pwc-archive/papers-with-abstracts huggingface dataset:
Functional Specifications and Testing Requirements of Grid-Forming Type-IV Offshore Wind Power
Authors: Sulav Ghimire, Gabriel M. G. Guerreiro, Kanakesh V. K., Emerson D. Guest, Kim H. Jensen, Guangya Yang, Xiongfei Wang
Date: 2024-05-08 00:00:00

Throughout the past few years, various transmission system operators (TSOs) and research institutes have defined several functional specifications for grid-forming (GFM) converters via grid codes, white papers, and technical documents. These institutes and organisations also proposed testing requirements for general inverter-based resources (IBRs) and specific GFM converters. This paper initially reviews functional specifications and testing requirements from several sources to create an understanding of GFM capabilities in general. Furthermore, it proposes an outlook of the defined GFM capabilities, functional specifications, and testing requirements for offshore wind power plant (OF WPP) applications from an original equipment manufacturer (OEM) perspective. Finally, this paper briefly establishes the relevance of new testing methodologies for equipment-level certification and model validation, focusing on GFM functional specifications.
Stability And Uncertainty Propagation In Power Networks: A Lyapunov-based Approach With Applications To Renewable Resources Allocation
Authors: Mohamad Kazma, Ahmad F. Taha
Date: 2024-05-08 00:00:00

The rapid increase in the integration of intermittent and stochastic renewable energy resources (RER) introduces challenging issues related to power system stability. Interestingly, identifying grid nodes that can best support stochastic loads from RER, has gained recent interest. Methods based on Lyapunov stability are commonly exploited to assess the stability of power networks. These strategies approach quantifying system stability while considering: (i) simplified reduced order power system models that do not model power flow constraints, or (ii) data-driven methods that are prone to measurement noise and hence can inaccurately depict stochastic loads as system instability. In this paper, while considering a nonlinear differential algebraic equation (NL-DAE) model, we introduce a new method for assessing the impact of uncertain renewable power injections on the stability of power system nodes/buses. The identification of stable nodes informs the operator/utility on how renewables injections affect the stability of the grid. The proposed method is based on optimizing metrics equivalent to the Lyapunov spectrum of exponents; its underlying properties result in a computationally efficient and scalable stable node identification algorithm for renewable energy resources allocation. The developed framework is studied on various standard power networks.
A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography
Authors: Zhangyu Xu, Majid Khazaee, Nhan Duy Truong, Deniel Havenga, Armin Nikpour, Arman Ahnood, Omid Kavehei
Date: 2024-05-08 00:00:00

Endovascular brain-computer interfaces (eBCIs) offer a minimally invasive way to connect the brain to external devices, merging neuroscience, engineering, and medical technology. Achieving wireless data and power transmission is crucial for the clinical viability of these implantable devices. Typically, solutions for endovascular electrocorticography (ECoG) include a sensing stent with multiple electrodes (e.g. in the superior sagittal sinus) in the brain, a subcutaneous chest implant for wireless energy harvesting and data telemetry, and a long (tens of centimetres) cable with a set of wires in between. This long cable presents risks and limitations, especially for younger patients or those with fragile vasculature. This work introduces a wireless and leadless telemetry and power transfer solution for endovascular ECoG. The proposed solution includes an optical telemetry module and a focused ultrasound (FUS) power transfer system. The proposed system can be miniaturised to fit in an endovascular stent. Our solution uses optical telemetry for high-speed data transmission (over 2 Mbit/s, capable of transmitting 41 ECoG channels at a 2 kHz sampling rate and 24-bit resolution) and the proposed power transferring scheme provides up to 10mW power budget into the site of the endovascular implants under the safety limit. Tests on bovine tissues confirmed the system's effectiveness, suggesting that future custom circuit designs could further enhance eBCI applications by removing wires and auxiliary implants, minimising complications.
Visually Guided Swarm Motion Coordination via Insect-inspired Small Target Motion Reactions
Authors: Md Arif Billah, Imraan A. Faruque
Date: 2024-05-07 00:00:00

Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), collective group motion can be achieved. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.
Asymmetry of Frequency Distribution in Power Systems: Sources, Estimation, Impact and Control
Authors: Taulant Kerci, Federico Milano
Date: 2024-05-07 00:00:00

This paper analyses an emerging real-world phenomena in inverter-based renewable-dominated power systems, namely, asymmetry of frequency distribution. The paper first provides a rationale on why asymmetry reduces the "quality" of the frequency control and system operation. Then it provides qualitative theoretical insights that explain asymmetry in terms of the nonlinearity of real-world power systems and associated models. In particular network losses and pitch angle-based frequency control of wind power plants are discussed. Then the paper proposes a nonlinear compensation control to reduce the asymmetry as well as a statistical metric based on the frequency probability distribution to quantify the level of asymmetry in a power system. Real-world data obtained from the Irish and Australian transmission systems serve to support the theoretical appraisal, whereas simulations based on an IEEE benchmark system show the effectiveness of the proposed nonlinear compensation. The case study also shows that, while automatic generation control reduces asymmetry, frequency control limits and droop-based frequency support provided by wind generation using a tight deadband of 15 mHz, namely active power control, leads to a significant increase in the asymmetry of the frequency probability distribution.
Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation
Authors: Rodrigo A. González, Paulina Quintanilla
Date: 2024-05-07 00:00:00

This study presents a grey-box recursive identification technique to estimate key parameters in a mineral flotation process across two scenarios. The method is applied to a nonlinear physics-based dynamic model validated at a laboratory scale, allowing real-time updates of two model parameters, n and C, in response to changing conditions. The proposed approach effectively adapts to process variability and allows for continuous adjustments based on operational fluctuations, resulting in a significantly improved estimation of concentrate grade - one key performance indicator. In Scenario 1, parameters n and C achieved fit metrics of 97.99 and 96.86, respectively, with concentrate grade estimations improving from 75.1 to 98.69 using recursive identification. In Scenario 2, the fit metrics for n and C were 96.27 and 95.48, respectively, with the concentrate grade estimations increasing from 96.27 to 99.45 with recursive identification. The results demonstrate the effectiveness of the proposed grey-box recursive identification method in accurately estimating parameters and predicting concentrate grade in a mineral flotation process.
A Weighted Least-Squares Method for Non-Asymptotic Identification of Markov Parameters from Multiple Trajectories
Authors: Jiabao He, Cristian R. Rojas, Håkan Hjalmarsson
Date: 2024-05-07 00:00:00

Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step, including subspace identification and multi-step least-squares algorithms, such as Weighted Null-Space Fitting. Recently, there has been an increasing interest in non-asymptotic analysis of estimation algorithms. In this contribution we identify the Markov parameters using weighted least-squares and present non-asymptotic analysis for such estimator. To cover both stable and unstable systems, multiple trajectories are collected. We show that with the optimal weighting matrix, weighted least-squares gives a tighter error bound than ordinary least-squares for the case of non-uniformly distributed measurement errors. Moreover, as the optimal weighting matrix depends on the system's true parameters, we introduce two methods to consistently estimate the optimal weighting matrix, where the convergence rate of these estimates is also provided. Numerical experiments demonstrate improvements of weighted least-squares over ordinary least-squares in finite sample settings.
Weighted Least-Squares PARSIM
Authors: Jiabao He, Cristian R. Rojas, Håkan Hjalmarsson
Date: 2024-05-07 00:00:00
Tasks: State Space Models

Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them involve a common intermediate step, that is to estimate the range space of the extended observability matrix. In this contribution, an optimized version of the parallel and parsimonious SIM (PARSIM), PARSIM\textsubscript{opt}, is proposed by using weighted least-squares. It not only inherits all the benefits of PARSIM but also attains the best linear unbiased estimator for the above intermediate step. Furthermore, inspired by SIMs based on the predictor form, consistent estimates of the optimal weighting matrix for weighted least-squares are derived. Essential similarities, differences and simulated comparisons of some key SIMs related to our method are also presented.
Long-term usage of the off-grid photovoltaic system with lithium-ion battery-based energy storage system on high mountains: A case study in Payiun Lodge on Mt. Jade in Taiwan
Authors: Hsien-Ching Chung
Date: 2024-05-07 00:00:00

Energy supply on high mountains remains an open issue since grid connection is unavailable. In the past, diesel generators with lead-acid battery energy storage systems (ESSs) are applied in most cases. Recently, photovoltaic (PV) system with lithium-ion (Li-ion) battery ESS is an appropriate method for solving this problem in a greener way. In 2016, an off-grid PV system with Li-ion battery ESS has been installed in Paiyun Lodge on Mt. Jade (the highest lodge in Taiwan). After operation for more than 7 years, the aging problem of the whole electric power system becomes a critical issue for long-term usage. In this work, a method is established for analyzing the massive energy data (over 7 million rows) and estimating the health of the Li-ion battery system, such as daily operation patterns as well as C-rate, temperature, and accumulated energy distributions. The accomplished electric power improvement project dealing with the power system aging is reported. Based on the long-term usage experience, a simple cost analysis model between lead-acid and Li-ion battery systems is built, explaining that the expensive Li-ion batteries can compete with the cheap lead-acid batteries for long-term usage on high mountains. This case study provides engineers and researchers a fundamental understanding of the long-term usage of off-grid PV ESSs and engineering on high mountains.
A 49.8mm2 Fully Integrated, 1.5m Transmission-Range, High-Data-Rate IR-UWB Transmitter for Brain Implants
Authors: Cong Ding, Mingxiang Gao, Anja K. Skrivervik, Mahsa Shoaran
Date: 2024-05-07 00:00:00

To address the challenge of extending the transmission range of implantable TXs while also minimizing their size and power consumption, this paper introduces a transcutaneous, high data-rate, fully integrated IR-UWB transmitter that employs a novel co-designed power amplifier (PA) and antenna interface for enhanced performance. With the co-designed interface, we achieved the smallest footprint of 49.8mm2 and the longest transmission range of 1.5m compared to the state-of-the-art IR-UWB TXs.
Derisking of subsynchronous torsional oscillations in power systems with conventional and inverter-based generation
Authors: Nicolas Bonafé, Julian Freytes, Hani Saad
Date: 2024-05-07 00:00:00

This article proposes an application of a derisking methodology of subsynchronous torsional oscillations considering a realistic use case. The main objective is to summarize and draft a synthetic paper clarifying the complete methodology highlighting the main information needed step-by-step. For exemplification, a real model from a decommissioned oil power plant is adopted, where a fictitious high voltage direct current power link is connected. In this article, stress is laid on details of the application of the derisking methods: the unit interaction factor and the complex torque coefficients method. Then, the different steps to obtain results are explicitly explained. Moreover, the design and tuning process of supplementary subsynchronous damping controller is discussed. This mitigation section uses minimal information to correctly damp the unstable oscillations, as one would expect from industrial projects where the data sharing may be limited. Finally, the resources needed to perform each step of the study were summarized.
Latency and Energy Minimization in NOMA-Assisted MEC Network: A Federated Deep Reinforcement Learning Approach
Authors: Arian Ahmadi, Anders Høst-Madsen, Zixiang Xiong
Date: 2024-05-07 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing

Multi-access edge computing (MEC) is seen as a vital component of forthcoming 6G wireless networks, aiming to support emerging applications that demand high service reliability and low latency. However, ensuring the ultra-reliable and low-latency performance of MEC networks poses a significant challenge due to uncertainties associated with wireless links, constraints imposed by communication and computing resources, and the dynamic nature of network traffic. Enabling ultra-reliable and low-latency MEC mandates efficient load balancing jointly with resource allocation. In this paper, we investigate the joint optimization problem of offloading decisions, computation and communication resource allocation to minimize the expected weighted sum of delivery latency and energy consumption in a non-orthogonal multiple access (NOMA)-assisted MEC network. Given the formulated problem is a mixed-integer non-linear programming (MINLP), a new multi-agent federated deep reinforcement learning (FDRL) solution based on double deep Q-network (DDQN) is developed to efficiently optimize the offloading strategies across the MEC network while accelerating the learning process of the Internet-of-Thing (IoT) devices. Simulation results show that the proposed FDRL scheme can effectively reduce the weighted sum of delivery latency and energy consumption of IoT devices in the MEC network and outperform the baseline approaches.
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach
Authors: Kui Wang, Changyang She, Zongdian Li, Tao Yu, Yonghui Li, Kei Sakaguchi
Date: 2024-05-07 00:00:00
Tasks: Autonomous Driving, Reinforcement Learning (RL)

Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs). This research proposes an innovative roadside unit (RSU)-assisted cooperative maneuvering system aimed at enhancing road safety and traveling efficiency at intersections for CAVs. We utilize RSUs for real-time traffic data acquisition and train an offline reinforcement learning (RL) algorithm based on human driving data. Evaluation results obtained from hardware-in-loop autonomous driving simulations show that our approach employing the twin delayed deep deterministic policy gradient and behavior cloning (TD3+BC), achieves performance comparable to state-of-the-art autonomous driving systems in terms of safety measures while significantly enhancing travel efficiency by up to 17.38% in intersection areas. This paper makes a pivotal contribution to the field of intelligent transportation systems, presenting a breakthrough solution for improving urban traffic flow and safety at intersections.
An Electronically Tunable 28-34 GHz 2-D Steerable Leaky Wave Antenna
Authors: Mahdi Alesheikh, Md Hedayatullah Maktoomi, Soheil Saadat, Hamidreza Aghasi
Date: 2024-05-07 00:00:00

In this paper, a 2-D beam steering mm-wave antenna based on the leaky wave configuration is presented. Microstrip leaky wave antennas are known to exhibit beam rotation by changing the frequency. In this work, the microstrip leaky wave antenna is adopted and co-integrated with electronically tunable board components that periodically load the antenna. By independent control of variable capacitors and diodes, single-frequency 2-D beam steering across the bandwidth is achieved. The proposed antenna is fabricated in Rogers printed circuit board technologies and the simulation results exhibit a peak realized gain of 8 dBi, radiation bandwidth of 28-34 GHz, radiation efficiency of more than 80%, and more than 90$^\circ$ and 70$^\circ$ of beam rotation in the $\phi$ and $\theta$ directions.
Merging Parameter Estimation and Classification Using LASSO
Authors: Le Wang, Ying Wang, Yu Qiu, Mian Li, Håkan Hjalmarsson
Date: 2024-05-06 00:00:00
Tasks: Classification, parameter estimation

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not \textit{a priori} be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness.
What doesn't kill Gaia makes her stronger
Authors: Rudy Arthur, Arwen E. Nicholson, Nathan J. Mayne
Date: 2024-05-08 00:00:00

Life on Earth has experienced numerous upheavals over its approximately 4 billion year history. In previous work we have discussed how interruptions to stability lead, on average, to increases in habitability over time, a tendency we called Entropic Gaia. Here we continue this exploration, working with the Tangled Nature Model of co-evolution, to understand how the evolutionary history of life is shaped by periods of acute environmental stress. We find that while these periods of stress pose a risk of complete extinction, they also create opportunities for evolutionary exploration which would otherwise be impossible, leading to more populous and stable states among the survivors than in alternative histories without a stress period. We also study how the duration, repetition and number of refugia into which life escapes during the perturbation affects the final outcome. The model results are discussed in relation to both Earth history and the search for alien life.
Topological conditions drive stability in meta-ecosystems
Authors: Johannes Nauta, Manlio De Domenico
Date: 2024-05-08 00:00:00
Tasks: Diversity

On a global level, ecological communities are being perturbed at an unprecedented rate by human activities and environmental instabilities. Yet, we understand little about what factors facilitate or impede long-term persistence of these communities. While observational studies indicate that increased biodiversity must, somehow, be driving stability, theoretical studies have argued the exact opposite viewpoint instead. This encouraged many researchers to participate in the ongoing diversity-stability debate. Within this context, however, there has been a severe lack of studies that consider spatial features explicitly, even though nearly all habitats are spatially embedded. To this end, we study here the linear stability of meta-ecosystems on networks that describe how discrete patches are connected by dispersal between them. By combining results from random-matrix theory and network theory, we are able to show that there are three distinct features that underlie stability: edge density, tendency to triadic closure, and isolation or fragmentation. Our results appear to further indicate that network sparsity does not necessarily reduce stability, and that connections between patches are just as, if not more, important to consider when studying the stability of large ecological systems.
Neurocomputational Phenotypes in Female and Male Autistic Individuals
Authors: Michelle McCleod, Sean Borneman, Evie Malaia
Date: 2024-05-07 00:00:00
Tasks: EEG

Autism Spectrum Disorder (ASD) is characterized by an altered phenotype in social interaction and communication. Additionally, autism typically manifests differently in females as opposed to males: a phenomenon that has likely led to long-term problems in diagnostics of autism in females. These sex-based differences in communicative behavior may originate from differences in neurocomputational properties of brain organization. The present study looked to examine the relationship between one neurocomputational measure of brain organization, the local power-law exponent, in autistic vs. neurotypical, as well as male vs. female participants. To investigate the autistic phenotype in neural organization based on biological sex, we collected continuous resting-state EEG data for 19 autistic young adults (10 F), and 23 controls (14 F), using a 64-channel Net Station EEG acquisition system. The data was analyzed to quantify the 1/f power spectrum. Correlations between power-law exponent and behavioral measures were calculated in a between-group (female vs. male; autistic vs. neurotypical) design. On average, the power-law exponent was significantly greater in the male ASD group than in the female ASD group in fronto-central regions. The differences were more pronounced over the left hemisphere, suggesting neural organization differences in regions responsible for language complexity. These differences provide a potential explanation for behavioral variances in female vs. male autistic young adults.