Optimal Topology Design for Disturbance Minimization in Power Grids

The transient response of power grids to external disturbances influences their stable operation. This paper studies the effect of topology in linear time-invariant dynamics of different power grids. For a variety of objective functions, a unified framework based on $H_2$ norm is presented to analyze the robustness to ambient fluctuations. Such objectives include loss reduction, weighted consensus of phase angle deviations, oscillations in nodal frequency, and other graphical metrics. The framework is then used to study the problem of optimal topology design for robust control goals of different grids. For radial grids, the problem is shown as equivalent to the hard "optimum communication spanning tree" problem in graph theory and a combinatorial topology construction is presented with bounded approximation gap. Extended to loopy (meshed) grids, a greedy topology design algorithm is discussed. The performance of the topology design algorithms under multiple control objectives are presented on both loopy and radial test grids. Overall, this paper analyzes topology design algorithms on a broad class of control problems in power grid by exploring their combinatorial and graphical properties.Comment: 6 pages, 3 figures, a version of this work will appear in ACC 201

Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization

We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operation cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A simple numerical example illustrates inherent tradeoffs between operation cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives

On the Use of Reinforcement Learning for Attacking and Defending Load Frequency Control

The electric grid is an attractive target for cyberattackers given its critical nature in society. With the increasing sophistication of cyberattacks, effective grid defense will benefit from proactively identifying vulnerabilities and attack strategies. We develop a deep reinforcement learning-based method that recognizes vulnerabilities in load frequency control, an essential process that maintains grid security and reliability. We demonstrate how our method can synthesize a variety of attacks involving false data injection and load switching, while specifying the attack and threat models - providing insight into potential attack strategies and impact. We discuss how our approach can be employed for testing electric grid vulnerabilities. Moreover our method can be employed to generate data to inform the design of defense strategies and develop attack detection methods. For this, we design and compare a (deep learning-based) supervised attack detector with an unsupervised anomaly detector to highlight the benefits of developing defense strategies based on identified attack strategies

Summer 2022 Biology Internship: Medical University of Graz, Austria

This internship was a 6 week internship program in Graz, Austria at the Medical University of Graz. This program allows for intercampus and institutional interaction in multiple scientific research disciplines whereby young scientists gain exposure, direction and guidance during their developing career paths. During the internship you will be to assist a transplant surgical team. Additionally, you can assist Dr. Philipp Siegler in their mini project

Information Encryption and Retrieval in Mid-RF Range using Acousto-optic Chaos

In recent work, low-frequency AC signal encryption, decryption and retrieval using system-parameter based keys at the receiver stage of an acousto-optic (A-O) Bragg cell under first-order feedback have been demonstrated [1,2]. The corresponding nonlinear dynamics have also been investigated using the Lyapunov exponent and the so-called bifurcation maps [3]. The results were essentially restricted to A-O chaos around 10 KHz, and (baseband) signal bandwidths in the 1-4 KHz range. The results have generally been satisfactory, and parameter tolerances (prior to severe signal distortion at the output) in the ±5% - ±10% range have been obtained. Periodic AC waveforms, and a short audio clip have been examined in this series of investigations. Obviously, a main drawback in the above series of simulations has been the low and impractical signal bandwidths used. The effort to increase the bandwidth involves designing a feedback system with much higher chaos frequency that would then be amenable to higher BW information. In this paper, we re-visit the problem for the case where the feedback delay time is reduced considerably, and the system parameters in the transmitter adjusted in order to drive the system with a DC driver bias into chaos. Reducing the feedback time delay to less than 1 μs, an average chaos frequency of about 10 MHz was achieved after a few trials. For the AC application, a chaos region was chosen that would allow a large enough dynamic range for the width of the available passband. Based on these dynamic choices, periodic AC signals with 1 MHz (fundamental) bandwidth were used for the RF bias driver (along with a DC bias). A triangular wave and a rectangular pulse train were chosen as examples. Results for these cases are presented here, along with comments on the system performance, and the possibility of including (static) images for signal encryption. Overall, the results are encouraging and affirm the possibility of using A-O chaos for securely transmitting and retrieving information in the mid-RF range (a few 10s of MHz)

Equivalence of primary control strategies for AC and DC microgrids

Microgrid frequency and voltage regulation is a challenging task, as classical generators with rotational inertia are usually replaced by converter-interfaced systems that inherently do not provide any inertial response. The aim of this paper is to analyse and compare autonomous primary control techniques for alternating current (AC) and direct current (DC) microgrids that improve this transient behaviour. In this context, a virtual synchronous machine (VSM) technique is investigated for AC microgrids, and its behaviour for different values of emulated inertia and droop slopes is tested. Regarding DC microgrids, a virtual-impedance-based algorithm inspired by the operation concept of VSMs is proposed. The results demonstrate that the proposed strategy can be configured to have an analogous behaviour to VSM techniques by varying the control parameters of the integrated virtual-impedances. This means that the steady-state and transient behaviour of converters employing these strategies can be configured independently. As shown in the simulations, this is an interesting feature that could be, for instance, employed for the integration of different dynamic generation or storage systems, such as batteries or supercapacitors

Spectral Analysis of Encrypted Chaotic Signals Using Fast Fourier Transforms and Laboratory Spectral Analyzers

The use of acousto-optic chaos, as manifested via first-order feedback in an acousto-optic Bragg cell, in encrypting a message wave and subsequently recovering the message in the receiver using a chaotic heterodyne strategy, has been reported recently [1-3]. In examining the dynamical system analytically using computer simulation, (expected) modulated chaos waveforms are obtained within specified observation windows. Because of the relatively random nature inherent in chaos waveforms, it is essentially impossible to ascertain from the visual display of the chaotic wave whether a given message signal has in fact modulated the chaotic carrier . In fact, it has been observed from earlier work that by appropriately controlling the chaos parameters, one may hide the silhouette of the message from the envelope of the modulated chaos [1]. This was found to be especially true for low-frequency chaos (in the KHz range). For chaos in the mid-RF (up to 10s of MHz) range, it is seen that the silhouette is more difficult to suppress (even though this does not affect the robustness of the encryption). To adequately determine whether modulation has in fact occurred by passing the AC signal through the sound cell bias input, one needs to examine the spectral content of the chaos wave. In this paper, we discuss the results of such spectral analyses using two different approaches, (i) fast Fourier transforms applied to the displayed waveform; and (ii) transferring the intensity-vs-time data to an Excel spreadsheet, and then applying this information to a laboratory spectrum analyzer with adequate bandwidth. The results are mutually compared and interpreted in terms of encryption and decryption properties

A Novel Distributed Privacy Paradigm for Visual Sensor Networks Based on Sharing Dynamical Systems

Visual sensor networks (VSNs) provide surveillance images/video which must be protected from eavesdropping and tampering en route to the base station. In the spirit of sensor networks, we propose a novel paradigm for securing privacy and confidentiality in a distributed manner. Our paradigm is based on the control of dynamical systems, which we show is well suited for VSNs due to its low complexity in terms of processing and communication, while achieving robustness to both unintentional noise and intentional attacks as long as a small subset of nodes are affected. We also present a low complexity algorithm called TANGRAM to demonstrate the feasibility of applying our novel paradigm to VSNs. We present and discuss simulation results of TANGRAM

Advanced ovarian malignancy in pregnancy mimicking ovarian hyperstimulation syndrome: a case report

Advanced ovarian malignancy is a rare occurrence in pregnancy. Here we report a case of primary infertility presenting in early pregnancy following invitro fertilization with features of Ovarian hyperstimulation syndrome unresponsive to treatment. Further evaluation revealed advanced ovarian malignancy. She was treated with chemotherapy followed by staging surgery at the time of elective cesarean at 35 weeks gestation. This case outlines the difficulties in diagnosis of ovarian cancer during pregnancy