Abstract

Abstract
In this paper a Distribution State Estimator (DSE) tool suitable for real-time monitoring in poorly characterized low voltage networks is presented. An Autoencoder (AE) properly trained with Extreme Learning Machine (ELM) technique is the "brain" of the DSE. The estimation of system state variables, i.e., voltage magnitudes and phase angles is performed with an Evolutionary Particle Swarm Optimization (EPSO) algorithm that makes use of the already trained AE. By taking advantage of historical data and a very limited number of quasi real-time measurements, the presented approach turns possible monitoring networks where information of topology and parameters is not available. Results show improvements in terms of estimation accuracy and time performance when compared to other similar DSE tools that make use of the traditional back-propagation based algorithms for training execution.

Abstract
We analyse a model of immune response by T cells (CD4), where regulatory T cells (Tregs) act by inhibiting IL-2 secretion. We introduced an asymmetry reflecting that the difference between the growth and death rates can be higher for the active T cells and the active Tregs than for the inactive T cells and inactive Tregs. This asymmetry mimics the presence of memory T cells. In this paper we start by analysing the model in the absence of Tregs. We obtain an explicit formula that gives approximately the antigenic stimulation of T cells from the concentration of Tregs. Afterwards, we present an explicit formula that describes approximately the balance between the concentration of T cells and the concentration of Tregs; and an explicit formula that relates approximately the antigenic stimulation of T cells, the concentration of T cells and the concentration of Tregs. For our parameter values, the relation between the antigenic stimulation of T cells and the concentration of T cells is an hysteresis that is unfold when some of the parameters are changed. We also consider a linear tuning between the antigenic stimulation of T cells and the antigenic stimulation of Tregs. Again, we have obtained an explicit formula relating approximately the antigenic stimulation of T cells, the concentration of T cells and the concentration of Tregs. With it, we can explain the appearance of an isola and a transcritical bifurcation. © 2015 NSP Natural Sciences Publishing Cor.

Abstract
The smart grid concept increases the observability and controllability of the distribution system, which creates conditions for bi-directional control of Distributed Energy Resources (DER). The high penetration of Renewable Energy Resources (RES) in the distribution grid may create technical problems (e.g., voltage problems, branch congestion) in both transmission and distribution systems. The flexibility from DER can be explored to minimize RES curtailment and increase its hosting capacity. This paper explores the use of the Monte Carlo Simulation to estimate the flexibility range of active and reactive power at the boundary nodes between transmission and distribution systems, considering the available flexibility at the distribution grid level (e.g., demand response, on-load tap changer transformers). The obtained results suggest the formulation of an optimization problem in order to overcome the limitations of the Monte Carlo Simulation, increasing the capability to find extreme points of the flexibility map and reducing the computational effort.

Abstract
In this paper, an indirect approach to the dual-mode adaptive robust controller (DMARC) is proposed, which combines the typical transient and robustness properties of variable structure systems with a smooth control signal in steady state, typical of conventional adaptive controllers, as model reference adaptive controller. The aim of this indirect version, here named indirect DMARC, is to provide a more intuitive controller design, based on physical plant parameters, as resistances, inertia moments, capacitances, and so on, maintaining DMARC properties. In this paper, a stability analysis for the proposed controller and simulations to an unstable second-order plant will be presented. Copyright (c) 2013 John Wiley & Sons, Ltd.

Abstract
The continuous change in the manufacturing world is demanding more flexible, responsive and accurate planning tools, which are able to assist the decision-makers to take tactical and strategic decisions on short notice with a high level of confidence. For this purpose, these tools should dynamically explore different operative scenarios in the planning procedure and produce information about key performance indicators. This paper describes the development of an agent-based strategic planner, combining the flexibility of multi-agent systems principles with the optimization capability of a Mixed Integral Programming technique. The tool is integrated in an ecosystem of heterogeneous decision-making systems through an Enterprise Service Bus that also provides access to legacy data.