pwc-archive/papers-with-abstracts huggingface dataset:
A reference frame-based microgrid primary control for ensuring global convergence to a periodic orbit
Authors: Xinyuan Jiang, Constantino M. Lagoa, Daning Huang, Yan Li
Date: 2024-08-01 00:00:00

Electric power systems with growing penetration of renewable generation face problems of frequency oscillation and increased uncertainty as the operating point may veer close to instability. Traditionally the stability of these systems is studied either in terms of local stability or as an angle synchronization problem under the simplifying assumption that decouples the amplitude along with all dissipations. Without the simplifying assumption, however, the steady state being studied is basically a limit cycle with the convergence of its orbit in question. In this paper we present an analysis of the orbital stability of a microgrid integrating the proposed type of distributed generation controller, whose internal reference voltage arises from the rotation of the reference frame much like a rotating machine. We utilize the shifted passivity framework to prove that, with sufficient dissipation, such system is globally convergent to a nontrivial orbit. This is the first global stability result for the limit cycle of such system in the full state space, which provides new insight into the synchronization mechanism as well as how dissipation plays a role in the orbital stability. The proposed controller is verified with a test microgrid, demonstrating its stability and transient smoothness compared to the standard droop control.
An Extreme Value Theory Approach for Understanding Queue Length Dynamics in Adaptive Corridors
Authors: Shakib Mustavee, Pushkin Kachroo, Shaurya Agarwal
Date: 2024-08-01 00:00:00
Tasks: Management, Time Series

This paper introduces a novel approach employing extreme value theory to analyze queue lengths within a corridor controlled by adaptive controllers. We consider the maximum queue lengths of a signalized corridor consisting of nine intersections every two minutes, roughly equivalent to the cycle length. Our research shows that maximum queue lengths at all the intersections follow the extreme value distributions. To the best knowledge of the authors, this is the first attempt to characterize queue length time series using extreme value analysis. These findings are significant as they offer a mechanism to assess the extremity of queue lengths, thereby aiding in evaluating the effectiveness of the adaptive signal controllers and corridor management. Given that extreme queue lengths often precipitate spillover effects, this insight can be instrumental in preempting such scenarios.
Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form
Authors: Chris Verhoek, Roland Tóth
Date: 2024-08-01 00:00:00
Tasks: Form

We propose kernel-based approaches for the construction of a single-step and multi-step predictor of the velocity form of nonlinear (NL) systems, which describes the time-difference dynamics of the corresponding NL system and admits a highly structured representation. The predictors in turn allow to formulate completely data-driven representations of the velocity form. The kernel-based formulation that we derive, inherently respects the structured quasi-linear and specific time-dependent relationship of the velocity form. This results in an efficient multi-step predictor for the velocity form and hence for nonlinear systems. Moreover, by using the velocity form, our methods open the door for data-driven behavioral analysis and control of nonlinear systems with global stability and performance guarantees.
TERIME: An improved RIME algorithm with enhanced exploration and exploitation for robust parameter extraction of photovoltaic models
Authors: Shi-Shun Chen, Yu-Tong Jiang, Wen-Bin Chen, Xiao-Yang Li
Date: 2024-07-25 00:00:00

Parameter extraction of photovoltaic (PV) models is crucial for the planning, optimization, and control of PV systems. Although some methods using meta-heuristic algorithms have been proposed to determine these parameters, the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases. The unstable results will affect the reliable operation and maintenance strategies of PV systems. In response to this challenge, an improved rime optimization algorithm with enhanced exploration and exploitation, termed TERIME, is proposed for robust and accurate parameter identification for various PV models. Specifically, the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity. Meanwhile, a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth. The TERIME algorithm is applied to estimate the optimal parameters of the single diode model, double diode model, and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France, Photo Watt-PWP 201 and S75. According to the statistical analysis in 100 runs, the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions. All of our source codes are publicly available at https://github.com/dirge1/TERIME.
Comparative Study of Data-driven Area Inertia Estimation Approaches on WECC Power Systems
Authors: Bendong Tan, Jiangkai Peng, Ningchao Gao, Junbo Zhao, Jin Tan
Date: 2024-08-01 00:00:00

With the increasing integration of inverter-based resources into the power grid, there has been a notable reduction in system inertia, potentially compromising frequency stability. To assess the suitability of existing area inertia estimation techniques for real-world power systems, this paper presents a rigorous comparative analysis of system identification, measurement reconstruction, and electromechanical oscillation-based area inertia estimation methodologies, specifically applied to the large-scale and multi-area WECC 240-bus power system. Comprehensive results show that the system identification-based approach exhibits superior robustness and accuracy relative to its counterparts.
A Batch Update Using Multiplicative Noise Modelling for Extended Object Tracking
Authors: Christian Gramsch, Shishan Yang, Hosam Alqaderi
Date: 2024-08-01 00:00:00
Tasks: Object Tracking

While the tracking of multiple extended targets demands for sophisticated algorithms to handle the high complexity inherent to the task, it also requires low runtime for online execution in real-world scenarios. In this work, we derive a batch update for the recently introduced elliptical-target tracker called MEM-EKF*. The MEM-EKF* is based on the same likelihood as the well-established random matrix approach but is derived from the multiplicative error model (MEM) and uses an extended Kalman filter (EKF) to update the target state sequentially, i.e., measurement-by-measurement. Our batch variant updates the target state in a single step based on straightforward sums over all measurements and the MEM-specific pseudo-measurements. This drastically reduces the scaling constant for typical implementations and indeed we find a speedup of roughly 100x in our numerical experiments. At the same time, the estimation error which we measure using the Gaussian Wasserstein distance stays significantly below that of the random matrix approach in coordinated turn scenarios while being comparable otherwise.
Task-oriented and Semantics-aware Communications for Augmented Reality
Authors: Zhe Wang, Yansha Deng
Date: 2024-08-01 00:00:00

Upon the advent of the emerging metaverse and its related applications in Augmented Reality (AR), the current bit-oriented network struggles to support real-time changes for the vast amount of associated information, creating a significant bottleneck in its development. To address the above problem, we present a novel task-oriented and semantics-aware communication framework for augmented reality (TSAR) to enhance communication efficiency and effectiveness significantly. We first present an analysis of the traditional wireless AR point cloud communication framework, followed by a detailed summary of our proposed semantic information extraction within the end-to-end communication. Then, we detail the components of the TSAR framework, incorporating semantics extraction with deep learning, task-oriented base knowledge selection, and avatar pose recovery. Through rigorous experimentation, we demonstrate that our proposed TSAR framework considerably outperforms traditional point cloud communication framework, reducing wireless AR application transmission latency by 95.6% and improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.
A Novel Edge Laplacian-based Approach for Adaptive Formation Control of Uncertain Multi-agent Systems with Unified Relative Error Performance
Authors: Kun Li, Kai Zhao, Yongduan Song, Lihua Xie
Date: 2024-08-01 00:00:00

For most existing prescribed performance formation control methods, performance requirements are not directly imposed on the relative states between agents but on the consensus error, which lacks a clear physical interpretation of their solution. In this paper, we propose a novel adaptive prescribed performance formation control strategy, capable of guaranteeing prescribed performance on the relative errors, for uncertain high-order multi-agent systems under a class of directed graphs. Due to the consideration of performance constraints for relative errors, a coupled nonlinear interaction term that contains global graphic information among agents is involved in the error dynamics, leading to a fully distributed control design more difficult and challenging. Here by proposing a series of nonlinear mappings and utilizing the edge Laplacian along with Lyapunov stability theory, the presented formation control scheme exhibits the following appealing features when compared to existing results: 1) different performance requirements can be guaranteed in a unified way by solely tuning the design parameters a priori, without the need for control redesign and stability reanalysis under the proposed fixed control protocol, making the design more user-friendly and the implementation less demanding; 2) the complex and burdensome verification process for the initial constraint, often encountered in existing prescribed performance controls, is completely obviated if the performance requirements are global; and 3) nonlinear interaction is completely decoupled and the asymptotic stability of the formation manifold is ensured via using the adaptive parameter estimate technique. Finally, simulations of various performance behaviors are performed to show the efficiency of the theoretical results.
Joint Vehicle Connection and Beamforming Optimization in Digital Twin Assisted Integrated Sensing and Communication Vehicular Networks
Authors: Weihang Ding, Zhaohui Yang, Mingzhe Chen, Yuchen Liu, Mohammad Shikh-Bahaei
Date: 2024-08-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper introduces an approach to harness digital twin (DT) technology in the realm of integrated sensing and communications (ISAC) in the sixth-generation (6G) Internet-of-everything (IoE) applications. We consider moving targets in a vehicular network and use DT to track and predict the motion of the vehicles. After predicting the location of the vehicle at the next time slot, the DT designs the assignment and beamforming for each vehicle. The real time sensing information is then utilized to update and refine the DT, enabling further processing and decision-making. This model incorporates a dynamic Kalman gain, which is updated at each time slot based on the received echo signals. The state representation encompasses both vehicle motion information and the error matrix, with the posterior Cram\'er-Rao bound (PCRB) employed to assess sensing accuracy. We consider a network with two roadside units (RSUs), and the vehicles need to be allocated to one of them. To optimize the overall transmission rate while maintaining an acceptable sensing accuracy, an optimization problem is formulated. Since it is generally hard to solve the original problem, Lagrange multipliers and fractional programming are employed to simplify this optimization problem. To solve the simplified problem, this paper introduces both greedy and heuristic algorithms through optimizing both vehicle assignments and predictive beamforming. The optimized results are then transferred back to the real space for ISAC applications. Recognizing the computational complexity of the greedy and heuristic algorithms, a bidirectional long short-term memory (LSTM)-based recurrent neural network (RNN) is proposed for efficient beamforming design within the DT. Simulation results demonstrate the effectiveness of the DT-based ISAC network.
Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control
Authors: Hao Zhou, Chengming Hu, Dun Yuan, Ye Yuan, Di wu, Xue Liu, Charlie Zhang
Date: 2024-08-01 00:00:00
Tasks: Deep Reinforcement Learning, In-Context Learning, Language Modeling, Language Modelling, Large Language Model

Large language model (LLM) has recently been considered a promising technique for many fields. This work explores LLM-based wireless network optimization via in-context learning. To showcase the potential of LLM technologies, we consider the base station (BS) power control as a case study, a fundamental but crucial technique that is widely investigated in wireless networks. Different from existing machine learning (ML) methods, our proposed in-context learning algorithm relies on LLM's inference capabilities. It avoids the complexity of tedious model training and hyper-parameter fine-tuning, which is a well-known bottleneck of many ML algorithms. Specifically, the proposed algorithm first describes the target task via formatted natural language, and then designs the in-context learning framework and demonstration examples. After that, it considers two cases, namely discrete-state and continuous-state problems, and proposes state-based and ranking-based methods to select appropriate examples for these two cases, respectively. Finally, the simulations demonstrate that the proposed algorithm can achieve comparable performance as conventional deep reinforcement learning (DRL) techniques without dedicated model training or fine-tuning. Such an efficient and low-complexity approach has great potential for future wireless network optimization.
LSTM-Based Net Load Forecasting for Wind and Solar Power-Equipped Microgrids
Authors: Jesus Silva-Rodriguez, Elias Raffoul, Xingpeng Li
Date: 2024-07-31 00:00:00
Tasks: energy management, Load Forecasting, Management

The rising integration of variable renewable energy sources (RES), like solar and wind power, introduces considerable uncertainty in grid operations and energy management. Effective forecasting models are essential for grid operators to anticipate the net load - the difference between consumer electrical demand and renewable power generation. This paper proposes a deep learning (DL) model based on long short-term memory (LSTM) networks for net load forecasting in renewable-based microgrids, considering both solar and wind power. The model's architecture is detailed, and its performance is evaluated using a residential microgrid test case based on a typical meteorological year (TMY) dataset. The results demonstrate the effectiveness of the proposed LSTM-based DL model in predicting the net load, showcasing its potential for enhancing energy management in renewable-based microgrids.
Optimal price signal generation for demand-side energy management
Authors: Seyed Shahabaldin Tohidi, Henrik Madsen, Davide Calì, Tobias K. S. Ritschel
Date: 2024-07-31 00:00:00
Tasks: energy management, Management

Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price signal and weather forecasts. In this work, we use a Flexibility Function to represent the relationship between the price signal and the demand and investigate optimization problems for the price signal computation. Consequently, this study considers the higher and lower levels in the hierarchy from the markets to appliances, households, and districts. This paper investigates optimal price generation via the Flexibility Function and studies its employment in controller design for demand-side management, its capability to provide ancillary services for balancing throughout the Smart Energy Operating System, and its effect on the physical level performance. Sequential and simultaneous approaches for computing the price signal, along with various cost functions are analyzed and compared. Simulation results demonstrate the generated price/penalty signal and its employment in a model predictive controller.
Assessing the Reliability Benefits of Energy Storage as a Transmission Asset
Authors: David Sehloff, Jonghwan Kwon, Mahdi Mehrtash, Todd Levin, Benjamin F. Hobbs
Date: 2024-07-31 00:00:00

Utilizing energy storage solutions to reduce the need for traditional transmission investments has been recognized by system planners and supported by federal policies in recent years. This work demonstrates the need for detailed reliability assessment for quantitative comparison of the reliability benefits of energy storage and traditional transmission investments. First, a mixed-integer linear programming expansion planning model considering candidate transmission lines and storage technologies is solved to find the least-cost investment decisions. Next, operations under the resulting system configuration are simulated in a probabilistic reliability assessment which accounts for weather-dependent forced outages. The outcome of this work, when applied to TPPs, is to further equalize the consideration of energy storage compared to traditional transmission assets by capturing the value of storage for system reliability.
Scalable Synthesis of Formally Verified Neural Value Function for Hamilton-Jacobi Reachability Analysis
Authors: Yujie Yang, Hanjiang Hu, Tianhao Wei, Shengbo Eben Li, Changliu Liu
Date: 2024-07-30 00:00:00

Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems. It synthesizes a value function to represent a long-term safe set called feasible region. Early synthesis methods based on state space discretization cannot scale to high-dimensional problems, while recent methods that use neural networks to approximate value functions result in unverifiable feasible regions. To achieve both scalability and verifiability, we propose a framework for synthesizing verified neural value functions for HJ reachability analysis. Our framework consists of three stages: pre-training, adversarial training, and verification-guided training. We design three techniques to address three challenges to improve scalability respectively: boundary-guided backtracking (BGB) to improve counterexample search efficiency, entering state regularization (ESR) to enlarge feasible region, and activation pattern alignment (APA) to accelerate neural network verification. We also provide a neural safety certificate synthesis and verification benchmark called Cersyve-9, which includes nine commonly used safe control tasks and supplements existing neural network verification benchmarks. Our framework successfully synthesizes verified neural value functions on all tasks, and our proposed three techniques exhibit superior scalability and efficiency compared with existing methods.
Data Requirements and Prediction Scaling for Long-Term Failure Forecasts in Wind Turbines
Authors: Viktor Begun, Ulrich Schlickewei
Date: 2024-07-31 00:00:00

We investigate the key factors that enable early failure forecasting in wind turbines. For this purpose, we analyze studies with long-term forecasts and compare their main features: prediction time, methods, targeted components, dataset size, and check the effect of using additional sensors. We found that the size of the dataset is the main factor and that an approximate linear scaling holds: the number of forecast days is twice the size of the dataset, measured in turbine years. We also observe that the data allow us to quantify the meaning of "big" and "long" in the terms "big data" and "long-term" forecasts, which are found to be ten turbine years and two weeks.
H-Watch: An Open, Connected Platform for AI-Enhanced COVID19 Infection Symptoms Monitoring and Contact Tracing
Authors: Tommaso Polonelli, Lukas Schulthess, Philipp Mayer, Michele Magno, Luca Benini
Date: 2024-07-31 00:00:00

The novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch.
RF Power Transmission for Self-sustaining Miniaturized IoT Devices
Authors: Lukas Schulthess, Federico Villani, Philipp Mayer, Michele Magno
Date: 2024-07-31 00:00:00
Tasks: Management

Radio Frequency (RF) wireless power transfer is a promising technology that has the potential to constantly power small Internet of Things (IoT) devices, enabling even battery-less systems and reducing their maintenance requirements. However, to achieve this ambitious goal, carefully designed RF energy harvesting (EH) systems are needed to minimize the conversion losses and the conversion efficiency of the limited power. For intelligent internet of things sensors and devices, which often have non-constant power requirements, an additional power management stage with energy storage is needed to temporarily provide a higher power output than the power being harvested. This paper proposes an RF wireless power energy conversion system for miniaturized IoT composed of an impedance matching network, a rectifier, and power management with energy storage. The proposed sub-system has been experimentally validated and achieved an overall power conversion efficiency (PCE) of over 30 % for an input power of -10 dBm and a peak efficiency of 57 % at 3 dBm.