pwc-archive/papers-with-abstracts huggingface dataset:
A Note on the Estimation of Von Neumann and Relative Entropy via Quantum State Observers
Authors: Mark Balas, Vinod P. Gehlot, Tristan D. Griffith
Date: 2023-09-07 00:00:00

An essential quantity in quantum information theory is the von Neumann entropy which depends entirely on the quantum density operator. Once known, the density operator reveals the statistics of observables in a quantum process, and the corresponding von Neumann Entropy yields the full information content. However, the state, or density operator, of a given system may be unknown. Quantum state observers have been proposed to infer the unknown state of a quantum system. In this note, we show (i) that the von Neumann entropy of the state estimate produced by our quantum state observer is exponentially convergent to that of the system's true state, and (ii) the relative entropy between the system and observer's state converges exponentially to zero as long as the system starts in a full-rank state.
Deep Reinforcement Learning Enabled Joint Deployment and Beamforming in STAR-RIS Assisted Networks
Authors: Zhuoyuan Ma, Qi Zhao, Bai Yan, Jin Zhang
Date: 2023-09-07 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning

In the new generation of wireless communication systems, reconfigurable intelligent surfaces (RIS) and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have become competitive network components to achieve intelligent and reconfigurable network environments. However, existing work has not fully studied the deployment freedom of STAR-RIS, which limits further improvements in network communication performance. Therefore, this paper proposes a solution based on a deep reinforcement learning algorithm to dynamically deploy STAR-RIS and hybrid beamforming to improve the total communication rate of users in mobile wireless networks. The paper constructs a STAR-RIS assisted multi-user multiple-input single-output (MU-MISO) mobile wireless network and jointly optimizes the dynamic deployment strategy of STAR-RIS and the hybrid beamforming strategy to maximize the long-term total communication rate of users. To solve this problem, the paper uses the Proximal Policy Optimization (PPO) algorithm to optimize the deployment of STAR-RIS and the joint beamforming strategy of STAR-RIS and the base station. The trained policy can maximize the downlink transmission rate of the system and meet the real-time decision-making needs of the system. Numerical simulation results show that compared with the traditional scheme without using STAR-RIS and fixed STAR-RIS deployment, the PPO method proposed in this paper can effectively improve the total communication rate of wireless network users in the service area.
Research on Damage Analysis of Key Parts of UAV Flight Control System
Authors: Tianshun Li, Huaimin Chen, Ben Xiao, Hao Li, Shiyu Hao, Di Hai, Xuetong Wang
Date: 2023-09-07 00:00:00

A set of hardware in the loop simulation methods based on the UAV model is proposed to create fault data, which is used to judge the parts where faults happen. Actual flight experimental data is utilized to prove the reliability of Simulink models. Then a series of typical faults with various amplitudes are injected into different channels of UAV parts in hardware in the loop simulation platform. Fault data is created this way, and the effect on UAV flight and task/control can be obtained through damage analysis. Typical fault characters are extracted, and those parts that have faults can be analyzed and judged. We can also know the trend that faults will develop and conclude the reasons for faults based on exterior performance, which supports precise attack and performance evaluation techniques.
Cyber Recovery from Dynamic Load Altering Attacks: Linking Electricity, Transportation, and Cyber Networks
Authors: Mengxiang Liu, Zhongda Chu, Fei Teng
Date: 2023-09-06 00:00:00

To address the increasing vulnerability of power grids, significant attention has been focused on the attack detection and impact mitigation. However, it is still unclear how to effectively and quickly recover the cyber and physical networks from a cyberattack. In this context, this paper presents the first investigation of the Cyber Recovery from Dynamic load altering Attack (CRDA). Considering the interconnection among electricity, transportation, and cyber networks, two essential sub-tasks are formulated for the CRDA: i) Optimal design of repair crew routes to remove installed malware and ii) Adaptive adjustment of system operation to eliminate the mitigation costs while guaranteeing stability. To achieve this, linear stability constraints are obtained by estimating the related eigenvalues under the variation of multiple IBR droop gains based on the sensitivity information of strategically selected sampling points. Moreover, to obtain the robust recovery strategy, the potential counter-measures from the adversary during the recovery process are modeled as maximizing the attack impact of remaining compromised resources in each step. A Mixed-Integer Linear Programming (MILP) problem can be finally formulated for the CRDA with the primary objective to reset involved droop gains and secondarily to repair all compromised loads. Case studies are performed in the modified IEEE 39-bus power system to illustrate the effectiveness of the proposed CRDA compared to the benchmark case.
Optimal transmission switching and grid reconfiguration for transmission systems via convex relaxations
Authors: Vineet Jagadeesan Nair
Date: 2023-09-06 00:00:00

In this paper, we formulate optimization problems to perform optimal transmission switching (OTS) in order to operate power transmission grids most efficiently. In any given electrical network, several of the transmission lines are generally equipped with switches, circuit breakers, and/or reclosers. The conventional practice is to operate the grid using a static or fixed configuration. However, it may be beneficial to dynamically reconfigure the grid through switching actions in order to respond to real-time demand and supply conditions. This has the potential to help reduce costs and improve efficiency. Furthermore, such OTS may be more crucial in future power grids with much higher penetrations of renewable energy sources, which introduce more variability and intermittency in generation. Similarly, OTS can potentially help mitigate the effects of unpredictable demand fluctuations (e.g. due to extreme weather). We explored and compared several different formulations for the OTS problems in terms of computational performance and optimality. I also applied them to small transmission test case networks as a proof of concept to see what the effects of applying OTS are.
A Peer-to-Peer Energy Trading Framework in Distribution Systems Considering Network Constraints
Authors: Saeed Nasiri
Date: 2023-09-06 00:00:00
Tasks: energy trading

With the widespread adoption of Renewable Energy Sources (RESs) in low-voltage distribution systems, opportunities for energy trading among peers have emerged. In particular, the advent of distributed ledgers and blockchain technologies has catalyzed the application of Peer-to-Peer (P2P) economic concepts in decentralized, small-scale energy trading. This paper focuses on the critical physical layer aspects of transactions within the context of P2P energy trading, with a specific emphasis on addressing network constraints. Key challenges include maintaining margins for over/under voltage, voltage balance, and preventing congestion, all of which must be upheld during P2P energy exchanges. To address these challenges, we propose a novel analytical approach tailored to distribution networks. Furthermore, we introduce the Block Double Auction (BDA) mechanism as the P2P market mechanism for determining the acceptance or rejection of P2P transactions. The effectiveness of our proposed method is validated using the IEEE 33-node distribution test system, demonstrating its robust capabilities.
A Lie-Theoretic Approach to Propagating Uncertainty Jointly in Attitude and Angular Momentum
Authors: Amitesh S. Jayaraman, Jikai Ye, Gregory S. Chirikjian
Date: 2023-09-06 00:00:00
Tasks: State Estimation

Dynamic state estimation, as opposed to kinematic state estimation, seeks to estimate not only the orientation of a rigid body but also its angular velocity, through Euler's equations of rotational motion. This paper demonstrates that the dynamic state estimation problem can be reformulated as estimating a probability distribution on a Lie group defined on phase space (the product space of rotation and angular momentum). The propagation equations are derived non-parametrically for the mean and covariance of the distribution. It is also shown that the equations can be approximately solved by ignoring the third and higher moments of the probability distribution. Numerical experiments show that the distribution constructed from the propagated mean and covariance fits the sample data better than an extended Kalman filter.
Variational Bayesian Approximations Kalman Filter Based on Threshold Judgment
Authors: Zuxuan Zhang, Gang Wang, Jiacheng He, Shan Zhong
Date: 2023-09-06 00:00:00

The estimation of non-Gaussian measurement noise models is a significant challenge across various fields. In practical applications, it often faces challenges due to the large number of parameters and high computational complexity. This paper proposes a threshold-based Kalman filtering approach for online estimation of noise parameters in non-Gaussian measurement noise models. This method uses a certain amount of sample data to infer the variance threshold of observation parameters and employs variational Bayesian estimation to obtain corresponding noise variance estimates, enabling subsequent iterations of the Kalman filtering algorithm. Finally, we evaluate the performance of this algorithm through simulation experiments, demonstrating its accurate and effective estimation of state and noise parameters.
Existence and Completeness of Bounded Disturbance Observers: A Set-Membership Viewpoint
Authors: Yudong Li, Yirui Cong, Jiuxiang Dong
Date: 2023-09-06 00:00:00

This paper investigates the boundedness of the Disturbance Observer (DO) for linear discrete-time systems. In contrast to previous studies that focus on analyzing and/or designing observer gains, our analysis and synthesis approach is based on a set-membership viewpoint. From this viewpoint, a necessary and sufficient existence condition of bounded DOs is first established, which can be easily verified. Furthermore, a set-membership filter-based DO is developed, and its completeness is proved; thus, our proposed DO is bounded if and only if bounded DOs exist. We also prove that the proposed DO has the capability to achieve the worst-case optimality, which can provide a benchmark for the design of DOs. Finally, numerical simulations are performed to corroborate the effectiveness of the theoretical results.
A study on fault diagnosis in nonlinear dynamic systems with uncertainties
Authors: Steven X. Ding, Linlin Li
Date: 2023-09-06 00:00:00
Tasks: Fault Detection, Fault Diagnosis, State Space Models

In this draft, fault diagnosis in nonlinear dynamic systems is addressed. The objective of this work is to establish a framework, in which not only model-based but also data-driven and machine learning based fault diagnosis strategies can be uniformly handled. Instead of the well-established input-output and the associated state space models, stable image and kernel representations are adopted in our work as the basic process model forms. Based on it, the nominal system dynamics can then be modelled as a lower-dimensional manifold embedded in the process data space. To achieve a reliable fault detection as a classification problem, projection technique is a capable tool. For nonlinear dynamic systems, we propose to construct projection systems in the well-established framework of Hamiltonian systems and by means of the normalised image and kernel representations. For nonlinear dynamic systems, process data form a non-Euclidean space. Consequently, the norm-based distance defined in Hilbert space is not suitable to measure the distance from a data vector to the manifold of the nominal dynamics. To deal with this issue, we propose to use a Bregman divergence, a measure of difference between two points in a space, as a solution. Moreover, for our purpose of achieving a performance-oriented fault detection, the Bregman divergences adopted in our work are defined by Hamiltonian functions. This scheme not only enables to realise the performance-oriented fault detection, but also uncovers the information geometric aspect of our work. The last part of our work is devoted to the kernel representation based fault detection and uncertainty estimation that can be equivalently used for fault estimation. It is demonstrated that the projection onto the manifold of uncertainty data, together with the correspondingly defined Bregman divergence, is also capable for fault detection.
Under-frequency Load Shedding for Power Reserve Management in Islanded Microgrids
Authors: Bei Xu, Victor Paduani, Qi Xiao, Lidong Song, David Lubkeman, Ning Lu
Date: 2023-09-03 00:00:00
Tasks: Management

This paper introduces under-frequency load shedding (UFLS) schemes specially designed to fulfill the power reserve requirements in islanded microgrids (MGs), where only one grid-forming resource is available for frequency regulation. When the power consumption of the MG exceeds a pre-defined threshold, the MG frequency will be lowered to various setpoints, thereby triggering UFLS for different levels of load reduction. Three types of controllable devices are considered for executing UFLS: sectionalizers, smart meters, and controllable appliances. To avoid unnecessary UFLS activation, various time delay settings are analyzed, allowing short-lived power spikes caused by events like motor startups or cold-load pickups to be disregarded. We tested the proposed UFLS schemes on a modified IEEE 123-bus system on the OPAL-RT eMEGASIM platform. Simulation results verify the efficacy of the proposed approaches in restoring power reserves, maintaining phase power balance, and effectively handling short-lived power fluctuations. Furthermore, in comparison to sectionalizer-based UFLS, using smart meters or controllable loads for UFLS allows for a more accurate per-phase load shedding in a progressive manner. As a result, it leads to better balanced three-phase voltage and serves more loads.
Neural Networks for Fast Optimisation in Model Predictive Control: A Review
Authors: Camilo Gonzalez, Houshyar Asadi, Lars Kooijman, Chee Peng Lim
Date: 2023-09-06 00:00:00
Tasks: Benchmarking, Model Predictive Control

Model Predictive Control (MPC) is an optimal control algorithm with strong stability and robustness guarantees. Despite its popularity in robotics and industrial applications, the main challenge in deploying MPC is its high computation cost, stemming from the need to solve an optimisation problem at each control interval. There are several methods to reduce this cost. This survey focusses on approaches where a neural network is used to approximate an existing controller. Herein, relevant and unique neural approximation methods for linear, nonlinear, and robust MPC are presented and compared. Comparisons are based on the theoretical guarantees that are preserved, the factor by which the original controller is sped up, and the size of problem that a framework is applicable to. Research contributions include: a taxonomy that organises existing knowledge, a summary of literary gaps, discussion on promising research directions, and simple guidelines for choosing an approximation framework. The main conclusions are that (1) new benchmarking tools are needed to help prove the generalisability and scalability of approximation frameworks, (2) future breakthroughs most likely lie in the development of ties between control and learning, and (3) the potential and applicability of recently developed neural architectures and tools remains unexplored in this field.
Convolutional Neural Network-based RoCoF-Constrained Unit Commitment
Authors: Mingjian Tuo, Xingpeng Li
Date: 2023-09-06 00:00:00

The fast growth of inverter-based resources such as wind plants and solar farms will largely replace and reduce conventional synchronous generators in the future renewable energy-dominated power grid. Such transition will make the system operation and control much more complicated; and one key challenge is the low inertia issue that has been widely recognized. However, locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate significant inertia reduction has not been fully investigated in the literature. This paper presents a convolutional neural network (CNN) based RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability following the worst generator outage event while ensuring operational efficiency. A generic CNN based predictor is first trained to track the highest locational RoCoF based on a high-fidelity simulation dataset. The RoCoF predictor is then formulated as MILP constraints into the unit commitment model. Case studies are carried out on the IEEE 24-bus system, and simulation results obtained with PSS/E indicate that the proposed method can ensure locational post-contingency RoCoF stability without conservativeness.
The Iberian Exception: An overview of its effects over its first 100 days
Authors: David Robinson, Angel Arcos-Vargas, Micheael Tennican, Fernando Núñez
Date: 2023-09-05 00:00:00
Tasks: counterfactual

This paper offers an independent assessment of certain key economic effects of the Iberian Exception (IE). Their stated aim was to reduce the major component of electricity prices for most Iberian consumers, a component which was indexed to Iberian wholesale power market spot prices power market prices that were rising alarmingly due to extremely tight international markets for natural gas. The Spanish Government estimates that, during its first 100 days, the IE provided substantial benefits for consumers affected by the IE, which included over 10 million small consumers as well as many large ones, but the authors of this study question that estimate. The authors of this paper argue that the estimated effect of the IE on retail prices depends critically on the assumptions about what would have occurred in the absence of the IE, i.e., in a counterfactual scenario. Although counterfactuals are always difficult to construct, the government s counterfactual ignores demand elasticity, and this inflates their estimate of immediate consumer benefits. Using hourly data on the wholesale electricity market for the first 100 days of the IE, this paper s analysis of alternative counterfactuals that reflect the effects of demand elasticity shows substantially lower benefits of the IE for consumers than the Spanish government estimates. Indeed, this paper s analysis suggests that affected consumers would have paid somewhat less for electricity in the first 100 days of the IE had it not been introduced. The authors identify several other potential short and longterm effects of the IE that deserve further study. These include increased margins for fossil fired generators, reduced margins for some decarbonized inframarginal plant, heightened investor perceptions of regulatory risk, weakened incentives for efficient consumption, and higher carbon emissions and gas prices.
Investment-based optimisation of energy storage design parameters in a grid-connected hybrid renewable energy system
Authors: Sleiman Farah, Gorm Bruun Andresen
Date: 2023-09-05 00:00:00

Grid-connected hybrid renewable power systems with energy storage can reduce the intermittency of renewable power supply. However, emerging energy storage technologies need improvement to compete with lithium-ion batteries and reduce the cost of energy. Identifying and optimizing the the most valuable improvement path of these technologies is challenging due to the non-linearity of the energy system model when considering parameters as independent variables. To overcome this, a novel investment-based optimization method is proposed. The method involves linear optimization of the hybrid renewable energy system and subsequent investment optimization, accounting for diminishing improvements per investment. Applied to thermal energy, pumped thermal energy, molten salt, and adiabatic compressed air energy storage technologies, the results show that enhancing discharge efficiency is most valuable for all technologies. Reducing discharge capacity costs and energy storage capacity cost can also become important. Charge capacity cost and charge efficiency are found to be of lesser significance. The study provides detailed improvement pathways for each technology under various operational conditions, assisting developers in resource allocation. Overall, the investment-based optimization method and findings contribute to enhancing the competitiveness of emerging energy storage technologies and reducing reliance on batteries in renewable energy systems.
Parameter Dependent Robust Control Invariant Sets for LPV Systems with Bounded Parameter Variation Rate
Authors: Sampath Kumar Mulagaleti, Manas Mejari, Alberto Bemporad
Date: 2023-09-05 00:00:00
Tasks: Scheduling

Real-time measurements of the scheduling parameter of linear parameter-varying (LPV) systems enables the synthesis of robust control invariant (RCI) sets and parameter dependent controllers inducing invariance. We present a method to synthesize parameter-dependent robust control invariant (PD-RCI) sets for LPV systems with bounded parameter variation, in which invariance is induced using PD-vertex control laws. The PD-RCI sets are parameterized as configuration-constrained polytopes that admit a joint parameterization of their facets and vertices. The proposed sets and associated control laws are computed by solving a single semidefinite programing (SDP) problem. Through numerical examples, we demonstrate that the proposed method outperforms state-of-the-art methods for synthesizing PD-RCI sets, both with respect to conservativeness and computational load.
Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study
Authors: Federico Dettù, Simone Formentin, Stefano Varisco, Sergio Matteo Savaresi
Date: 2023-09-05 00:00:00
Tasks: Bayesian Optimization, global-optimization

Given the urgent need of simplifying the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run on-board to compute a nominal control action in run-time and an additional block C_delta is used to compensate for the mismatch between the simulator and the real vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of C_delta. More specifically, we compare the originally proposed Bayesian Optimization (BO) approach with the recently developed Set Membership Global Optimization (SMGO) and Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study -- the yaw-rate tracking problem -- so as to further prove the TiL-C effctiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to significantly reduce the computational effort required by BO.