Improved micro-contact resistance model that considers material deformation, electron transport and thin film characteristics

This paper reports on an improved analytic model forpredicting micro-contact resistance needed for designing microelectro-mechanical systems (MEMS) switches. The originalmodel had two primary considerations: 1) contact materialdeformation (i.e. elastic, plastic, or elastic-plastic) and 2) effectivecontact area radius. The model also assumed that individual aspotswere close together and that their interactions weredependent on each other which led to using the single effective aspotcontact area model. This single effective area model wasused to determine specific electron transport regions (i.e. ballistic,quasi-ballistic, or diffusive) by comparing the effective radius andthe mean free path of an electron. Using this model required thatmicro-switch contact materials be deposited, during devicefabrication, with processes ensuring low surface roughness values(i.e. sputtered films). Sputtered thin film electric contacts,however, do not behave like bulk materials and the effects of thinfilm contacts and spreading resistance must be considered. Theimproved micro-contact resistance model accounts for the twoprimary considerations above, as well as, using thin film,sputtered, electric contact

Faults Detection for Power Systems

Non

Viking '75 spacecraft design and test summary. Volume 1: Lander design

The Viking Mars program is summarized. The design of the Viking lander spacecraft is described

Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems

Conventional automatic reclosures blindly operate for permanent, semi-permanent or transient faults on an overhead line without any discrimination after allowing some estimated time delay. Reclosing onto a line with uncleared fault often results in, not only loss of stability and synchronism but also damage to system equipments, as a consequence. The thesis focuses on methods to discriminate a temporary fault from a permanent one, and accurately determine fault extinctiontime in an extra high voltage (EHV) transmission line in a bid to develop a self-adaptive automatic reclosing scheme. The fault identification prior to reclosing is based on optimized artificial neural network associated with three training algorithms, namely, Standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms. In addition, Taguchi's methodology is employed in optimizing the parameters of each algorithm used for training, and in deciding the number of hidden neurons of the neural network. To get data for training the neural networks, a range of faults are simulated on two case studies -single machine -infinite bus model (connected via EHVtransmission line) and a benchmark IEEE 9-bus electric system. The spectra of the fault voltage data are analyzed using Fast Fourier Transform, and it has been found out that the DC, the fundamental and the first four harmonic components can sufficiently and uniquely represent the condition of each fault. In each case study, the neural network is fed with the normalized energies of the DC, the fundamental and the first four harmonics of the faulted voltages, effectively trained with a set of training data, and verified with a dedicated testing data obtained from fault voltage signals generated on IEEE 14-bus electric system model. The results show the efficacy of the developed adaptive automatic reclosing scheme. This effectively means it is possible to avoid reclosing before any fault on a transmission line (be it temporary or permanent) is totally cleared

Digital Filters for Maintenance Management

Faults in mechanisms must be detected quickly and reliably in order to avoid important losses. Detection systems should be developed to minimize maintenance costs and are generally based on consistent models, but as simple as possible. Also, the models for detecting faults must adapt to external and internal conditions to the mechanism. The present chapter deals with three particular maintenance algorithms for turnouts in railway infrastructure by means of discrete filters that comply with these general objectives. All of them have the virtue of being developed within a well-known and common framework, namely the State Space with the help of the Kalman Filter (KF) and/or complementary Fixed Interval Smoother (FIS) algorithms. The algorithms are tested on real applications and thorough results are shown

Improving the resilience of cyber-physical systems under strategic adversaries

Renewable energy resources challenge traditional energy system operations by substituting the stability and predictability of fossil fuel based generation with the unreliability and uncertainty of wind and solar power. Rising demand for green energy drives grid operators to integrate sensors, smart meters, and distributed control to compensate for this uncertainty and improve the operational efficiency of the grid. Real-time negotiations enable producers and consumers to adjust power loads during shortage periods, such as an unexpected outage or weather event, and to adapt to time-varying energy needs. While such systems improve grid performance, practical implementation challenges can derail the operation of these distributed cyber-physical systems. Network disruptions introduce instability into control feedback systems, and strategic adversaries can manipulate power markets for financial gain. This dissertation analyzes the impact of these outages and adversaries on cyber-physical systems and provides methods for improving resilience, with an emphasis on distributed energy systems. First, a financial model of an interdependent energy market lays the groundwork for profit-oriented attacks and defenses, and a game theoretic strategy optimizes attack plans and defensive investments in energy systems with multiple independent actors. Then attacks and defenses are translated from a theoretical context to a real-time energy market via denial of service (DoS) outages and moving target defenses. Analysis on two market mechanisms shows how adversaries can disrupt market operation, destabilize negotiations, and extract profits by attacking network links and disrupting communication. Finally, a low-cost DoS defense technique demonstrates a method that energy systems may use to defend against attacks

Modeling Cascading Failures in Power Systems in the Presence of Uncertain Wind Generation

One of the biggest threats to the power systems as critical infrastructures is large-scale blackouts resulting from cascading failures (CF) in the grid. The ongoing shift in energy portfolio due to ever-increasing penetration of renewable energy sources (RES) may drive the electric grid closer to its operational limits and introduce a large amount of uncertainty coming from their stochastic nature. One worrisome change is the increase in CFs. The CF simulation models in the literature do not allow consideration of RES penetration in studying the grid vulnerability. In this dissertation, we have developed tools and models to evaluate the impact of RE penetration on grid vulnerability to CF. We modeled uncertainty injected from different sources by analyzing actual high-resolution data from North American utilities. Next, we proposed two CF simulation models based on simplified DC power flow and full AC power flow to investigate system behavior under different operating conditions. Simulations show a dramatic improvement in the line flow uncertainty estimation based on the proposed model compared to the simplified DC OPF model. Furthermore, realistic assumptions on the integration of RE resources have been made to enhance our simulation technique. The proposed model is benchmarked against the historical blackout data and widely used models in the literature showing similar statistical patterns of blackout size