Multivectorial strategy to interpret a resistive behaviour of loads in smart buildings

In Smart buildings, electric loads are affected by an important distortion in the current and voltage waveforms, caused by the increasing proliferation of non linear electronic devices. This paper presents an approach on non sinusoidal power theory based on Geometric Algebra that clearly improves traditional methods in the optimization of apparent power and power factor compensation. An example is included that demonstrates the superiority of this approach compared with traditional methods.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Plug-and-play Solvability of the Power Flow Equations for Interconnected DC Microgrids with Constant Power Loads

In this paper we study the DC power flow equations of a purely resistive DC power grid which consists of interconnected DC microgrids with constant-power loads. We present a condition on the power grid which guarantees the existence of a solution to the power flow equations. In addition, we present a condition for any microgrid in island mode which guarantees that the power grid remains feasible upon interconnection. These conditions provide a method to determine if a power grid remains feasible after the interconnection with a specific microgrid with constant-power loads. Although the presented condition are more conservative than existing conditions in the literature, its novelty lies in its plug-and-play property. That is, the condition gives a restriction on the to-be-connected microgrid, but does not impose more restrictions on the rest of the power grid.Comment: 8 pages, 2 figures, submitted to IEEE Conference on Decision and Control 201

Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions

Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.Comment: 18 page

Power Flow Modelling of Dynamic Systems - Introduction to Modern Teaching Tools

As tools for dynamic system modelling both conventional methods such as transfer function or state space representation and modern power flow based methods are available. The latter methods do not depend on energy domain, are able to preserve physical system structures, visualize power conversion or coupling or split, identify power losses or storage, run on conventional software and emphasize the relevance of energy as basic principle of known physical domains. Nevertheless common control structures as well as analysis and design tools may still be applied. Furthermore the generalization of power flow methods as pseudo-power flow provides with a universal tool for any dynamic modelling. The phenomenon of power flow constitutes an up to date education methodology. Thus the paper summarizes fundamentals of selected power flow oriented modelling methods, presents a Bond Graph block library for teaching power oriented modelling as compact menu-driven freeware, introduces selected examples and discusses special features.Comment: 12 pages, 9 figures, 4 table

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

Stabilization of MT-HVDC grids via passivity-based control and convex optimization

This paper presents a model for stabilizing multi-terminal high voltage direct-current (MT-HVDC) networks with constant power terminals (CPTs) interfaced with power electronic converters. A hierarchical structure of hierarchical control is developed, which guarantees a stable operation under load variations. This structure includes a port-Hamiltonian formulation representing the network dynamics and a passivity-based control (PBC) for the primary control. This control guarantees stability according to Lyapunov’s theory. Next, a convex optimal power flow formulation based on semidefinite programming (SDP) defines the control’s set point in the secondary/ tertiary control. The proposed stabilization scheme is general for both point-to-point HVDC systems and MTHVDC grids. Simulation results in MATLAB/Simulink demonstrate the stability of the primary control and the optimal performance of the secondary/tertiary control, considering three simulation scenarios on a reduced version of the CIGRE MT-HVDC test system: (i) variation of generation and load, (ii) short-circuit events with different fault resistances and (iii) grid topology variation. These simulations prove the applicability and efficiency of the proposed approach

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