pwc-archive/papers-with-abstracts huggingface dataset:
Multiplexing Power Converters for Cost-Effective and Flexible Soft Open Points
Authors: Matthew Deakin
Date: 2023-04-25 00:00:00

The feasible set of real powers that can be transferred by a three-terminal Soft Open Point (SOP) can be increased by selecting non-uniform power ratings for each of the three ac/dc legs of the SOP, then connecting a multi-terminal switch (multiplexer) to the ac side of each of those converters to facilitate reconfiguration. This paper generalizes this concept, considering the real and reactive power that n multiplexed ac/dc converters can transfer at an m-feeder bus. The performance of the device is studied numerically for a number of ac/dc sizing strategies through the volume of the feasible set of power transfers (the `capability chart volume', CCV) and distribution system loss reduction benefits (as an exemplar network service). Upper bounds on device performance are defined by considering the performance of a novel, idealised SOP consisting of a continuum of infinitesimal reconfigurable converters. Results demonstrate that the CCV can be more than doubled, with 99% of the relative performance improvement of the idealised converter achieved with designs consisting of as few as four converters. SOP equipment costs reductions of 24% are reported, with it concluded that reconfigurable, judiciously sized ac/dc legs can yield flexible and lower cost SOPs than conventional, hard-wired approaches.
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
Authors: Haneya Naeem Qureshi, Usama Masood, Marvin Manalastas, Syed Muhammad Asad Zaidi, Hasan Farooq, Julien Forgeat, Maxime Bouton, Shruti Bothe, Per Karlsson, Ali Rizwan, Ali Imran
Date: 2023-04-24 00:00:00
Tasks: Few-Shot Learning, Matrix Completion, Transfer Learning

The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of training data. We present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, few-shot learning, simulators, and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different network parameters and geometries. An overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms for real data access. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation.
Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems
Authors: Jingwei Dong, Yucheng Liao, Haiwei Xie, Jochen Cremer, Peyman Mohajerin Esfahani
Date: 2023-04-24 00:00:00
Tasks: Fault Detection

Ground fault detection in inverter-based microgrid (IBM) systems is challenging, particularly in a real-time setting, as the fault current deviates slightly from the nominal value. This difficulty is reinforced when there are partially decoupled disturbances and modeling uncertainties. The conventional solution of installing more relays to obtain additional measurements is costly and also increases the complexity of the system. In this paper, we propose a data-assisted diagnosis scheme based on an optimization-based fault detection filter with the output current as the only measurement. Modeling the microgrid dynamics and the diagnosis filter, we formulate the filter design as a quadratic programming (QP) problem that accounts for decoupling partial disturbances, robustness to non-decoupled disturbances and modeling uncertainties by training with data, and ensuring fault sensitivity simultaneously. To ease the computational effort, we also provide an approximate but analytical solution to this QP. Additionally, we use classical statistical results to provide a thresholding mechanism that enjoys probabilistic false-alarm guarantees. Finally, we implement the IBM system with Simulink and Real Time Digital Simulator (RTDS) to verify the effectiveness of the proposed method through simulations.
Co-optimization of Operational Unit Commitment and Reserve Power Scheduling for Modern Grid
Authors: Ramkrishna Mishan, Matthew Egan, Mohammed Ben-Idris, Hanif Livani
Date: 2023-04-24 00:00:00
Tasks: Scheduling

Modern power grids combine conventional generators with distributed energy resource (DER) generators in response to concerns over climate change and long-term energy security. Due to the intermittent nature of DERs, different types of energy storage devices (ESDs) must be installed to minimize unit commitment problems and accommodate spinning reserve power. ESDs have operational and resource constraints, such as charge and discharge rates or maximum and minimum state of charge (SoC). This paper proposes a linear programming (LP) optimization framework to maximize the unit-committed power for a specific optimum spinning reserve power for a particular power grid. Using this optimization framework, we also determine the total dispatchable power, non-dispatchable power, spinning reserve power, and arbitrage power using DER and ESD resource constraints. To describe the ESD and DER constraints, this paper evaluates several factors: availability, dispatchability, non-dispatchability, spinning reserve, and arbitrage factor. These factors are used as constraints in this LP optimization to determine the total optimal reserve power from the existing DERs. The proposed optimization framework maximizes the ratio of dispatchable to non-dispatchable power to minimize unit commitment problems within a specific range of spinning reserve power set to each DER. This optimization framework is implemented in the modified IEEE 34-bus distribution system, adding ten DERs in ten different buses to verify its efficacy.
A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles
Authors: James Dallas, Michael Thompson, Jonathan Y. M. Goh, Avinash Balachandran
Date: 2023-04-24 00:00:00
Tasks: Autonomous Vehicles, Computational Efficiency, Friction, Model Predictive Control

The ability to reliably maximize tire force usage would improve the safety of autonomous vehicles, especially in challenging edge cases. However, vehicle control near the limits of handling has many challenges, including robustly contending with tire force saturation, balancing model fidelity and computational efficiency, and coordinating inputs with the lower level chassis control system. This work studies Nonlinear Model Predictive Control for limit handling, specifically adapting to changing tire-road conditions and maximally allocating tire force utilization. We present a novel hierarchical framework that combines a single-track model with longitudinal weight transfer dynamics in the predictive control layer, with lateral brake distribution occurring at the chassis control layer. This vehicle model is simultaneously used in an Unscented Kalman Filter for online friction estimation. Comparative experiments on a full-scale vehicle operating on a race track at up to 95% of maximum tire force usage demonstrate the overall practical effectiveness of this approach.
Dynamic sizing of required balancing capacities: the operational approach in France
Authors: Jonathan Dumas, Viktor Terrier, Frédéric Bienvenu, Sébastien Finet, Nathalie Grisey
Date: 2023-04-24 00:00:00
Tasks: RTE

System operators employ operating reserves to deal with unexpected variations of demand and generation and guarantee the security of supply. However, they face new challenges to ensure this mission with the increasing share of renewable generation. This article focuses on the operational approach adopted by the French transmission system operator RTE for dynamically sizing the required margins in the dynamic margin monitoring strategy context. It relies on continuous forecasts of the main drivers of the uncertainties of the system imbalance. Four types of forecast errors, assumed to be independent, are considered in this approach: the errors in the wind and photovoltaic power generation, production of conventional power units, and electricity consumption. Then, the required margin is the result of comparing the global forecast error, computed as the convolution of these independent errors, with a security of supply criterion. This study presents the results of this method implemented at RTE and used in real-time operation.
Evidence based hand hygiene. Liquid or gel handrub, does it matter
Authors: Constantinos Voniatis, Szava Bansaghi, Daniel Sandor Veres, Peter Szeremy, Angela Jedlovszky Hajdu, Attila Szijarto, Tamas Haidegger
Date: 2023-04-24 00:00:00

Recent studies put under scrutiny the prevailing hand hygiene guidelines, which incorporate quantitative parameters regarding handrub volume and hand size. Understanding the criticality of complete (i.e., efficient) hand hygiene in healthcare, objectivization of hand hygiene related parameters are paramount, including the formulation of the ABHR. Senior medical students were invited, and randomly assigned to receive predetermined ABHR volumes (1.5 or 3ml). 340 participants were given equal amounts of gel and liquid on two separate hand hygiene occasions, which occurred two weeks apart. During the hand hygiene events, by employing a digital, fully automated system paired with fluorescent-traced ABHRs, disinfectant hand coverage was objectively investigated. The 1.5 ml ABHR volume (commonly applied in healthcare settings) is insufficient in either formulation, as the non-covered areas exceeded significant (5%+) of the total hand surface area. 3 ml, on the contrary, resulted in almost complete coverage (uncovered areas remained below 1.5%). Participants typically underestimated the volume which they needed to apply. While the liquid ABHR spreads better in the lower, 1.5ml volume compared to the gel, the latter was easier handled at larger volume. Drying times were 30/32s (gel and liquid formats, respectively) when 1.5ml handrub was applied, and 40/42s when 3ml was used. As the evaporation rates of the ABHR used in the study are similar to those available on the market, one can presume that the results presented in the study apply for most WHO conform ABHRs. The results show that applying 1.5ml volume was insufficient, as large part of the hand surface remained uncovered (7.0+-0.7% and 5.8+-1.0% of the hand surface in the case of gel and liquid, respectively). When 3ml handrub was applied drying times were 40/42s (gel and liquid, respectively).
Optimal Design of Neural Network Structure for Power System Frequency Security Constraints
Authors: Zhuoxuan Li, Zhongda Chu, Fei Teng
Date: 2023-04-24 00:00:00
Tasks: Computational Efficiency

Recently, frequency security is challenged by high uncertainty and low inertia in power system with high penetration of Renewable Energy Sources (RES). In the context of Unit Commitment (UC) problems, frequency security constraints represented by neural networks have been developed and embedded into the optimization problem to represent complicated frequency dynamics. However, there are two major disadvantages related to this technique: the risk of overconfident prediction and poor computational efficiency. To handle these disadvantages, novel methodologies are proposed to optimally design the neural network structure, including the use of asymmetric loss function during the training stage and scientifically selecting neural network size and topology. The effectiveness of the proposed methodologies are validated by case study which reveals the improvement of conservativeness and mitigation of computation performance issues.
Method for Specifying Location Data Requirements for Intralogistics Applications
Authors: Jakob Schyga, Markus Knitt, Johannes Hinckeldeyn, Jochen Kreutzfeldt
Date: 2023-04-24 00:00:00
Tasks: Indoor Localization

Various applications leverage location data to increase transparency, efficiency, and safety in intralogistics. There are several properties of location data, such as the data's degrees of freedom, system latency, update rate, or accuracy. To select a suitable indoor localization system, corresponding data requirements must be derived by analyzing the considered application. To date, the dependencies of the system performance and location data requirements have not been satisfactorily described in the literature. Thus, no method exists to adequately derive location data requirements. For intralogistics, such a method is of particular relevance due to the high-cost sensitivity and heterogeneity of partially safety-relevant indoor localization applications. To fill this gap, a method for selecting and quantifying location data requirements for the application in intralogistics is presented in this work, creating substantial added value for warehouse managers and system integrators. The method is based on a spatial model that is built on the premise that location data is used to determine the presence or absence of an entity in a multidimensional interest space. The usage of the method is demonstrated in an exemplary case study for the application of 'Automated Pallet Booking'.
Approximation- and Chattering-free Quasi Sliding Mode Control for Unknown Systems
Authors: Pankaj K Mishra, Pushpak Jagtap
Date: 2023-04-21 00:00:00

Motivated by the concept of Quasi-Sliding Mode (QSM) in discrete-time systems, this paper presents a novel approach that relaxes the requirement of ubiquitous exact sliding motion in continuous-time systems, aiming to achieve an approximation- and chattering-free quasi-sliding mode controller (QSMC). The proposed QSMC provides robust and chattering-free control for an unknown nonlinear system without the need for intricate control methods, learning agents, or system identification tools. Furthermore, comprehensive simulation studies have been conducted to validate the effectiveness of the proposed strategy.
Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources
Authors: Xiaoling Chen, Cory Miller, Mithun Goutham, Prasad Dev Hanumalagutti, Rachel Blaser, Stephanie Stockar
Date: 2023-04-23 00:00:00
Tasks: energy management, Management, Model Predictive Control

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50\% of the flexible loads, which is possible due to the use of the stationary energy storage.
Hier-RTLMP: A Hierarchical Automatic Macro Placer for Large-scale Complex IP Blocks
Authors: Andrew B. Kahng, Ravi Varadarajan, Zhiang Wang
Date: 2023-04-23 00:00:00

In a typical RTL to GDSII flow, floorplanning or macro placement is a critical step in achieving decent quality of results (QoR). Moreover, in today's physical synthesis flows (e.g., Synopsys Fusion Compiler or Cadence Genus iSpatial), a floorplan .def with macro and IO pin placements is typically needed as an input to the front-end physical synthesis. Recently, with the increasing complexity of IP blocks, and in particular with auto-generated RTL for machine learning (ML) accelerators, the number of hard macros in a single RTL block can easily run into the several hundreds. This makes the task of generating an automatic floorplan (.def) with IO pin and macro placements for front-end physical synthesis even more critical and challenging. The so-called peripheral approach of forcing macros to the periphery of the layout is no longer viable when the ratio of the sum of the macro perimeters to the floorplan perimeter is large, since this increases the required stacking depth of macros. In this paper, we develop a novel multilevel physical planning approach that exploits the hierarchy and dataflow inherent in the design RTL, and describe its realization in a new hierarchical macro placer, Hier-RTLMP. Hier-RTLMP borrows from traditional approaches used in manual system-on-chip (SoC) floorplanning to create an automatic macro placement for use with large IP blocks containing very large numbers of hard macros. Empirical studies demonstrate substantial improvements over the previous RTL-MP macro placement approach, and promising post-route improvements relative to a leading commercial place-and-route tool.
Introducing the Perturbative Solution of the Inter-Channel Stimulated Raman Scattering in Single-Mode Optical Fibers
Authors: Andrea D'Amico, Giacomo Borraccini, Vittorio Curri
Date: 2023-04-23 00:00:00

The continuously increasing IP data traffic demand, with geometrical growth rate exceeding 26%, requires a large transmission capacity increment from the fiber optical infrastructure. As the deploy of new fiber cables requires extensive investments, the development of multi-band amplifiers and transceivers, already available as prototypes, is progressively considered towards the entire low-loss single-mode bandwidth beyond the 5 THz C-band. In this perspective, an adequate handling of the variations along the frequency of the fiber physical features becomes crucial for the fiber propagation modeling in multi-band wavelength division multiplexing (WDM) channel comb transmission scenarios. In particular, the inter-channel stimulated Raman scattering (SRS) is the fundamental inter-band effect in this context. The SRS effect on the WDM comb propagated through a single-mode optical fiber is described by a set of ordinary differential equations (ODEs). To date, an exact solution of the SRS ODEs has not been proposed, and in the literature numerical solutions or approximations have been considered in order to take into account this effect. In this work, a perturbative solution of the SRS ODEs is presented enabling an efficient trade-off between the target accuracy and the computational time. Considering a C+L+S transmission scenario, the perturbative expansion up to the 2nd order ensures an excellent accuracy. Whereas, in an U-to-E transmission scenario, the 3rd order is required in order to reach an equivalent accuracy.
Control of Discrete-Time LTI Systems using Stochastic Ensemble Systems
Authors: Nirabhra Mandal, Mohammad Khajenejad, Sonia Martinez
Date: 2023-04-23 00:00:00

In this paper, we study the control properties of a new class of stochastic ensemble systems that consists of families of random variables. These random variables provide an increasingly good approximation of an unknown discrete, linear-time invariant (DLTI) system, and can be obtained by a standard, data-driven procedure. Our first result relates the reachability properties of the stochastic ensemble system with that of the limiting DLTI system. We then provide a method to combine the control inputs obtained from the stochastic ensemble systems to compute a control input for the DLTI system. Later, we deal with a particular kind of stochastic ensemble system generated from realizing Bernoulli random variables. For this, we characterize the variance of the computed state and control. We also do the same for a situation where the data is updated sequentially in a streaming fashion. We illustrate the results numerically in various simulation examples.
Moving-horizon False Data Injection Attack Design against Cyber-Physical Systems
Authors: Yu Zheng, Sridhar Babu Mudhangulla, Olugbenga Moses Anubi
Date: 2023-04-23 00:00:00

Systematic attack design is essential to understanding the vulnerabilities of cyber-physical systems (CPSs), to better design for resiliency. In particular, false data injection attacks (FDIAs) are well-known and have been shown to be capable of bypassing bad data detection (BDD) while causing targeted biases in resulting state estimates. However, their effectiveness against moving horizon estimators (MHE) is not well understood. In fact, this paper shows that conventional FDIAs are generally ineffective against MHE. One of the main reasons is that the moving window renders the static FDIA recursively infeasible. This paper proposes a new attack methodology, moving-horizon FDIA (MH-FDIA), by considering both the performance of historical attacks and the current system's status. Theoretical guarantees for successful attack generation and recursive feasibility are given. Numerical simulations on the IEEE-14 bus system further validate the theoretical claims and show that the proposed MH-FDIA outperforms state-of-the-art counterparts in both stealthiness and effectiveness. In addition, \textcolor{blue}{an experiment on} a path-tracking control system of an autonomous vehicle shows the feasibility of the MH-FDIA in real-world nonlinear systems.
Battery Capacity Knee-Onset Identification and Early Prediction Using Degradation Curvature
Authors: Huang Zhang, Faisal Altaf, Torsten Wik
Date: 2023-04-23 00:00:00
Tasks: Time Series

Abrupt capacity fade can have a significant impact on performance and safety in battery applications. To address concerns arising from possible knee occurrence, this work aims for a better understanding of their cause by introducing a new definition of capacity knees and their onset. A curvature-based identification of a knee and its onset is proposed, which relies on the discovery of a distinctly fluctuating behavior in the transition between an initial and a final stable acceleration of the degradation. The method is validated on experimental degradation data of two different battery chemistries, synthetic degradation data, and is also benchmarked to the state-of-the-art knee identification method in the literature. The results demonstrate that our proposed method could successfully identify capacity knees when the state-of-the-art knee identification method failed. Furthermore, a significantly strong correlation is found between knee and end of life (EoL) and almost equally strong between knee onset and EoL. As the method does not require the full capacity fade curve, this opens up online knee-onset identification as well as knee and EoL prediction.