164 research outputs found
A scalable line-independent design algorithm for voltage and frequency control in AC islanded microgrids
We propose a decentralized control synthesis procedure for stabilizing
voltage and frequency in AC Islanded microGrids (ImGs) composed of Distributed
Generation Units (DGUs) and loads interconnected through power lines. The
presented approach enables Plug-and-Play (PnP) operations, meaning that DGUs
can be added or removed without compromising the overall ImG stability. The
main feature of our approach is that the proposed design algorithm is
line-independent. This implies that (i) the synthesis of each local controller
requires only the parameters of the corresponding DGU and not the model of
power lines connecting neighboring DGUs, and (ii) whenever a new DGU is plugged
in, DGUs physically coupled with it do not have to retune their regulators
because of the new power line connected to them. Moreover, we formally prove
that stabilizing local controllers can be always computed, independently of the
electrical parameters. Theoretical results are validated by simulating in PSCAD
the behavior of a 10-DGUs ImG
Plug-and-Play Model Predictive Control based on robust control invariant sets
In this paper we consider a linear system represented by a coupling graph
between subsystems and propose a distributed control scheme capable to
guarantee asymptotic stability and satisfaction of constraints on system inputs
and states. Most importantly, as in Riverso et al., 2012 our design procedure
enables plug-and-play (PnP) operations, meaning that (i) the addition or
removal of subsystems triggers the design of local controllers associated to
successors to the subsystem only and (ii) the synthesis of a local controller
for a subsystem requires information only from predecessors of the subsystem
and it can be performed using only local computational resources. Our method
hinges on local tube MPC controllers based on robust control invariant sets and
it advances the PnP design procedure proposed in Riverso et al., 2012 in
several directions. Quite notably, using recent results in the computation of
robust control invariant sets, we show how critical steps in the design of a
local controller can be solved through linear programming. Finally, an
application of the proposed control design procedure to frequency control in
power networks is presented
Plug-and-Play Decentralized Model Predictive Control
In this paper we consider a linear system structured into physically coupled
subsystems and propose a decentralized control scheme capable to guarantee
asymptotic stability and satisfaction of constraints on system inputs and
states. The design procedure is totally decentralized, since the synthesis of a
local controller uses only information on a subsystem and its neighbors, i.e.
subsystems coupled to it. We first derive tests for checking if a subsystem can
be plugged into (or unplugged from) an existing plant without spoiling overall
stability and constraint satisfaction. When this is possible, we show how to
automatize the design of local controllers so that it can be carried out in
parallel by smart actuators equipped with computational resources and capable
to exchange information with neighboring subsystems. In particular, local
controllers exploit tube-based Model Predictive Control (MPC) in order to
guarantee robustness with respect to physical coupling among subsystems.
Finally, an application of the proposed control design procedure to frequency
control in power networks is presented.Comment: arXiv admin note: text overlap with arXiv:1210.692
Voltage stabilization in DC microgrids: an approach based on line-independent plug-and-play controllers
We consider the problem of stabilizing voltages in DC microGrids (mGs) given
by the interconnection of Distributed Generation Units (DGUs), power lines and
loads. We propose a decentralized control architecture where the primary
controller of each DGU can be designed in a Plug-and-Play (PnP) fashion,
allowing the seamless addition of new DGUs. Differently from several other
approaches to primary control, local design is independent of the parameters of
power lines. Moreover, differently from the PnP control scheme in [1], the
plug-in of a DGU does not require to update controllers of neighboring DGUs.
Local control design is cast into a Linear Matrix Inequality (LMI) problem
that, if unfeasible, allows one to deny plug-in requests that might be
dangerous for mG stability. The proof of closed-loop stability of voltages
exploits structured Lyapunov functions, the LaSalle invariance theorem and
properties of graph Laplacians. Theoretical results are backed up by
simulations in PSCAD
Plug-and-play distributed state estimation for linear systems
This paper proposes a state estimator for large-scale linear systems
described by the interaction of state-coupled subsystems affected by bounded
disturbances. We equip each subsystem with a Local State Estimator (LSE) for
the reconstruction of the subsystem states using pieces of information from
parent subsystems only. Moreover we provide conditions guaranteeing that the
estimation errors are confined into prescribed polyhedral sets and converge to
zero in absence of disturbances. Quite remarkably, the design of an LSE is
recast into an optimization problem that requires data from the corresponding
subsystem and its parents only. This allows one to synthesize LSEs in a
Plug-and-Play (PnP) fashion, i.e. when a subsystem gets added, the update of
the whole estimator requires at most the design of an LSE for the subsystem and
its parents. Theoretical results are backed up by numerical experiments on a
mechanical system
Model predictive controllers for reduction of mechanical fatigue in wind farms
We consider the problem of dispatching WindFarm (WF) power demand to
individual Wind Turbines (WT) with the goal of minimizing mechanical stresses.
We assume wind is strong enough to let each WTs to produce the required power
and propose different closed-loop Model Predictive Control (MPC) dispatching
algorithms. Similarly to existing approaches based on MPC, our methods do not
require changes in WT hardware but only software changes in the SCADA system of
the WF. However, differently from previous MPC schemes, we augment the model of
a WT with an ARMA predictor of the wind turbulence, which reduces uncertainty
in wind predictions over the MPC control horizon. This allows us to develop
both stochastic and deterministic MPC algorithms. In order to compare different
MPC schemes and demonstrate improvements with respect to classic open-loop
schedulers, we performed simulations using the SimWindFarm toolbox for MatLab.
We demonstrate that MPC controllers allow to achieve reduction of stresses even
in the case of large installations such as the 100-WTs Thanet offshore WF
Stability properties of adaptive real-time feedback scheduling: A statistical approach
This paper focuses on the statistical analysis of an adaptive real-time feedback scheduling technique based on imprecise computation. We consider two-version tasks made of a mandatory and an optional part to be scheduled according to a feedback control rate-monotonic algorithm. A Proportional-Integral-Derivative (PID) control action provides the feedback strategy for deciding about the execution or rejection of the optional sub-tasks. By modelling the task execution times as random variables, we compute the probability density of the CPU utilization and derive conditions on PID parameters guaranteeing the stability of the overall system around a desired level of
CPU utilization. This allows us to highlight the tasks statistics and the scheduling parameters that affect critically stability. The analysis is developed by first exploiting a number of simplifying assumptions that are progressively removed. The main results are also demonstrated through monte-carlo simulations of the scheduling algorithm.Izmir Institute of Technology and Institute Ae´ronautique et Spatia
Universal Approximation Property of Hamiltonian Deep Neural Networks
This paper investigates the universal approximation capabilities of
Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of
Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown
that HDNNs enjoy, by design, non-vanishing gradients, which provide numerical
stability during training. However, although HDNNs have demonstrated
state-of-the-art performance in several applications, a comprehensive study to
quantify their expressivity is missing. In this regard, we provide a universal
approximation theorem for HDNNs and prove that a portion of the flow of HDNNs
can approximate arbitrary well any continuous function over a compact domain.
This result provides a solid theoretical foundation for the practical use of
HDNNs
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