This paper presents an integrated modeling framework that captures both the electrical behavior of data centers and the distinctive operational features of interconnected power grids. A detailed data center model is developed, incorporating server clusters, power electronic interfaces, and standby generation, with particular focus on load dynamics during demand response events, backup power transitions, and power quality disturbances. The model is coupled with an electrical circuit representation of variable IT loads, capturing the dynamic characteristics of AI training workloads. Using this integrated framework, the impact of data center operations on grid performance is evaluated, including voltage variations, loss-of-load events, and fault conditions. In addition, grid-level oscillations associated with high penetration of data center loads are examined. Results indicate that rapid variations in data center load can: (a) shift existing and/or develop new oscillatory modes, (b) increase harmonic distortion, (c) reduce system damping (up to 52%), and (d) induce frequency deviations with a maximum of approximately 0.36Hz. These findings highlight the potential impact of large-scale data center dynamics on grid stability.

Accurate topology information is critical for effective monitoring, control, and optimization of modern power distribution systems, particularly as the increasing integration of distributed energy resources (DERs) and frequent switching operations challenge traditional static topology assumptions. A data-driven framework for topology estimation in three-phase unbalanced distribution networks is proposed in this paper that leverages Thevenin equivalent impedance inference. The method estimates Thevenin impedance between selected bus pairs using voltage and current measurements derived from power flow and admittance models. Switch statuses are indirectly identified by analyzing impedance magnitudes and applying defined thresholds to distinguish between open and closed configurations. The framework is implemented in a modular MATLAB environment and validated using the IEEE 123-bus test feeder under various radial topologies. Simulation results demonstrate high robustness and accuracy, achieving classification rates exceeding 98% under diverse loading and switching conditions. The proposed method is compatible with real-time system monitoring and enhances situational awareness for distribution system operators within existing Distribution Management Systems (DMS).

Distribution line impedance is a foundational parameter in power distribution systems, essential for state estimation, fault localization, voltage control, and topology identification. However, the availability of accurate impedance data is often limited, especially during faults and topology changes. This paper proposes a data-driven Hybrid Kalman estimation algorithm to estimate per-phase line conductance and susceptance using only partial voltage and power measurements from substations, regulators, and load buses. The hybrid framework uses a physicsinformed approach that leverages the computational efficiency of linearized prediction and robust handling of nonlinearities, while adaptively tuning the process and measurement noise covariances. Simulation results on IEEE 13-bus and 123-bus test systems show that the proposed method reduces RMSE in susceptance estimation by over 98 % compared to EKF and nearly 40 % compared to UKF, while reducing conductance RMSE by 30 % on average. The results demonstrate the algorithm's scalability, accuracy, and robustness in scenarios with limited measurement observability and phase unbalance common in realworld distribution feeders.

As the world has been increasingly faced with the consequences of human-induced climate change, vehicle electrification has been seen as a critical process in moving away from a carbon-based economy. However, the impact of vehicle electrification on carbon dioxide emission will be limited if the electricity that is used to charge that vehicle comes from a coal-fired or natural gas power plant. Solar electric vehicles address the source of electricity generation by incorporating solar panels into the design of the vehicle itself. While a small number of commercial solar electric vehicles are starting to hit the market, much of the visible development work has taken place in solar racing competitions since the 1980s. As complex electromechanical systems, the prototyping of solar electric vehicles can be expensive in terms of time and money. In recent years, physics-based modeling or digital twins have been advanced as a way to nimbly explore the design space of complex engineered systems. One such platform for physics-based modeling is MATLAB Simscape, which has been used to model conventional, electric, and hybrid vehicles. In this work, we extend that modeling to consider a solar electric race vehicle inspired by the vehicle Appalachian State University entered into the 2022 American Solar Challenge,

Culverts are stormwater structures that require ongoing condition assessment and proactive maintenance for optimal performance to reduce flooding, especially in urban areas. Managing these structures can be complex and costly regarding time, money, and personnel resources. Machine learning (ML) tools are a powerful means of predicting culvert conditions via learning from their existing data records. However, the data available on such subjects are usually low in quantity and of various qualities; thus, they need significant data processing for ML. Existing stormwater infrastructure condition prediction studies rarely detail their data processing methods. Hence, this work uses a comprehensive case study to illustrate essential data preparation techniques to address common data issues and enhance the performance of commonly used ML algorithms. The study shows methods for exploratory data analysis to understand the dataset, data wrangling methods for preprocessing data of various quality, and data engineering procedures for addressing insufficient data issues. After the data processing procedures are applied, F-1 scores are used to evaluate the ML models' performance. The random forest classifier model, one of the four ML models, performed best after applying the data wrangling and data engineering methods. Overall, this work provides transparency of methods and applications to encourage ML use in the water resources engineering field.

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%.

Wastewater treatment processes are energy intensive, thus requiring improvement for energy efficiency. Recent literature shows efforts to improve aspects of wastewater treatment processes with machine learning models. This study focuses on one particular process stage, the aeration stage of wastewater treatment, where bacteria is used for aerobic digestion of organic matter. This bacteria requires oxygen to live, requiring the use of air blowers to maintain the water's oxygen content at a minimum level. This aeration process comprises a significant percentage of the wastewater treatment plant's energy usage; thus, any improvements to this process have a large impact on a plant's overall energy usage. Hence, the goal of this study is to predict the operation intensity of air blowers based on influent water parameters using five machine learning models. Many plants rely on operators using experience and best judgment to make control decisions regarding the air blowers' operation. Reliance on operator experience allows for human error, reducing the energy-efficiency of a wastewater plant. For this study, data from the Benchmark Simulation Model No.1, or BSM1, is used to train several machine learning models in predicting the short-term future operation of a plant's air blowers for a given influent load, and their effectiveness is evaluated relative to one another. With this comparative evaluation, a forecasting model may be found which creates energy savings by helping operators make better-informed control decisions. Based on the example case study results, the proposed approach shows promise for this application (e.g., the Random Forest regression model performed with an average adjusted $R^{2}$ score of 0.8960).

Measurable water quality parameters (e.g., potential of hydrogen, electrical conductivity) are crucial factors in determining the health and viability of coastal fisheries. In particular, shellfish farms (e.g., oysters) are susceptible to changes in water quality. Farmers are often faced with the choice of relying on publicly-available data or investing in expensive commercial monitoring buoys. By gaining accurate real-time localized knowledge of process conditions, effective control methods can then be implemented to maintain optimal process conditions for improved performance and support data-driven decision making. This project focuses on the integration and testing of modern Internet-of-Things (IoT) technologies, open-source software and instrumentation, and automated data collection. The produced water quality measurement (WQM) device consists of a cost-effective PVC frame to support an Arduino-based data collection system. The data collection system monitors and collects seven water quality metrics (i.e., dissolved oxygen, electrical conductivity, oxidation reduction potential, potential of hydrogen, total dissolved solids, temperature, and turbidity). An LTE connection is used to relay the collected metrics and buoy location. The MQTT protocol is used to transmit the data, and a PC receives and translates the data into human-readable graphs and measures. An open-source user interface, developed in Python, allows the user to view time series plots of the data, see a go/no-go status regarding pre-defined process control limits, and view the location of the buoy on a map. This work focuses on the physical construction and testing of the device components and systems to support preparation for future field deployment.

Extreme rainfall events coupled with compromised stormwater infrastructure can result in flooding and erosion. The ability to accurately predict watershed flow behavior during rainfall events can aid stormwater utility professionals in efficiently operating stormwater systems. However, watershed flow behavior can be challenging to predict during rainfall events due to the stochastic behavior of rainfall for a specific location and the nonlinear process characteristics of streams and their respective watersheds. In this work, we propose a machine learning-based approach to model and predict watershed flow behavior during rainfall events to support data driven decision making. In comparison with conventional mathematical model frameworks, machine learning-based models typically provide improved performance for processing large datasets with “noisy” or even missing data points. Several machine learning methods are considered, including long short-term memory (LSTM), which shows promise as a viable method in this application based on the preliminary results with the proposed machine learning framework (e.g., R2 > 0.8).

Wildfires are among the most significant events that can present a considerable risk to power systems, so a proper evaluation of the associated risk is necessary to ensure the resilience and economic value of the system. This work aims to showcase a new geoprocessing methodology that is based on the Getis-Ord Gi* Hotspot spatial analysis to classify the risk of wildfires in overhead transmission lines (OHTL). The method was applied to 354 line spans of a 500 kV transmission line installed in the Brazilian Savanna in Northern Brazil. For validation, the results were compared with another methodology proposed by Berredo et al. that was developed for the same biome. The performance of the new method was validated via Kernel density estimation. The results show positive performance with the proposed method to quantify the risk of wildfires and suggest that the reference model might be excessively conservative.

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. 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 (Br = 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. 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

The purpose of this study is to examine the impact of land use and rainfall on water quality in urban streams. This research includes analysis and prediction of a water quality indicator for streams in Mecklenburg County, the most urbanized county in North Carolina, United States. The analysis helps to determine future pollutant levels based on past trends using machine learning models. Land use data from the Multi-Resolution Land Characteristics Consortium that details land development in the county by classes (e.g., urban, forest) was used, along with monthly average precipitation data for the county. The work explores the use of eight regression models to predict levels of the total suspended solids (TSS) pollutant. The accuracy of the prediction is measured by statistical methods and compared among the models. Based on the results, the proposed approach shows viability for water quality prediction.

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 article 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 article, 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.

This paper presents a unique control architecture for grid-connected inverters. By adding vendor-provided static controllers to an identification-based adaptive control strategy, the proposed method expands the controller’s operating range for dynamic grid conditions. The optimal control policy, based on the nonlinear system dynamics per energy signals at the inverter terminals, determines each controller’s contribution, respectively. These dynamic energy signals are transformed into a physically meaningful signal that represents the dynamics that influence the inverter output power quality via a frequency mode decomposition algorithm per a discrete transform. To enhance the grid’s power quality and overall stability over a wide range of operating conditions, the proposed integrated control architecture eliminates the undesirable high-frequency content via the decomposed energy signals. The proposed method is validated based on experimental results, which showcase the effectiveness of the proposed control over conventional static proportional–integral control (e.g., settling time reduced >56%).

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.

Underground stormwater infrastructure, such as culverts, present a significant maintenance challenge for municipal agencies due to aging, urbanization, and economic pressures. Decision support via machine learning regarding the most effective renewal, replacement, and maintenance, dependable condition prediction can alleviate this burden. In contrast to conventional mathematical models, machine learning-based models generally offer better performance when processing large datasets with missing or “noisy” data. A novel data-driven approach for predicting culvert conditions based on existing data inventory is proposed in this paper using an artificial neural network with a synthetic minority oversampling technique to address imbalanced datasets. Preliminary results show the viability of the proposed machine learning framework for use with this work’s application.

Aging compounded with the urbanization encroachment and economic pressures have rendered underground infrastructure a great challenge to municipal agencies in terms of maintenance. Reliable condition prediction can alleviate this burden for underground pipelines by providing decision support on optimal renewal, replacement, and maintenance. However, municipalities are challenged on the management of stormwater pipelines, as it is usually constrained by limited budget and time. Different from traditional mathematical models, machine learning shows better strength in processing large datasets amidst some missing or "noisy" data. This paper proposes a framework for a novel data-driven model for predicting stormwater infrastructure conditions to identify at-risk pipelines and culverts using existing data inventory.

Renewable energy resources are gaining fast adoption in the power grid because of their relatively low cost and offered environmental benefits. However, the grid will experience decreased inertia as a result of these added resources. Numerous research studies have focused on improving the grid damping, including the lack of inertia, due to renewable resource integration. In this article, we present an adaptive damping controller to help mitigate oscillations during disturbances on the grid, caused by fault occurrences, sudden load changes, capacitor switching, or even the intermittent nature of the energy sources for most renewable systems, considering both integrated transmission and distribution systems. The test system is developed based on Kundur's two-area system as the transmission system and the IEEE 123-bus system as the distribution system. Then, the novel proposed adaptive optimal damping control architecture is validated via simulation-based test cases conducted in MATLAB/Simulink using real-life grid data. It is observed that the proposed control architecture not only damps the oscillations more than 10% as compared with the state-of-the-art methods but can also control multiple generators.