pwc-archive/papers-with-abstracts huggingface dataset:
Secure Expansion of Energy Storage and Transmission Lines Considering Bundling Option Under Renewable Penetration
Authors: Mojtaba Moradi-Sepahvand, Turaj Amraee
Date: 2023-10-09 00:00:00

This paper presents a multi-stage expansion model for the co-planning of transmission lines, battery energy storage (ES), and wind power plants (WPP). High penetration of renewable energy sources (RES) is integrated into the proposed model concerning renewable portfolio standard (RPS) policy goals. The possibility of bundling existing transmission lines to uprate power flow capacity is considered. Renewable energy curtailment and load shedding are included in the model to assess the system operation more precisely. Battery ES devices are co-planned to defer transmission expansion and renewable management. To make the time complexity of the problem tractable and capture the uncertainties of load and RES in an hourly resolution, a chronological time-period clustering algorithm is used to extract the representative hours of each planning stage. Additionally, the flexible ramp reserve is utilized to handle the uncertainty of RES. An accelerated Benders dual decomposition (BDD) algorithm is developed to solve the proposed model mixed-integer linear programming (MILP) formulation. The N-1 security criterion is evaluated by considering a designed contingency screening (CS) algorithm to identify higher risk contingencies. The effectiveness of the proposed co-planning model is evaluated using IEEE RTS 24-bus and IEEE 118-bus test systems.
A Comparison between Markov Chain and Koopman Operator Based Data-Driven Modeling of Dynamical Systems
Authors: Saeid Tafazzol, Nan Li, Ilya Kolmanovsky, Dimitar Filev
Date: 2023-10-09 00:00:00
Tasks: Computational Efficiency

Markov chain-based modeling and Koopman operator-based modeling are two popular frameworks for data-driven modeling of dynamical systems. They share notable similarities from a computational and practitioner's perspective, especially for modeling autonomous systems. The first part of this paper aims to elucidate these similarities. For modeling systems with control inputs, the models produced by the two approaches differ. The second part of this paper introduces these models and their corresponding control design methods. We illustrate the two approaches and compare them in terms of model accuracy and computational efficiency for both autonomous and controlled systems in numerical examples.
Online Equitable Ride-sharing Car Distribution in a Congested Traffic
Authors: Hossein Rastgoftar
Date: 2023-10-08 00:00:00
Tasks: Scheduling

The usability of ride-sharing services like Uber and Lyft has been considerably improved by advancements in cellular communications. Such a tech-driven transportation system can reduce the number of private cars, in roads with limited physical capacity, effectively match drivers with passengers requesting service, and save drivers and passengers time by optimal scheduling and estimating of the trips. However, the existing services offered by ride-sharing companies may not necessarily reduce congestion, if they are not equitably accessed in urban areas. This paper addresses this important issue by classifying cars as ride-sharing and non-ride-sharing cars and developing a novel dissensus-based traffic evolution dynamics to effectively model their distribution in a shared network of interconnected roads (NOIR). This new dynamics models the ride-sharing car evolution as a non-stationary Markov process with uncertainty matrices decomposed into outflow and tendency probability matrices, where outflow probabilities are constrained by the non-ride-sharing cars behavior, but the tendency probabilities are considered as decision variables (or distributed controls) for achieving an equitable distribution of ride-sharing cars in a congested traffic.
Safe Exit Controllers Synthesis for Continuous-time Stochastic Systems
Authors: Bai Xue
Date: 2023-10-08 00:00:00

This paper tackles the problem of generating safe exit controllers for continuous-time systems described by stochastic differential equations (SDEs). The primary aim is to develop controllers that maximize the lower bounds of the exit probability that the system escapes from a safe but uncomfortable set within a specified time frame and guide it towards a comfortable set. The paper considers two distinct cases: one in which the boundary of the safe set is a subset of the boundary of the uncomfortable set, and the other where the boundaries of the two sets do not intersect. To begin, we present a sufficient condition for establishing lower bounds on the exit probability in the first case. This condition serves as a guideline for constructing an online linear programming problem. The linear programming problem is designed to implicitly synthesize an optimal exit controller that maximizes the lower bounds of the exit probability. The method employed in the first case is then extended to the second one. Finally, we demonstrate the effectiveness of the proposed approaches on one example.
Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control
Authors: Qiuhua Huang, Renke Huang, Tianzhixi Yin, Sohom Datta, Xueqing Sun, Jason Hou, Jie Tan, Wenhao Yu, YuAn Liu, Xinya Li, Bruce Palmer, Ang Li, Xinda Ke, Marianna Vaiman, Song Wang, Yousu Chen
Date: 2023-10-08 00:00:00

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.
Complementing AC & DC Terminal Stability Analyses of MMC with Inner Loop Impedance
Authors: Chongbin Zhao, Qirong Jiang
Date: 2023-10-08 00:00:00

Learning from two-level voltage source converters, the existing impedance-based stability analyses of modular multilevel converters (MMCs) primarily focus on system modes with finite closed-loop transfer functions, which consider perturbations of the current flowing into the public AC/DC terminal as the input. However, this approach may be insufficient for MMCs due to their actively controlled circulating circuit, resulting from the distributed modulation of each arm and the circulating current control (CCC). To address this limitation, two cases that are not covered by the AC/DC terminal stability analysis are initially presented to support the conjecture. Subsequently, an inner loop impedance for the circulating circuit is established, which considers the dynamics of public terminals and divides the injected voltage perturbation by the corresponding current perturbation at the same frequency. To avoid the need for a right-half plane pole check when applying the Nyquist criterion, a logarithmic derivative-based criterion is proposed to directly identify the system modes. By utilizing the inner loop impedance, it becomes possible to achieve CCC parameter tuning with stability constraints and conduct an internal stability analysis of MMC-based systems. This work provides a strong foundation for the integration of power-electronicized power systems from the perspective of classical control theories.
Secondary frequency control of islanded microgrid considering wind and solar stochastics
Authors: Cheng Zhong, Zhifu Jiang, Xiangyu Zhang, Jikai Chen, Yang Li
Date: 2023-10-08 00:00:00
Tasks: Model Predictive Control

This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reduce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios.
Reach-avoid Analysis for Sampled-data Systems with Measurement Uncertainties
Authors: Taoran Wu, Dejin Ren, Shuyuan Zhang, Lei Wang, Bai Xue
Date: 2023-10-08 00:00:00

Digital control has become increasingly prevalent in modern systems, making continuous-time plants controlled by discrete-time (digital) controllers ubiquitous and crucial across industries, including aerospace, automotive, and manufacturing. This paper focuses on investigating the reach-avoid problem in such systems, where the objective is to reach a goal set while avoiding unsafe states, especially in the presence of state measurement uncertainties. We propose an approach that builds upon the concept of exponential control guidance barrier functions, originally used for synthesizing continuous-time feedback controllers. We introduce a sufficient condition that, if met by a given continuous-time feedback controller, ensures the safe guidance of the system into the goal set in its sampled-data implementation, despite state measurement uncertainties. The event of reaching the goal set is determined based on state measurements obtained at the sampling time instants. Numerical examples are provided to demonstrate the validity of our theoretical developments, showcasing successful implementation in solving the reach-avoid problem in sampled-data systems with state measurement uncertainties.
Robust Multivariate Detection and Estimation with Fault Frequency Content Information
Authors: Jingwei Dong, Kaikai Pan, Sergio Pequito, Peyman Mohajerin Esfahani
Date: 2023-10-07 00:00:00
Tasks: Fault Detection

This paper studies the problem of fault detection and estimation (FDE) for linear time-invariant (LTI) systems with a particular focus on frequency content information of faults, possibly as multiple disjoint continuum ranges, and under both disturbances and stochastic noise. To ensure the worst-case fault sensitivity in the considered frequency ranges and mitigate the effects of disturbances and noise, an optimization framework incorporating a mixed H_/H2 performance index is developed to compute the optimal detection filter. Moreover, a thresholding rule is proposed to guarantee both the false alarm rate (FAR) and the fault detection rate (FDR). Next, shifting attention to fault estimation in specific frequency ranges, an exact reformulation of the optimal estimation filter design using the restricted Hinf performance index is derived, which is inherently non-convex. However, focusing on finite frequency samples and fixed poles, a lower bound is established via a highly tractable quadratic programming (QP) problem. This lower bound together with an alternating optimization (AO) approach to the original estimation problem leads to a suboptimality gap for the overall estimation filter design. The effectiveness of the proposed approaches is validated through applications of a non-minimum phase hydraulic turbine system and a multi-area power system.
Multi-Spacecraft Predictive Sensor Tasking for Cislunar Space Situational Awareness
Authors: Kento Tomita, Yuri Shimane, Koki Ho
Date: 2023-10-07 00:00:00

This paper delves into the predictive sensor tasking algorithm for the multi-observer, multi-target sensor setting, leveraging the Extended Information Filter (EIF). Conventional predictive formulations suffer from the curse of dimensionality due to the dependence of the performance metric on the target-observer assignment history. This paper exploits the EIF's additive structure of measurement information to break the dependence and devises an efficient linear integer programming formulation. We further investigate the resulting formulation to study how the cislunar dynamics expands and shrinks the measurement information, and discuss when the information gain is maximized in relation to the observation space and the uncertainty deformation caused by the dynamics. We numerically demonstrate that the predictive sensor tasking algorithm outperforms the myopic algorithm in two different metrics, depending on the formulation.
A Closed-form Solution for the Strapdown Inertial Navigation Initial Value Problem
Authors: James Goppert, Li-Yu Lin, Kartik Pant, Benjamin Perseghetti
Date: 2023-10-07 00:00:00
Tasks: Form

Strapdown inertial navigation systems (SINS) are ubiquitious in robotics and engineering since they can estimate a rigid body pose using onboard kinematic measurements without knowledge of the dynamics of the vehicle to which they are attached. While recent work has focused on the closed-form evolution of the estimation error for SINS, which is critical for Kalman filtering, the propagation of the kinematics has received less attention. Runge-Kutta integration approaches have been widely used to solve the initial value problem; however, we show that leveraging the special structure of the SINS problem and viewing it as a mixed-invariant vector field on a Lie group, yields a closed form solution. Our closed form solution is exact given fixed gyroscope and accelerometer measurements over a sampling period, and it is utilizes 12 times less floating point operations compared to a single integration step of a 4th order Runge-Kutta integrator. We believe the wide applicability of this work and the efficiency and accuracy gains warrant general adoption of this algorithm for SINS.
Review of Machine Learning Techniques for Power Electronics Control and Optimization
Authors: Maryam Bahrami, Zeyad Khashroum
Date: 2023-10-07 00:00:00

In the rapidly advancing landscape of contemporary technology, power electronics assume a pivotal role across diverse applications, ranging from renewable energy systems to electric vehicles and consumer electronics. The efficacy and precision of these power electronics systems stand as cornerstones of their functionality. Within this context, the integration of machine learning techniques assumes paramount significance. This article endeavors to present an extensive and comprehensive review of the machine learning techniques that find application in power electronics control and optimization. Through meticulous exploration, we aim to elucidate the profound potential of these methods in shaping the future of power electronics control and optimization.
An Operational Data-Driven Malfunction Detection Framework for Enhanced Power Distribution System Monitoring -- The DeMaDs Approach
Authors: David Fellner, Thomas I. Strasser, Wolfgang Kastner, Feizifar Behnam, Ibrahim F. Abdulhadi
Date: 2023-10-06 00:00:00

The changes in the electric energy system toward a sustainable future are inevitable and already on the way today. This often entails a change of paradigm for the electric energy grid, for example, the switch from central to decentralized power generation which also has to provide grid-supporting functionalities. However, due to the scarcity of distributed sensors, new solutions for grid operators for monitoring these functionalities are needed. The framework presented in this work allows to apply and assess data-driven detection methods in order to implement such monitoring capabilities. Furthermore, an approach to a multi-stage detection of misconfigurations is introduced. Details on implementations of the single stages as well as their requirements are also presented. Furthermore, testing and validation results are discussed. Due to its feature of being seamlessly integrable into system operators' current metering infrastructure, clear benefits of the proposed solution are pointed out.
Real-time Optical Network Sensing Control Plane Enabled by a Novel Sub us Response Time Fibre Sensing Control Device
Authors: Mijail Szczerban, Mikael Mazur, Lauren Dallachiesa, Haïk Mardoyan, Sarvesh Bidkar, Roland Ryf, Jesse Simsarian
Date: 2023-10-06 00:00:00

We propose and implement a novel fibre sensing control device and associated sensing control plane that effectively controls backscatter and polarization-based fibre sensing. We experimentally demonstrate in a fibre network that this device and associated control plane can achieve sub-us response time.
Vulnerability Analysis of Nonlinear Control Systems to Stealthy False Data Injection Attacks
Authors: Amir Khazraei, Miroslav Pajic
Date: 2023-10-06 00:00:00
Tasks: State Estimation

In this work, we focus on analyzing vulnerability of nonlinear dynamical control systems to stealthy false data injection attacks on sensors. We start by defining the stealthiness notion in the most general form where an attack is considered stealthy if it would be undetected by any intrusion detector, i.e., any intrusion detector could not do better than a random guess. Depending on the level of attacker's knowledge about the plant model, controller, and the system states, two different attack models are considered. For each attack model, we derive the conditions for which the system will be vulnerable to stealthy impactful attacks, in addition to finding a methodology for designing such sequence of false data injection attacks. When the attacker has complete knowledge about the system, we show that if the closed loop system is incrementally exponentially stable while the open loop plant is incrementally unstable, then the system is vulnerable to stealthy yet impactful attacks on sensors. However, in the second attack model, with less knowledge about the system, additional conditions need to be satisfied and the level of stealthiness depends on the accuracy of attacker's knowledge about the system. We also consider the impact of stealthy attacks on state estimation, and show that if the closed loop control system including the estimator is incrementally stable, then the state estimation in the presence of attack converges to the attack free estimates. Finally, we illustrate our results on numerical case studies.
Full-scale modal testing of a Hawk T1A aircraft for benchmarking vibration-based methods
Authors: Marcus Haywood-Alexander, Robin S. Mills, Max D. Champneys, Matthew R. Jones, Matthew S. Bonney, David Wagg, Timothy J. Rogers
Date: 2023-10-06 00:00:00
Tasks: Benchmarking, Experimental Design, Structural Health Monitoring

Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what can be learned from measured vibration data. However, significant challenges remain in the tasks of identification, prediction and evaluation of full-scale structures. A significant aid in the roadmap to the application of cutting-edge methods to the demands of in-service engineering structures, is the development of comprehensive benchmark datasets. With the aim of developing a useful and worthwhile benchmark dataset for structural dynamics, an extensive testing campaign is presented here. This recent campaign was performed on a decommissioned BAE system Hawk T1A aircraft at the Laboratory for Verification and Validation (LVV) in Sheffield. The aim of this paper is to present the dataset, providing details on the structure, experimental design, and data acquired. The collected data is made freely and openly available with the intention that it serve as a benchmark dataset for challenges in full-scale structural dynamics. Here, the details pertaining to two test phases (frequency and time domain) are presented. So as to ensure that the presented dataset is able to function as a benchmark, some baseline-level results are additionally presented for the tasks of identification and prediction, using standard approaches. It is envisaged that advanced methodologies will demonstrate superiority by favourable comparison with the results presented here. Finally, some dataset-specific challenges are described, with a view to form a hierarchy of tasks and frame discussion over their relative difficulty.
The Optimal use of Segmentation for Sampling Calorimeters
Authors: Fernando Torales Acosta, Bishnu Karki, Piyush Karande, Aaron Angerami, Miguel Arratia, Kenneth Barish, Ryan Milton, Sebastián Morán, Benjamin Nachman, Anshuman Sinha
Date: 2023-10-02 00:00:00
Tasks: Segmentation

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. To inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.