pwc-archive/papers-with-abstracts huggingface dataset:
Circuit-theoretic Joint Parameter-State Estimation -- Balancing Optimality and AC Feasibility
Authors: Peng Sang, Amritanshu Pandey
Date: 2024-04-16 00:00:00
Tasks: State Estimation

AC State Estimation (ACSE) is widely recognized as a practical approach for determining the grid states in steady-state conditions. It serves as a fundamental analysis to ensure grid security and is a reference for market dispatch. As grid complexity increases with rapid electrification and decarbonization, there is a growing need for more accurate knowledge of the grid operating state. However, existing ACSE algorithms have technical gaps. Critically, current ACSE algorithms are susceptible to erroneous system parameters, which are assumed to be fixed in traditional approaches. In this paper, we build a novel circuit-theoretic joint parameter-state estimation algorithm to address this limitation. The innovative algorithm builds an analogous equivalent circuit of the grid with states and certain parameters unknown. It solves a circuit-constrained optimization to estimate the most likely grid states and parameters given a set of measurements. Further, it quantifies the goodness of the estimated output by formulating tight convex envelopes around the original non-convex problem to quantify the quality of estimates. We compare the various proposed approaches on systems with up to 2869 nodes while demonstrating a tradeoff between solution optimality and model fidelity.
A Game-Theoretic Approach for PMU Deployment Against False Data Injection Attacks
Authors: Sajjad Maleki, Subhash Lakshminarayana, E. Veronica Belmega, Carsten Maple
Date: 2024-04-16 00:00:00

Phasor Measurement Units (PMUs) are used in the measurement, control and protection of power grids. However, deploying PMUs at every bus in a power system is prohibitively expensive, necessitating partial PMU placement that can ensure system observability with minimal units. One consequence of this economic approach is increased system vulnerability to False Data Injection Attacks (FDIAs). This paper proposes a zero-sum game-based approach to strategically place an additional PMU (following the initial optimal PMU deployment that ensures full observability) to bolster robustness against FDIAs by introducing redundancy in attack-susceptible areas. To compute the Nash equilibrium (NE) solution, we leverage a reinforcement learning algorithm that mitigates the need for complete knowledge of the opponent's actions. The proposed PMU deployment algorithm increases the detection rate of FDIA by 36% compared to benchmark algorithms.
Dependability in Embedded Systems: A Survey of Fault Tolerance Methods and Software-Based Mitigation Techniques
Authors: Mohammadreza Amel Solouki, Shaahin Angizi, Massimo Violante
Date: 2024-04-16 00:00:00
Tasks: Survey

Fault tolerance is a critical aspect of modern computing systems, ensuring correct functionality in the presence of faults. This paper presents a comprehensive survey of fault tolerance methods and software-based mitigation techniques in embedded systems. The focus is on real-time embedded systems, considering their resource constraints and the increasing interconnectivity of computing systems in commercial and industrial applications. The survey covers various fault-tolerance methods, including hardware, software, and hybrid redundancy. Particular emphasis is given to software faults, acknowledging their significance as a leading cause of system failures. Moreover, the paper explores the challenges posed by soft errors in modern computing systems. The survey concludes by emphasizing the need for continued research and development in fault-tolerance methods, specifically in the context of real-time embedded systems, and highlights the potential for extending fault-tolerance approaches to diverse computing environments.
Decentralized Control for Heterogeneous Battery Energy Storage System
Authors: Yusuke Hakuta, Yasushi Amano, Tomohiko Jimbo, Shuji Tomura
Date: 2024-04-16 00:00:00

Battery energy storage systems (BESSs) are essential for stable power supply in renewable energy systems that can operate in all weather. Future BESSs will be massive and pluggable with several heterogeneous batteries. In this paper, a novel decentralized control method for a heterogeneous BESS is proposed, in which each battery autonomously operates based on its characteristics. First, a control method that uses only one broadcast signal for each type of battery is proposed. Second, the asymptotic stability of the tracking error is proved. Third, numerical simulations confirm that the proposed control method has robust tracking performance of the total electric power to the demanded power when some batteries fail and are detached from the system. Last, in order to suppress degradation of battery, equalization of the state of charge is achieved for each type of battery without communication among the batteries.
Metamaterial-inspired Wearable Pad for Enhancing EM Coupling with Biological Tissues
Authors: Maria Koutsoupidou, Dimitrios C. Tzarouchis, Dionysios Rompolas, Ioannis Sotiriou, George Palikaras, Panagiotis Kosmas
Date: 2024-04-16 00:00:00
Tasks: Diagnostic

Wearable, implantable, and ingestible antennas are continuously evolving in biomedical applications, as they are crucial components in devices used for monitoring and controlling physiological parameters. This work presents an experimentally validated wearable pad which can improve transmission of electromagnetic waves into the human body. This metamaterial-inspired matching pad, which is based on small metallic loops encased in a thin dielectric layer, is mechanically stable, flexible, and passive. As such, the pad can serve as a coupling medium for microwave medical systems and implantable device communication. Operating in the 2.4-2.5 GHz range, the pad demonstrates significant improvement in signal penetration levels (and, hence, depth) into a biological tissue. The study presents design methodology, simulation studies, in-lab development, and experimental characterization of this pad, which can offer a practical solution for enhanced communication and functionality in various medical diagnostic systems.
Disturbance Rejection-Guarded Learning for Vibration Suppression of Two-Inertia Systems
Authors: Fan Zhang, Jinfeng Chen, Yu Hu, Zhiqiang Gao, Ge Lv, Qin Lin
Date: 2024-04-16 00:00:00

Model uncertainty presents significant challenges in vibration suppression of multi-inertia systems, as these systems often rely on inaccurate nominal mathematical models due to system identification errors or unmodeled dynamics. An observer, such as an extended state observer (ESO), can estimate the discrepancy between the inaccurate nominal model and the true model, thus improving control performance via disturbance rejection. The conventional observer design is memoryless in the sense that once its estimated disturbance is obtained and sent to the controller, the datum is discarded. In this research, we propose a seamless integration of ESO and machine learning. On one hand, the machine learning model attempts to model the disturbance. With the assistance of prior information about the disturbance, the observer is expected to achieve faster convergence in disturbance estimation. On the other hand, machine learning benefits from an additional assurance layer provided by the ESO, as any imperfections in the machine learning model can be compensated for by the ESO. We validated the effectiveness of this novel learning-for-control paradigm through simulation and physical tests on two-inertial motion control systems used for vibration studies.
Dynamic Complex-Frequency Control of Grid-Forming Converters
Authors: Roger Domingo-Enrich, Xiuqiang He, Verena Häberle, Florian Dörfler
Date: 2024-04-15 00:00:00

Complex droop control, alternatively known as dispatchable virtual oscillator control (dVOC), stands out for its unique capabilities in synchronization and voltage stabilization among existing control strategies for grid-forming converters. Complex droop control leverages the novel concept of ``complex frequency'', thereby establishing a coupled connection between active and reactive power inputs and frequency and rate-of-change-of voltage outputs. However, its reliance on static droop gains limits its ability to exhibit crucial dynamic response behaviors required in future power systems. To address this limitation, this paper introduces dynamic complex-frequency control, upgrading static droop gains with dynamic transfer functions to enhance the richness and flexibility in dynamic responses for frequency and voltage control. Unlike existing approaches, the complex-frequency control framework treats frequency and voltage dynamics collectively, ensuring small-signal stability for frequency synchronization and voltage stabilization simultaneously. The control framework is validated through detailed numerical case studies on the IEEE nine-bus system, also showcasing its applicability in multi-converter setups.
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study
Authors: Yujie Yang, Zhilong Zheng, Shengbo Eben Li, Masayoshi Tomizuka, Changliu Liu
Date: 2024-04-15 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning (RL)

Satisfying safety constraints is a priority concern when solving optimal control problems (OCPs). Due to the existence of infeasibility phenomenon, where a constraint-satisfying solution cannot be found, it is necessary to identify a feasible region before implementing a policy. Existing feasibility theories built for model predictive control (MPC) only consider the feasibility of optimal policy. However, reinforcement learning (RL), as another important control method, solves the optimal policy in an iterative manner, which comes with a series of non-optimal intermediate policies. Feasibility analysis of these non-optimal policies is also necessary for iteratively improving constraint satisfaction; but that is not available under existing MPC feasibility theories. This paper proposes a feasibility theory that applies to both MPC and RL by filling in the missing part of feasibility analysis for an arbitrary policy. The basis of our theory is to decouple policy solving and implementation into two temporal domains: virtual-time domain and real-time domain. This allows us to separately define initial and endless, state and policy feasibility, and their corresponding feasible regions. Based on these definitions, we analyze the containment relationships between different feasible regions, which enables us to describe the feasible region of an arbitrary policy. We further provide virtual-time constraint design rules along with a practical design tool called feasibility function that helps to achieve the maximum feasible region. We review most of existing constraint formulations and point out that they are essentially applications of feasibility functions in different forms. We demonstrate our feasibility theory by visualizing different feasible regions under both MPC and RL policies in an emergency braking control task.
Priority aware grouping-based multihop routing scheme for RIS-assisted wireless networks
Authors: Lakshmikanta Sau, Priyadarshi Mukherjee, Sasthi C. Ghosh
Date: 2024-04-15 00:00:00

Reconfigurable intelligent surfaces (RISs) is a novel communication technology that has been recently presented as a potential candidate for beyond fifth-generation wireless communication networks. In this paper, we propose a priority-aware user traffic-dependent grouping-based multihop routing scheme for a RIS-assisted millimeter wave (mmWave) device-to-device (D2D) communication network with spatially correlated channels. Specifically, the proposed scheme exploits the priority of the users (based on their respective delay-constrained applications) and the aspect of spatial correlation in the narrowly spaced reflecting elements of the RISs. Here, based on the other users in the neighborhood, their respective traffic characteristics, and the already deployed RISs in the surroundings, we establish a multihop connection for information transfer from one of the users to its intended receiver. In this context, we take into account the impact of considering practical discrete phase shifts at the RIS patches instead of its ideal continuous counterpart. Moreover, we also claim and demonstrate that the existing classic least remaining distance (LRD)-based approach is not always the optimal solution. Finally, numerical results demonstrate the advantages of the proposed strategy and that it significantly outperforms the existing benchmark schemes in terms of system performance metrics such as data throughput, energy consumption, as well as energy efficiency.
Disturbance feedback-based model predictive control in uncertain dynamic environments
Authors: Philipp Buschermöhle, Taouba Jouini, Torsten Lilge, Matthias A. Müller
Date: 2024-04-15 00:00:00
Tasks: Model Predictive Control

This paper presents a robust MPC scheme for linear systems subject to time-varying, uncertain constraints that arise from uncertain environments. The predicted input sequence is parameterized over future environment states to guarantee constraint satisfaction despite an imprecise environment prediction and unknown evolution of the future constraints. We provide theoretical guarantees for recursive feasibility and asymptotic convergence. Finally, a brief simulation example showcases our results.
Sample-Based Conservative Bias Linear Power Flow Approximations
Authors: Paprapee Buason, Sidhant Misra, Daniel K. Molzahn
Date: 2024-04-15 00:00:00

The power flow equations are central to many problems in power system planning, analysis, and control. However, their inherent non-linearity and non-convexity present substantial challenges during problem-solving processes, especially for optimization problems. Accordingly, linear approximations are commonly employed to streamline computations, although this can often entail compromises in accuracy and feasibility. This paper proposes an approach termed Conservative Bias Linear Approximations (CBLA) for addressing these limitations. By minimizing approximation errors across a specified operating range while incorporating conservativeness (over- or under-estimating quantities of interest), CBLA strikes a balance between accuracy and tractability by maintaining linear constraints. By allowing users to design loss functions tailored to the specific approximated function, the bias approximation approach significantly enhances approximation accuracy. We illustrate the effectiveness of our proposed approach through several test cases.
Guaranteed Reachability on Riemannian Manifolds for Unknown Nonlinear Systems
Authors: Taha Shafa, Melkior Ornik
Date: 2024-04-15 00:00:00
Tasks: Trajectory Planning

Determining the reachable set for a given nonlinear system is critically important for autonomous trajectory planning for reach-avoid applications and safety critical scenarios. Providing the reachable set is generally impossible when the dynamics are unknown, so we calculate underapproximations of such sets using local dynamics at a single point and bounds on the rate of change of the dynamics determined from known physical laws. Motivated by scenarios where an adverse event causes an abrupt change in the dynamics, we attempt to determine a provably reachable set of states without knowledge of the dynamics. This paper considers systems which are known to operate on a manifold. Underapproximations are calculated by utilizing the aforementioned knowledge to derive a guaranteed set of velocities on the tangent bundle of a complete Riemannian manifold that can be reached within a finite time horizon. We then interpret said set as a control system; the trajectories of this control system provide us with a guaranteed set of reachable states the unknown system can reach within a given time. The results are general enough to apply on systems that operate on any complete Riemannian manifold. To illustrate the practical implementation of our results, we apply our algorithm to a model of a pendulum operating on a sphere and a three-dimensional rotational system which lives on the abstract set of special orthogonal matrices.
Stable Inversion of Piecewise Affine Systems with Application to Feedforward and Iterative Learning Control
Authors: Isaac A. Spiegel, Nard Strijbosch, Robin de Rozario, Tom Oomen, Kira Barton
Date: 2024-04-15 00:00:00

Model inversion is a fundamental technique in feedforward control. Unstable inverse models present a challenge in that useful feedforward control trajectories cannot be generated by directly propagating them. Stable inversion is a process for generating useful trajectories from unstable inverses by handling their stable and unstable modes separately. Piecewise affine (PWA) systems are a popular framework for modeling complicated dynamics. The primary contributions of this article are closed-form inverse formulas for a general class of PWA models, and stable inversion methods for these models. Both contributions leverage closed-form model representations to prove sufficient conditions for solution existence and uniqueness, and to develop solution computation methods. The result is implementable feedforward control synthesis from PWA models with either stable or unstable inverses. In practice, feedforward control alone may yield substantial tracking errors due to mismatch between the known system model and the unknowable complete system physics. Iterative learning control (ILC) is a technique for achieving robustness to model error in feedforward control. To demonstrate the primary contributions' validity and utility, this article also integrates PWA stable inversion with ILC in simulations based on a physical printhead positioning system.
Hierarchical Fault-Tolerant Coverage Control for an Autonomous Aerial Agent
Authors: Savvas Papaioannou, Christian Vitale, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2024-04-15 00:00:00

Fault-tolerant coverage control involves determining a trajectory that enables an autonomous agent to cover specific points of interest, even in the presence of actuation and/or sensing faults. In this work, the agent encounters control inputs that are erroneous; specifically, its nominal controls inputs are perturbed by stochastic disturbances, potentially disrupting its intended operation. Existing techniques have focused on deterministically bounded disturbances or relied on the assumption of Gaussian disturbances, whereas non-Gaussian disturbances have been primarily been tackled via scenario-based stochastic control methods. However, the assumption of Gaussian disturbances is generally limited to linear systems, and scenario-based methods can become computationally prohibitive. To address these limitations, we propose a hierarchical coverage controller that integrates mixed-trigonometric-polynomial moment propagation to propagate non-Gaussian disturbances through the agent's nonlinear dynamics. Specifically, the first stage generates an ideal reference plan by optimising the agent's mobility and camera control inputs. The second-stage fault-tolerant controller then aims to follow this reference plan, even in the presence of erroneous control inputs caused by non-Gaussian disturbances. This is achieved by imposing a set of deterministic constraints on the moments of the system's uncertain states.
Data-Driven Stability Assessment of Power Electronic Converters with Multi-Resolution Dynamic Mode Decomposition
Authors: Rui Kong, Subham Sahoo, Yongjie Liu, Frede Blaabjerg
Date: 2024-04-15 00:00:00
Tasks: Diagnostic

Harmonic instability occurs frequently in the power electronic converter system. This paper leverages multi-resolution dynamic mode decomposition (MR-DMD) as a data-driven diagnostic tool for the system stability of power electronic converters, not requiring complex modeling and detailed control information. By combining dynamic mode decomposition (DMD) with the multi-resolution analysis used in wavelet theory, dynamic modes and eigenvalues can be identified at different decomposition levels and time scales with the MR-DMD algorithm, thereby allowing for handling datasets with transient time behaviors, which is not achievable using conventional DMD. Further, the selection criteria for important parameters in MR-DMD are clearly defined through derivation, elucidating the reason for enabling it to extract eigenvalues within different frequency ranges. Finally, the analysis results are verified using the dataset collected from the experimental platform of a low-frequency oscillation scenario in electrified railways featuring a single-phase converter.
A Gray-Box Stability Analysis Mechanism for Power Electronic Converters
Authors: Rui Kong, Subham Sahoo, Yubo Song, Frede Blaabjerg
Date: 2024-04-15 00:00:00

This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology. By fusing the underlying physical constraints of the state equations into data snapshots, the system dynamic state matrix and input matrix are simultaneously approximated to identify the dominant system dynamic modes and eigenvalues using the DMD with control (DMDc) algorithm. While retaining the advantages of eliminating the need for intrinsic controller information, the proposed gray-box method establishes higher accuracy and interpretable outcomes over the conventional DMD method. Finally, under experimental conditions of a low-frequency oscillation scenario in electrified railways featuring a single-phase converter, the proposed gray-box DMDc is verified to identify the dominant eigenvalues more accurately.
MPC using mixed-integer programming for aquifer thermal energy storages
Authors: Johannes van Randenborgh, Moritz Schulze Darup
Date: 2024-04-15 00:00:00
Tasks: Model Predictive Control

Aquifer thermal energy storages (ATES) are used to temporally store thermal energy in groundwater saturated aquifers. Typically, two storages are combined, one for heat and one for cold, to support heating and cooling of buildings. This way, the use of classical fossil fuel-based heating, ventilation, and air conditioning can be significantly reduced. Exploiting the benefits of ATES beyond "seasonal" heating in winter and cooling in summer as well as meeting legislative restrictions requires sophisticated control. We propose a tailored model predictive control (MPC) scheme for the sustainable operation of ATES systems, which mainly builds on a novel model and objective function. The new approach leads to a mixed-integer quadratic program. Its performance is evaluated on real data from an ATES system in Belgium.
Monitoring Based Fatigue Damage Prognosis of Wind Turbine Composite Blades under Uncertain Wind Loads
Authors: Chizhi Zhang, Hua-Peng Chen
Date: 2024-04-15 00:00:00
Tasks: Prognosis

Lifecycle assessment of wind turbines is essential to improve their design and to optimum maintenance plans for preventing failures during the design life. A critical element of wind turbines is the composite blade due to uncertain cyclic wind loads with relatively high frequency and amplitude in offshore environments. It is critical to detect the wind fatigue damage evolution in composite blades before they fail catastrophically and destroy the entire wind turbines. This study presents a methodology for analysing the fatigue failure probability of a wind turbine composite blade by using monitoring stochastic deterioration modelling. On the basis of 5 minutes mean wind speed measurements, the internal stresses can be accurately obtained from finite element analysis, and failure probabilities are predicted by stochastic gamma process fatigue damage model in design service life. A numerical example of a wind turbine composite blade is investigated to show the applicability of the proposed model. The results show that the stochastic fatigue damage model can give reliable results for time-dependent reliability analysis for composite blades of wind turbines.