arise from the conceptualization of control for closed life support systems which are life support systems in which all or most of the mass is recycled are discussed. Modeling and control of uncertain and poorly defined systems, resource allocation in closed life support systems, and control structures or systems with delay and closure are emphasized
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