pwc-archive/papers-with-abstracts huggingface dataset:
A Collaborative System of Systems Simulation of Urban Air Mobility
Authors: Nabih Naeem, Patrick Ratei, Prajwal Shiva Prakasha, Lukas Asmer, Roman Jaksche, Henry Pak, Karolin Schweiger, Asija Velieva, Fares Naser, Majed Swaid, Jan Pertz, Malte Niklass
Date: 2023-10-03 00:00:00
Tasks: Management

The implementation of Urban Air Mobility represents a complex challenge in aviation due to the high degree of innovation required across various domains to realize it. From the use of advanced aircraft powered by novel technologies, the management of the air space to enable high density operations, to the operation of vertidromes serving as a start and end point of the flights, Urban Air Mobility paradigm necessitates significant innovation in many aspects of civil aviation as we know it today. In order to understand and assess the many facets of this new paradigm, a Collaborative Agent-Based Simulation is developed to holistically evaluate the System of Systems through the modeling of the stakeholders and their interactions as per the envisioned Concept of Operations. To this end, models of vertidrome air-side operations, unmanned/manned air space management, demand estimation and passenger mode choice, vehicle operator cost and revenues, vehicle design, and fleet management are brought together into a System of Systems Simulation of Urban Air Mobility. Through collaboration, higher fidelity models of each domain can be integrated into a single environment achieving fidelity levels not easily achievable otherwise. Furthermore, the integration enables the capture of cross-domain effects and allows domain-specific studies to be evaluated at a holistic level. This work demonstrates the Collaborative Simulation and the process of building it through the integration of several geographically distributed tools into an Agent-Based Simulation without the need for sharing code.
Grid-Forming Control Methods for Weakly Connected Offshore WPPs
Authors: Sulav Ghimire, Kanakesh V Kkuni, Simon C Jakobsen, Thyge Knueppel, Kim H Jensen, Emerson Guest, Tonny W Rasmussen, Guangya Yang
Date: 2023-10-03 00:00:00

Grid-forming control (GFC) has seen numerous technological advances in their control types, applications, and the multitude of services they provide. Some examples of the services they provide include black start, inertial frequency response, and islanded operation capabilities with the possibility of re-synchronization without the need of additional support from other devices such as storage. State of the art literature proposes a variety of GFCs which can provide single or multiple of these services. However, study of these different GFCs for weakly-connected offshore wind power plants (WPPs) based on time-domain simulation and focusing on the large signal disturbance is not well covered. This paper reviews some of the most researched grid-forming control methods applicable to offshore WPPs and provides a comparative investigation and discussion of their stability properties and applicability, especially when connected to a weak-grid. The paper also provides a discussion on the prerequisites and challenges surrounding the comparative study of different GFCs.
A Unifying Perspective for Safety of Stochastic Systems: From Barrier Functions to Finite Abstractions
Authors: Luca Laurenti, Morteza Lahijanian
Date: 2023-10-03 00:00:00

Providing safety guarantees for stochastic dynamical systems is a central problem in various fields, including control theory, machine learning, and robotics. Existing methods either employ Stochastic Barrier Functions (SBFs) or rely on numerical approaches based on finite abstractions. SBFs, analogous to Lyapunov functions, are used to establish (probabilistic) set invariance, whereas abstraction-based approaches approximate the stochastic system with a finite model to compute safety probability bounds. This paper presents a unifying perspective on these seemingly different approaches. Specifically, we show that both methods can be interpreted as approximations of a stochastic dynamic programming problem. This perspective allows us to formally establish the correctness of both techniques, characterize their convergence and optimality properties, and analyze their respective assumptions, advantages, and limitations. Our analysis reveals that, unlike SBFs-based methods, abstraction-based approaches can provide asymptotically optimal safety certificates, albeit at the cost of increased computational effort.
Design and Stability of Angle based Feedback Control in Power Systems: A Negative-Imaginary Approach
Authors: Yijun Chen, Ian R. Petersen, Elizabeth L. Ratnam
Date: 2023-10-03 00:00:00

This paper considers a power transmission network characterized by interconnected nonlinear swing dynamics on generator buses. At the steady state, frequencies across different buses synchronize to a common nominal value such as $50$Hz or $60$Hz, and power flows on transmission lines are within steady-state envelopes. We assume that fast measurements of generator rotor angles are available. Our approach to frequency and angle control centers on equipping generator buses with large-scale batteries that are controllable on a fast timescale. We link angle based feedback linearization control with negative-imaginary systems theory. Angle based feedback controllers are designed using large-scale batteries as actuators and can be implemented in a distributed manner incorporating local information. Our analysis demonstrates the internal stability of the interconnection between the power transmission network and the angle based feedback controllers. This internal stability underscores the benefits of achieving frequency synchronization and preserving steady-state power flows within network envelopes through the use of feedback controllers. Our approach will enable transmission lines to be operated at maximum power capacity since stability robustness is ensured by the use of feedback controllers rather than conservative criteria such as the equal area criterion. By means of numerical simulations, we illustrate our results.
Health Guardian: Using Multi-modal Data to Understand Individual Health
Authors: Vince S. Siu, Kuan Yu Hsieh, Italo Buleje, Takashi Itoh, Tian Hao, Ben Civjan, Nigel Hinds, Bing Dang, Jeffrey L. Rogers, Bo Wen
Date: 2023-10-03 00:00:00

Artificial intelligence (AI) has shown great promise in revolutionizing the field of digital health by improving disease diagnosis, treatment, and prevention. This paper describes the Health Guardian platform, a non-commercial, scientific research-based platform developed by the IBM Digital Health team to rapidly translate AI research into cloud-based microservices. The platform can collect health-related data from various digital devices, including wearables and mobile applications. Its flexible architecture supports microservices that accept diverse data types such as text, audio, and video, expanding the range of digital health assessments and enabling holistic health evaluations by capturing voice, facial, and motion bio-signals. These microservices can be deployed to a clinical cohort specified through the Clinical Task Manager (CTM). The CTM then collects multi-modal, clinical data that can iteratively improve the accuracy of AI predictive models, discover new disease mechanisms, or identify novel biomarkers. This paper highlights three microservices with different input data types, including a text-based microservice for depression assessment, a video-based microservice for sit-to-stand mobility assessment, and a wearable-based microservice for functional mobility assessment. The CTM is also discussed as a tool to help design and set up clinical studies to unlock the full potential of the platform. Today, the Health Guardian platform is being leveraged in collaboration with research partners to optimize the development of AI models by utilizing a multitude of input sources. This approach streamlines research efforts, enhances efficiency, and facilitates the development and validation of digital health applications.
Decentralized Micro Water-Energy Co-Optimization for Small Communities
Authors: Jesus Silva-Rodriguez, Xingpeng Li
Date: 2023-10-02 00:00:00
Tasks: Management

The water-energy nexus encompasses the interdependencies between water and energy resources identifying the existing links between the production and distribution of these resources. Therefore, understanding the water-energy nexus is crucial for developing sustainable and integrated resource management approaches. This paper proposes a decentralized co-optimization model for a micro water-energy nexus system (MWEN), aiming to optimize the combined supply of both resources to end consumers. The approach respects the separate ownership and management of the water and energy sectors while bridging the gap between their optimized operations. An enhanced version of the alternating direction method of multipliers (ADMM) is proposed, the objective-based ADMM (OB-ADMM), which is able to robustly optimize each system independently towards a common objective, only sharing information about the power consumption of water management, providing privacy for each resource provider.
Data-driven Forced Oscillation Localization using Inferred Impulse Responses
Authors: Shaohui Liu, Hao Zhu, Vassilis Kekatos
Date: 2023-10-02 00:00:00

Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems. In this work, we propose a comprehensive data-driven framework for inferring the sources of forced oscillation (FO) using solely synchrophasor measurements. During normal grid operations, fast-rate ambient data are collected to recover the impulse responses in the small-signal regime, without requiring the system model. When FO events occur, the source is estimated based on the frequency domain analysis by fitting the least-squares (LS) error for the FO data using the impulse responses recovered previously. Although the proposed framework is purely data-driven, the result has been established theoretically via model-based analysis of linearized dynamics under a few realistic assumptions. Numerical validations demonstrate its applicability to realistic power systems including nonlinear, higher-order dynamics with control effects using the IEEE 68-bus system, and the 240-bus system from the IEEE-NASPI FO source location contest. The generalizability of the proposed methodology has been validated using different types of measurements and partial sensor coverage conditions.
Distributionally Robust Path Integral Control
Authors: Hyuk Park, Duo Zhou, Grani A. Hanasusanto, Takashi Tanaka
Date: 2023-10-02 00:00:00

We consider a continuous-time continuous-space stochastic optimal control problem, where the controller lacks exact knowledge of the underlying diffusion process, relying instead on a finite set of historical disturbance trajectories. In situations where data collection is limited, the controller synthesized from empirical data may exhibit poor performance. To address this issue, we introduce a novel approach named Distributionally Robust Path Integral (DRPI). The proposed method employs distributionally robust optimization (DRO) to robustify the resulting policy against the unknown diffusion process. Notably, the DRPI scheme shows similarities with risk-sensitive control, which enables us to utilize the path integral control (PIC) framework as an efficient solution scheme. We derive theoretical performance guarantees for the DRPI scheme, which closely aligns with selecting a risk parameter in risk-sensitive control. We validate the efficacy of our scheme and showcase its superiority when compared to risk-neutral PIC policies in the absence of the true diffusion process.
Dynamic Ancillary Services: From Grid Codes to Transfer Function-Based Converter Control
Authors: Verena Häberle, Linbin Huang, Xiuqiang He, Eduardo Prieto-Araujo, Florian Dörfler
Date: 2023-10-02 00:00:00

Conventional grid-code specifications for dynamic ancillary services provision such as fast frequency and voltage regulation are typically defined by means of piece-wise linear step-response capability curves in the time domain. However, although the specification of such time-domain curves is straightforward, their practical implementation in a converter-based generation system is not immediate, and no customary methods have been developed yet. In this paper, we thus propose a systematic approach for the practical implementation of piece-wise linear time-domain curves to provide dynamic ancillary services by converter-based generation systems, while ensuring grid-code and device-level requirements to be reliably satisfied. Namely, we translate the piece-wise linear time-domain curves for active and reactive power provision in response to a frequency and voltage step change into a desired rational parametric transfer function in the frequency domain, which defines a dynamic response behavior to be realized by the converter. The obtained transfer function can be easily implemented e.g. via a PI-based matching control in the power loop of standard converter control architectures. We demonstrate the performance of our method in numerical grid-code compliance tests, and reveal its superiority over classical droop and virtual inertia schemes which may not satisfy the grid codes due to their structural limitations.
Risk-Sensitive Inhibitory Control for Safe Reinforcement Learning
Authors: Armin Lederer, Erfaun Noorani, John S. Baras, Sandra Hirche
Date: 2023-10-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Safe Reinforcement Learning

Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently received increasing attention in recent years for ensuring the safety of control. Realized using control barrier functions or predictive safety filters, these approaches can effectively ensure the satisfaction of state constraints through an online adaptation of nominal control laws, e.g., obtained through reinforcement learning. While the focus of these realizations of inhibitory control has been on risk-neutral formulations, human studies have shown a tight link between response inhibition and risk attitude. Inspired by this insight, we propose a flexible, risk-sensitive method for inhibitory control. Our method is based on a risk-aware condition for value functions, which guarantees the satisfaction of state constraints. We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success. By enforcing the derived safety conditions online using the learned value function, risk-sensitive inhibitory control is effectively achieved. The effectiveness of the developed control scheme is demonstrated in simulations.
Feature-Driven Strategies for Trading Wind Power and Hydrogen
Authors: Emil Helgren, Jalal Kazempour, Lesia Mitridati
Date: 2023-10-02 00:00:00

This paper develops a feature-driven model for hybrid power plants, enabling them to exploit available contextual information such as historical forecasts of wind power, and make optimal wind power and hydrogen trading decisions in the day-ahead stage. For that, we develop different variations of feature-driven linear policies, including a variation where policies depend on price domains, resulting in a price-quantity bidding curve. In addition, we propose a real-time adjustment strategy for hydrogen production. Our numerical results show that the final profit obtained from our proposed feature-driven trading mechanism in the day-ahead stage together with the real-time adjustment strategy is very close to that in an ideal benchmark with perfect information.
A Dynamic Macroscopic Framework for Pricing of Ride-hailing Services with an Optional Bus Lane Access for Pool Vehicles
Authors: Lynn Fayed, Gustav Nilsson, Nikolas Geroliminis
Date: 2023-10-02 00:00:00
Tasks: Model Predictive Control

On-demand trip sharing is an efficient solution to mitigate the negative impact e-hailing has on congestion. It motivates platform operators to reduce their fleet size, and serves the same demand level with a lower effective distance traveled. Users nevertheless prefer to travel solo and for shorter distances despite the fare discount they receive. By offering them the choice to pool and travel in high occupancy dedicated bus lanes, we provide them with a larger incentive to share their rides, yet this creates additional bus delays. In this work, we develop dynamic feedback-based control schemes that adjust the price gap between solo and pool trips to improve multi-modal delays. First, we develop a modal- and space-dependent aggregate model for private vehicles, ride-pooling, and buses, and we use this model to test different control strategies. To minimize the error between the target and actual speeds in the bus network, we design a PI controller and show that by adjusting pool trip fares, we can, with little input data, minimize this error. We also put forward a Model Predictive Control (MPC) framework to minimize the total Passenger Hours Traveled (PHT) and Waiting Times (WT) for the different travelers. Moreover, we show how the MPC framework can be utilized to impose a minimum speed in dedicated bus lanes to ensure that the buses operate on schedule. The results mark the possibility of improving the overall network conditions by incentivizing or discouraging pooling in the vehicle or bus network.
Neural Fiber Activation in Unipolar vs Bipolar Deep Brain Stimulation
Authors: Anna Franziska Frigge, Alexander Medvedev, Elena Jiltsova, Dag Nyholm
Date: 2023-10-02 00:00:00

Deep Brain Stimulation (DBS) is an established and powerful treatment method in various neurological disorders. It involves chronically delivering electrical pulses to a certain stimulation target in the brain in order to alleviate the symptoms of a disease. Traditionally, the effect of DBS on neural tissue has been modeled based on the geometrical intersection of the static Volume of Tissue Activated (VTA) and the stimulation target. Recent studies suggest that the Dentato-Rubro-Thalamic Tract (DRTT) may serve as a potential common underlying stimulation target for tremor control in Essential Tremor (ET). However, clinical observations highlight that the therapeutic effect of DBS, especially in ET, is strongly influenced by the dynamic DBS parameters such as pulse width and frequency, as well as stimulation polarity. This study introduces a computational model to elucidate the effect of the stimulation signal shape on the DRTT under neural input. The simulation results suggest that achieving a specific pulse amplitude threshold is necessary before eliciting the therapeutic effect through adjustments in pulse widths and frequencies becomes feasible. Longer pulse widths proved more likely to induce firing, thus requiring a lower stimulation amplitude. Additionally, the modulation effect of bipolar configurations on neural traffic was found to vary significantly depending on the chosen stimulation polarity and the direction of neural traffic. Further, bipolar configurations demonstrated the ability to selectively influence firing patterns in different fiber tracts.
Position Sensing Errors in Synchronous Motor Drives
Authors: Prerit Pramod
Date: 2023-10-02 00:00:00
Tasks: Position

Non-ideal position estimation results in degraded performance of synchronous motor drive systems due to reduction of the average capability of the drive as well as torque harmonics of different orders. The signature and extent of the performance degradation is further dependent, quite significantly, on the current control architecture, i.e., feedforward or feedback control, employed. This paper presents a comprehensive analysis of non-idealities or errors in position estimation and their effects on the control performance of synchronous motor drives. Analytical models capturing the error in various signals caused by position sensing errors in the drive system for different control architectures are presented and are validated with simulation and experimental results on a prototype permanent magnet synchronous motor drive.
Performance Analysis of Synchronous Motor Drives under Concurrent Errors in Position and Current Sensing
Authors: Prerit Pramod
Date: 2023-10-02 00:00:00
Tasks: Position

Field oriented control of permanent magnet synchronous motor drives involves the closed-loop regulation of currents in the synchronous reference frame. The current feedback is directly affected by errors in both position and stationary frame current measurements. This paper presents the exact analytical expression for estimated synchronous frame currents under simultaneous errors in both sensors along with a detailed analysis of the incorrect estimation on the closed-loop current control performance.
Regulating CPU Temperature With Thermal-Aware Scheduling Using a Reduced Order Learning Thermal Model
Authors: Anthony Dowling, Lin Jiang, Ming-Cheng Cheng, Yu Liu
Date: 2023-10-02 00:00:00
Tasks: CPU, Scheduling

Modern real-time systems utilize considerable amounts of power while executing computation-intensive tasks. The execution of these tasks leads to significant power dissipation and heating of the device. It therefore results in severe thermal issues like temperature escalation, high thermal gradients, and excessive hot spot formation, which may result in degrading chip performance, accelerating device aging, and premature failure. Thermal-Aware Scheduling (TAS) enables optimization of thermal dissipation to maintain a safe thermal state. In this work, we implement a new TAS algorithm, POD-TAS, which manages the thermal behavior of a multi-core CPU based on a defined set of states and their transitions. We compare the performances of a dynamic RC thermal circuit simulator (HotSpot) and a reduced order Proper Orthogonal Decomposition (POD)-based thermal model and we select the latter for use in our POD-TAS algorithm. We implement a novel simulation-based evaluation methodology to compare TAS algorithms. This methodology is used to evaluate the performance of the proposed POD-TAS algorithm. Additionally, we compare the performance of a state of the art TAS algorithm, RT-TAS, to our proposed POD-TAS algorithm. Furthermore, we utilize the COMBS benchmark suite to provide CPU workloads for task scheduling. Our experimental results on a multi-core processor using a set of 4 benchmarks demonstrate that the proposed POD-TAS method can improve thermal performance by decreasing the peak thermal variance by 53.0% and the peak chip temperature of 29.01%. Using a set of 8 benchmarks, the comparison of the two algorithms shows a decrease of 29.57% in the peak spatial variance of the chip temperature and 26.26% in the peak chip temperature. We also identify several potential future research directions.
Robust Feedback Control of Power Systems with Solar Plants and Composite Loads
Authors: Muhammad Nadeem, MirSaleh Bahavarnia, Ahmad F. Taha
Date: 2023-10-01 00:00:00

Due to the rapid developments in synchronized measurement technologies, there exist enormous opportunities to attenuate disturbances in future power grids with high penetration of renewables and complex load demands. To that end, this paper investigates the effectiveness of new robust feedback controllers for interconnected power systems with advanced power electronics-based models of photovoltaic (PV) power plants, composite load dynamics, and detailed higher-order synchronous generator models. Specifically, we design new, advanced control-theoretic wide-area controllers to improve the transient stability of nonlinear differential-algebraic models. Thorough simulation studies are carried out to assess the performance of the proposed controllers. Several fundamental questions on the proposed controllers' computational complexity and disturbance attenuation performance are raised and addressed. Simulation results demonstrate that with the proposed controllers as a secondary control layer, the overall transient stability and system robustness against load and renewables disturbances/uncertainties can be significantly improved compared to the state-of-the-art.