Time-domain models for power system stability and unbalance

It is an important and difficult challenge to protect modern interconnected power system from blackouts. Applying advanced power system protection techniques and increasing power system stability are ways to improve the reliability and security of power systems. Phasor-domain software packages such as Power System Simulator for Engineers (PSS/E) can be used to study large power systems but cannot be used for transient analysis. In order to observe both power system stability and transient behavior of the system during disturbances, modeling has to be done in the time-domain. This work focuses on modeling of power systems and various control systems in the Alternative Transients Program (ATP). ATP is a time-domain power system modeling software in which all the power system components can be modeled in detail. Models are implemented with attention to component representation and parameters. The synchronous machine model includes the saturation characteristics and control interface. Transient Analysis Control System is used to model the excitation control system, power system stabilizer and the turbine governor system of the synchronous machine. Several base cases of a single machine system are modeled and benchmarked against PSS/E. A two area system is modeled and inter-area and intra-area oscillations are observed. The two area system is reduced to a two machine system using reduced dynamic equivalencing. The original and the reduced systems are benchmarked against PSS/E. This work also includes the simulation of single-pole tripping using one of the base case models. Advantages of single-pole tripping and comparison of system behavior against three-pole tripping are studied. Results indicate that the built-in control system models in PSS/E can be effectively reproduced in ATP. The benchmarked models correctly simulate the power system dynamics. The successful implementation of a dynamically reduced system in ATP shows promise for studying a small sub-system of a large system without losing the dynamic behaviors. Other aspects such as relaying can be investigated using the benchmarked models. It is expected that this work will provide guidance in modeling different control systems for the synchronous machine and in representing dynamic equivalents of large power systems

3D Printing‐Enabled Design and Manufacturing Strategies for Batteries: A Review

Lithium-ion batteries (LIBs) have significantly impacted the daily lives, finding broad applications in various industries such as consumer electronics, electric vehicles, medical devices, aerospace, and power tools. However, they still face issues (i.e., safety due to dendrite propagation, manufacturing cost, random porosities, and basic & planar geometries) that hinder their widespread applications as the demand for LIBs rapidly increases in all sectors due to their high energy and power density values compared to other batteries. Additive manufacturing (AM) is a promising technique for creating precise and programmable structures in energy storage devices. This review first summarizes light, filament, powder, and jetting-based 3D printing methods with the status on current trends and limitations for each AM technology. The paper also delves into 3D printing-enabled electrodes (both anodes and cathodes) and solid-state electrolytes for LIBs, emphasizing the current state-of-the-art materials, manufacturing methods, and properties/performance. Additionally, the current challenges in the AM for electrochemical energy storage (EES) applications, including limited materials, low processing precision, codesign/comanufacturing concepts for complete battery printing, machine learning (ML)/artificial intelligence (AI) for processing optimization and data analysis, environmental risks, and the potential of 4D printing in advanced battery applications, are also presented

A STUDY OF FACE MILLING INCONEL 718 USING CERAMIC TOOLS

Master'sMASTER OF ENGINEERIN

The machinability and residual stresses in facing of inconel 718

Ph.DDOCTOR OF PHILOSOPH

Optimization of process parameters on machining rate and overcut in electrochemical micromachining using grey relational analysis

36-42This paper investigates the effect and parametric optimization of process parameters for Electrochemical micromachining (EMM) of 304 stainless steel using grey relation analysis. Experiments were conducted using machining voltage, pulse on-time, electrolyte concentration and tool tip shapes as typical process parameters. The grey relational analysis was adopted to obtain grey relational grade for EMM process with multiple characteristics namely machining rate and overcut. Analysis of variance was performed to get the contribution of each parameter on the performance characteristics and it was observed that electrolyte concentration and tool tip shape were the most significant process parameters that affect the EMM robustness. The experimental results reveal that, the conical with rounded electrode, machining voltage of 9V, pulse on-time of 15ms and electrolyte concentration of 0.35mole/l is the optimum combination for higher machining rate and lesser overcut. The experimental results for the optimal setting show that there is considerable improvement in the process

Magnesium ferrite nanostructures for detection of ethanol vapours - A first-principles study

The adsorption behaviour and electronic properties of ethanol vapour on MgFe2O4 ceramic nanostructures are studied using density functional theory technique. The structural stability of MgFe2O4 nanostructure is determined with the help of formation energy. The adsorption behaviour of ethanol molecules on MgFe2O4 base material is analysed in terms of average energy gap variation, Mulliken charge transfer, band gap and adsorption energy. The most prominent adsorption sites of ethanol vapours on MgFe2O4 nanostructure are investigated at atomistic level. The density of states spectrum reveals the clear picture about the electronic properties of MgFe2O4 nanostructure. The density of states and electronic band gap confirmed the adsorption of ethanol vapours on MgFe2O4 nanostructure. The changes in the energy band gap and density of states are observed upon adsorption of ethanol vapour molecules on MgFe2O4 nanostructure. The density of states spectrum also confirms the changes in peak maxima due to the transfer of electrons between MgFe2O4 nanostructure and ethanol vapours. The adsorption of oxygen atom from ethanol vapour on iron in MgFe2O4 is found to be more prominent rather than other adsorption sites. The findings show that MgFe2O4 nanostructure can be utilized to sense the presence of ethanol vapour in the atmosphere

Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study

BackgroundApproximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone–based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence–related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups. ObjectiveThis research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy. MethodsFirst, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm. ResultsThe highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001). ConclusionsThis study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future

Enhanced structure prediction of gene products containing class III adenylyl cyclase domains

Domain finding algorithms are useful to understand overall domain architecture and to propose biological function to gene products. Automated methods of applying these tools to large-scale genome studies often employ stringent thresholds to recognize sequence domains. The realization of additional domains can be tedious involving manual intervention but can lead to better understanding of overall biological function. We propose a multi-step approach for the further examination of unassigned linker regions that exploits properties such as the conservation of domain architectures of homologous proteins to propose connections. Improved structure prediction is possible starting from initial domain architectures, obtained from simple 'domain finding' techniques, by concentrating on connecting unassigned regions. 254 unassigned regions have been examined in 114 gene products that potentially contain at least one class III adenylyl cyclase domain for a pilot study. Reliable structure prediction was possible for nearly 80% of unassigned regions. New connections were recognized that assign putative structure and function to these regions by indirect searches (26%. Several others (34%) could be associated with three-dimensional models that might pertain to novel folds and new functions with enough structural content and evolutionary conservation. The presence of additional domains will provide further clues to the overall function of the gene products and their recruitment in particular biochemical pathways