Information rate maximization over a resistive grid

The work presents the first results of the authors research on adaptive cellular neural networks (CNN) based on a global information theoretic cost-function. It considers the simplest case of optimizing a resistive grid such that the Shannon information rate across the input-output boundaries of the grid is maximized. Besides its importance in information theory, information rate has been proven to be a useful concept for principal as well independent component analysis (PCA, ICA). In contrast to linear fully connected neural networks, resistive grids due to their local coupling can resemble models of physical media and are feasible for a VLSI implementation. Results for spatially invariant as well as for the spatially variant case are presented and their relation to principal subspace analysis (PSA) is outlined. Simulation results show the validity of the proposed results

Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio

Output Impedance Diffusion into Lossy Power Lines

Output impedances are inherent elements of power sources in the electrical grids. In this paper, we give an answer to the following question: What is the effect of output impedances on the inductivity of the power network? To address this question, we propose a measure to evaluate the inductivity of a power grid, and we compute this measure for various types of output impedances. Following this computation, it turns out that network inductivity highly depends on the algebraic connectivity of the network. By exploiting the derived expressions of the proposed measure, one can tune the output impedances in order to enforce a desired level of inductivity on the power system. Furthermore, the results show that the more "connected" the network is, the more the output impedances diffuse into the network. Finally, using Kron reduction, we provide examples that demonstrate the utility and validity of the method

Dynamic Energy Management

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with time over a horizon. We leverage this to develop a real-time control strategy, model predictive control, which at each time step solves a dynamic power flow optimization problem, using forecasts of future quantities such as demands, capacities, or prices, to choose the current power flow values. Finally, we consider a useful extension of model predictive control that explicitly accounts for uncertainty in the forecasts. We mirror our framework with an object-oriented software implementation, an open-source Python library for planning and controlling power flows at any scale. We demonstrate our method with various examples. Appendices give more detail about the package, and describe some basic but very effective methods for constructing forecasts from historical data.Comment: 63 pages, 15 figures, accompanying open source librar

Vanadium Redox Flow Battery Modelling and PV Self-Consumption Management Strategy Optimization

This work aims to maximize the photovoltaic solar electricity’s self-consumption, through the development and validation of an equivalent electric model of a vanadium redox flow battery and its implementation in an energy management strategy. The first phase of the work presents the modelling of the 5.0 kW/60 kWh VRFB integrated in a solar photovoltaic microgrid - 3.5 kWp monocrystalline plus 3.2 kWp polycrystalline technology - at the University of Évora. The model is based in the equivalent electric circuit model built upon the consulted bibliographic references allowing to calculate the battery parameters on the desired power. It considers the auxiliary power consumption and operational parameters and despite its simplicity attains for a good match with experimental results. Upon its validation, the model is further enhanced as to better describe the VRFB real response in its regular operating conditions. Assessment of the enhanced model is based on key performance indicators such as selfconsumption rate, rate of battery usage or electric grid independence. In this work an approach to best fit the battery modelling and simultaneously the energy management strategy for a PV+VRFB system is presented, based on actual operating conditions and on a prescribed EMS goal

A control strategy for a distributed power generation microgrid application with voltage and current controlled source converter

This paper presents a pseudodroop control structure integrated within a microgrid system through distributed power generation (DPG) modules capable to function in off-grid islanded, genset-connected, and grid-connected modes of operation. System efficiency has an important role in order to harvest the maximum available renewable energy from dc or ac sources while providing power backup capability. A control strategy is proposed in off-grid islanded mode method based on the microgrid line-frequency control as agent of communication for energy control between the DPG modules. A critical case is where the ac load demand could be lower than the available power from the photovoltaic solar array, where the battery bank can be overcharged with unrecoverable damage consequences. The DPG voltage-forming module controls the battery charge algorithm with a frequency-generator function, and the DPG current source module controls its output current through a frequency-detection function. The physical installation between DPG modules is independent, since no additional communication wiring is needed between power modules, which represent another integration advantage within the microgrid-type application

A Circuit-based Model for the Interpretation of Perfect Metamaterial Absorbers

A popular absorbing structure, often referred to as Perfect Metamaterial Absorber, comprising metallic periodic pattern over a thin low-loss grounded substrate is studied by resorting to an efficient transmission line model. This approach allows the derivation of simple and reliable closed formulas describing the absorption mechanism of the subwavelength structure. The analytic form of the real part of the input impedance is explicitly derived in order to explain why moderate losses of the substrate is sufficient to achieve matching with free space, that is, perfect absorption. The effect of the constituent parameters for tuning the working frequency and tailoring the absorption bandwidth is addressed. It is also shown that the choice of highly capacitive coupled elements allows obtaining the largest possible bandwidth whereas a highly frequency selective design is achieved with low capacitive elements like a cross array. Finally, the angular stability of the absorbing structure is investigated.Comment: Accepted for publication on IEEE Transactions on Antennas and Propagatio

Identification of vortexes obstructing the dynamo mechanism in laboratory experiments

The magnetohydrodynamic dynamo effect explains the generation of self-sustained magnetic fields in electrically conducting flows, especially in geo- and astrophysical environments. Yet the details of this mechanism are still unknown, e.g., how and to which extent the geometry, the fluid topology, the forcing mechanism and the turbulence can have a negative effect on this process. We report on numerical simulations carried out in spherical geometry, analyzing the predicted velocity flow with the so-called Singular Value Decomposition, a powerful technique that allows us to precisely identify vortexes in the flow which would be difficult to characterize with conventional spectral methods. We then quantify the contribution of these vortexes to the growth rate of the magnetic energy in the system. We identify an axisymmetric vortex, whose rotational direction changes periodically in time, and whose dynamics are decoupled from those of the large scale background flow, is detrimental for the dynamo effect. A comparison with experiments is carried out, showing that similar dynamics were observed in cylindrical geometry. These previously unexpected eddies, which impede the dynamo effect, offer an explanation for the experimental difficulties in attaining a dynamo in spherical geometry.Comment: 25 pages, 12 figures, submitted to Physics of Fluid

Reactive control of a two-body point absorber using reinforcement learning

In this article, reinforcement learning is used to obtain optimal reactive control of a two-body point absorber. In particular, the Q-learning algorithm is adopted for the maximization of the energy extraction in each sea state. The controller damping and stiffness coefficients are varied in steps, observing the associated reward, which corresponds to an increase in the absorbed power, or penalty, owing to large displacements. The generated power is averaged over a time horizon spanning several wave cycles due to the periodicity of ocean waves, discarding the transient effects at the start of each new episode. The model of a two-body point absorber is developed in order to validate the control strategy in both regular and irregular waves. In all analysed sea states, the controller learns the optimal damping and stiffness coefficients. Furthermore, the scheme is independent of internal models of the device response, which means that it can adapt to variations in the unit dynamics with time and does not suffer from modelling errors