Michael Smith

Wildfires are one of the main contributors to the failure of overhead lines, presenting significant threats to power systems. The complex interaction between vegetation, topography, and ignition sources makes prediction challenging. Furthermore, current overhead transmission line standards do not establish design or operation and maintenance protocols to evaluate and address risks, impacts, and mitigation methods of wildfire. This study presents a novel methodology to assess the risk of exposure of overhead transmission lines (OHTLs) to wildfires and the associated risk of outage due to wildfire-induced flashovers. Applied to a 500 kV compact line in the Brazilian Savanna, the tested risk assessment presented positive performance compared with alternative techniques. Additionally, this study investigated 108 outages of the same OHTL caused by wildfire-induced flashovers, revealing potential inadequacies in the minimum vegetation clearance distance (MVCD) and vegetation management practice adopted.

The power grid complexity has increased with the expanded penetration of renewable energy resources, creating grid performance challenges. This work proposes a parametrically optimized coordinated control framework and investigates the impact of a synchronous condenser and its associated parameters on the oscillation characteristics and stability of a weak grid system integrated with renewable energy (e.g., wind). For analysis, a model of a power grid with a wind farm (Type 4), and a thermal power station lumped group approach (e.g., exciter system and steam governor) are developed to model the grid with a variable impedance network. Then, to evaluate the specific impact of the synchronous condenser parameters (e.g., exciter gain, transient reactance, and inertial constant), a linearized state-space model is constructed. A design guideline is proposed, based on the study outcomes, for the optimal selection of synchronous condenser parameters for specific bulk grid conditions. For validation, analysis is performed in PSCAD, an electromagnetic transient simulation platform, and MATLAB/Simulink via a representative weak grid model. The proposed approach offers improved control and enhanced stability of the electric grid with renewables.

In this paper, a novel method for the identification of oscillatory modes based on subspace identification is proposed for bulk electric grids integrated with renewable energy resources (RERs) and battery energy storage systems (BESS). The main contribution of the work is the development of a subspace identification framework to monitor the grid at various measurement points and identify the oscillatory modes. Based on this information, the paper also proposes a control algorithm for BESS that can damp the frequency oscillations of the grid. The main advantage of such a technique is its ability to improve electric grid stability with millions of RERs and BESS. The architecture is tested on a smaller electric grid model and modified IEEE test system models. It was found that the proposed method not only provides information regarding the system oscillatory modes in grid-connected and islanded modes of operation with RERs and BESS, but it is also capable of damping frequency oscillations. As showcased in validation, the improvement in frequency oscillation damping is more than 20%.

This paper presents the theory and development, validation, and results of a transient computational multiphysics model for analyzing the magnetic field, particle dynamics, and capture efficiency of magnetic and nonmagnetic (e.g., Red Blood Cells and E. Coli bacteria) microparticles in a traveling wave ferromagnetic microfluidic device. This computational model demonstrates proof-of-concept of a method for greatly enhancing magnetic bioseparation in ferromicrofluidic systems using an array of copper conductive elements arranged in quadrature to create a periodic potential energy landscape. In contrast to previous works, our approach theoretically uses a microfluidic device with an electronic chip platform consisting of integrated copper electrodes that carry currents to generate programable magnetic field gradients locally. Alternating currents are applied to the electrodes in quadrature (using a 90 deg phase change from the neighboring electrode) to create a periodic magnetic field pattern that travels along the length of the microchannel. Our previous work evaluated magnetic and nonmagnetic particles in a static magnetic field within the same channel geometry. This work is a phase 2 study that expands on the previous work and analyzes the dynamics of magnetic and nonmagnetic entities characterized by material magnetic susceptibility in a transient magnetic field. This is an improvement over our previous work. The model, which is described in more detail in the methods section, combines a Eulerian-Lagrangian and two-way particle-fluid coupling CFD analysis with closed-form magnetic field analysis that is used to predict magnetic separation considering dominant magnetic and hydrodynamic forces similar to our previous works in magnetic drug targeting. The model was also validated with an experimental low frequency stationary flow study on separating nonmagnetic latex fluorescent particles in a water based ferrofluid. The results from the experimental study and the developed model demonstrate that the proposed device may potentially be used as an effective platform for microparticle and cellular manipulation and sorting. The developed multiphysics model could potentially be used as a design optimization tool for traveling wave ferromicrofluidic devices.

Purpose Worldwide, cardiovascular disease is the leading cause of hospitalization and death. Recently, the use of magnetizable nanoparticles for medical drug delivery has received much attention for potential treatment of both cancer and cardiovascular disease. However, proper understanding of the interacting magnetic field forces and the hydrodynamics of blood flow is needed for effective implementation. This paper presents the computational results of simulated implant assisted medical drug targeting (IA-MDT) via induced magnetism intended for administering patient specific doses of therapeutic agents to specific sites in the cardiovascular system. The drug delivery scheme presented in this paper functions via placement of a faintly magnetizable stent at a diseased location in the carotid artery, followed by delivery of magnetically susceptible drug carriers guided by the local magnetic field. Using this method, the magnetic stent can apply high localized magnetic field gradients within the diseased artery, while only exposing the neighboring tissues, arteries, and organs to a modest magnetic field. The localized field gradients also produce the forces needed to attract and hold drug-containing magnetic nanoparticles at the implant site for delivering therapeutic agents to treat in-stent restenosis. Methods The multi-physics computational model used in this work is from our previous work and has been slightly modified for the case scenario presented in this paper. The computational model is used to analyze pulsatile blood flow, particle motion, and particle capture efficiency in a magnetic stented region using the magnetic properties of magnetite (Fe₃O₄) and equations describing the magnetic forces acting on particles produced by an external cylindrical electromagnetic coil. The electromagnetic coil produces a uniform magnetic field in the computational arterial flow model domain, while both the particles and the implanted stent are paramagnetic. A Eulerian-Lagrangian technique is adopted to resolve the hemodynamic flow and the motion of particles under the influence of a range of magnetic field strengths (B_r = 2T, 4T, 6T, and 8T). Particle diameter sizes of 10 nm–4 µm in diameter were evaluated. Two dimensionless numbers were evaluated in this work to characterize relative effects of Brownian motion (BM), magnetic force induced particle motion, and convective blood flow on particle motion. Results The computational simulations demonstrate that the greatest particle capture efficiency results for particle diameters within the micron range of 0.7–4 µm, specifically in regions where flow separation and vortices are at a minimum. Similar to our previous work (which did not involve the use of a magnetic stent), it was also observed that the capture efficiency of particles decreases substantially with particle diameter, especially in the superparamagnetic regime. Contrary to our previous work, using a magnetic stent tripled the capture efficiency of superparamagnetic particles. The highest capture efficiency observed for superparamagnetic particles was 78% with an 8 T magnetic field strength and 65% with a 2 T magnetic field strength when analyzing 100 nm particles. For 10 nm particles and an 8 T magnetic field strength, the particle capture efficiency was 55% and for a 2 T magnetic field strength the particle capture efficiency was observed to be 43%. Furthermore, it was found that larger magnetic field strengths, large particle diameter sizes (1 µm and above), and slower blood flow velocity improves the particle capture efficiency. The distribution of captured particles on the vessel wall along the axial and azimuthal directions is also discussed. Results for captured particles on the vessel wall along the axial flow direction showed that the particle density decreased along the axial direction, especially after the stented region. For the entrance section of the stented region, the captured particle density distribution along the axial direction is large, corresponding to the center-symmetrical distribution of the magnetic force in that section. Conclusion The simulation results presented in this work have shown to yield favorable capture efficiencies for micron range particles and superparamagnetic particles using magnetized implants such as the stent discussed in this work. The results presented in this work justify further investigation of MDT as a treatment technique for cardiovascular disease.

Nonlinear behavior is exhibited by many real-world processes (e.g., power grids, mechanical processes, such as power plant cooling systems). For such processes, depending on the characteristics of the system, various control strategies have been used. However, a single control method alone may not always provide optimal stable performance, based on the process conditions. This paper proposes a hybrid adaptive control architecture with a method to improve system knowledge. The proposed hybrid control architecture is aimed to provide improved performance for a nonlinear process during transient conditions via an adaptive control method with improved system knowledge that produces optimized performance. The hybrid adaptive control technique combines controllers with separate cost functions, each desirable during different process conditions to leverage the strengths of each control method, with a method to improve system knowledge for input to the adaptive control functions. In this paper, the proposed control architecture methodology (e.g., mathematical model) is detailed first and then the stability of the proposed method is evaluated. For validation of the proposed control method, an illustrative example is then provided based on a real-life nonlinear system (e.g., pressurizer pressure control). The representative test case is detailed and the performance results are compared (e.g., conventional and other adaptive control methods). It is demonstrated that the proposed control method reduces the peak time and overshoot by 27% when compared to a linear quadratic regulator acting alone.

Inspection of a spent fuel pool (SFP) liner of a nuclear power plant is important to validate the integrity of this component to perform its intended function (e.g., confirm that there are not any defects in the pool liner). Eddy current array (ECA) testing offers a reliable and sensitive nondestructive examination (NDE) technique to inspect SFP liners but requires a delivery system to transport the ECA sensor to these hard-to-reach and harsh environment locations. This work presents an ECA sensor delivery tool design for use with inspection of an SFP. The design for a payload delivery tool proof-of-concept model is proposed that can transport an ECA sensor with positional feedback. The tool allows the sensor to traverse the wall of a nuclear SFP liner, with both horizontal and vertical motion capabilities to support positioning and inspection. The tool provides both manual and automatic operating modes with variable speeds. Computational-based simulations and analysis validate that the performance requirements for the ECA sensor delivery tool are satisfied to support inspection of an SFP liner upper region.

This work investigates the impact of phase-locked loop (PLL) parameters on the stability of a wind-integrated weak power grid. The analysis is based on a weak grid model that integrates a Type-4 wind turbine generator with the grid represented by a lumped group model of a thermal power station with a steam governor and exciter system through a variable impedance network. This model is classically used for a weak grid study representing a real-life system. First, the impact of proportional and integral gain constants of the PLL on stability and oscillation characteristics is discussed in detail. Based on the analysis, an adaptive gain scheduling PI-based PLL architecture is proposed. The advantages of the proposed architecture are studied based on rigorous changes in the grid impedances and the wind dynamics. The results show that the proposed architecture significantly improves grid stability and damping when compared with conventional PLL designs.