pwc-archive/papers-with-abstracts huggingface dataset:
Ba-ZebraConf: A Three-Dimension Bayesian Framework for Efficient System Troubleshooting
Authors: Deyi Xing, Weicong Chen, Curtis Tatsuoka, Xiaoyi Lu
Date: 2024-12-15 00:00:00
Tasks: Bayesian Optimization

The proliferation of heterogeneous configurations in distributed systems presents significant challenges in ensuring stability and efficiency. Misconfigurations, driven by complex parameter interdependencies, can lead to critical failures. Group Testing (GT) has been leveraged to expedite troubleshooting by reducing the number of tests, as demonstrated by methods like ZebraConf. However, ZebraConf's binary-splitting strategy suffers from sequential testing, limited handling of parameter interdependencies, and susceptibility to errors such as noise and dilution. We propose Ba-ZebraConf, a novel three-dimensional Bayesian framework that addresses these limitations. It integrates (1) Bayesian Group Testing (BGT), which employs probabilistic lattice models and the Bayesian Halving Algorithm (BHA) to dynamically refine testing strategies, prioritizing high-informative parameters and adapting to real-time outcomes. Bayesian optimization tunes hyperparameters, such as pool sizes and test thresholds, to maximize testing efficiency. (2) Bayesian Optimization (BO) to automate hyperparameter tuning for test efficiency, and (3) Bayesian Risk Refinement (BRR) to iteratively capture parameter interdependencies and improve classification accuracy. Ba-ZebraConf adapts to noisy environments, captures parameter interdependencies, and scales effectively for large configuration spaces. Experimental results show that Ba-ZebraConf reduces test counts and execution time by 67% compared to ZebraConf while achieving 0% false positives and false negatives. These results establish Ba-ZebraConf as a robust and scalable solution for troubleshooting heterogeneous distributed systems.
Distributed Estimation with Quantized Measurements and Communication over Markovian Switching Topologies
Authors: Ying Wang, Jian Guo, Yanlong Zhao, Ji-Feng Zhang
Date: 2024-12-14 00:00:00
Tasks: parameter estimation

This paper addresses distributed parameter estimation in stochastic dynamic systems with quantized measurements, constrained by quantized communication and Markovian switching directed topologies. To enable accurate recovery of the original signal from quantized communication signal, a persistent excitation-compliant linear compression encoding method is introduced. Leveraging this encoding, this paper proposes an estimation-fusion type quantized distributed identification algorithm under a stochastic approximation framework. The algorithm operates in two phases: first, it estimates neighboring estimates using quantized communication information, then it creates a fusion estimate by combining these estimates through a consensus-based distributed stochastic approximation approach. To tackle the difficulty caused by the coupling between these two estimates, two combined Lyapunov functions are constructed to analyze the convergence performance. Specifically, the mean-square convergence of the estimates is established under a conditional expectation-type cooperative excitation condition and the union topology containing a spanning tree. Besides, the convergence rate is derived to match the step size's order under suitable step-size coefficients. Furthermore, the impact of communication uncertainties including stochastic communication noise and Markov-switching rate is analyzed on the convergence rate. A numerical example illustrates the theoretical findings and highlights the joint effect of sensors under quantized communication.
Interharmonic Power: A New Concept for Power System Oscillation Source Location
Authors: Wilsun Xu, Jing Yong, Horacio J. Marquez, Chun Li
Date: 2024-12-14 00:00:00

Power system oscillations are a significant concern for system operators, a problem that has grown due to the interconnection of inverter-based resources. To address this issue, various methods have been proposed to locate the sources of oscillations, which is essential for effective mitigation actions. A common characteristic of these methods is that they rely on phasor representation of oscillation phenomena. This paper takes a different approach by examining the actual voltage and current waveforms underlying the phasors. It is found that the presence of interharmonic components is both the necessary and sufficient condition for phasor oscillations. Moreover, the generation and propagation of interharmonic powers are identified as the true culprits behind power system oscillations and oscillatory instability. Based on these insights, two new methods are developed for locating oscillation sources: one for measurement-based monitoring applications and another for model-based system studies. These findings are validated through four field data-based and one simulation-based case studies.
Physics-based battery model parametrisation from impedance data
Authors: Noël Hallemans, Nicola E. Courtier, Colin P. Please, Brady Planden, Rishit Dhoot, Robert Timms, S. Jon chapman, David Howey, Stephen R. Duncan
Date: 2024-12-14 00:00:00

Non-invasive parametrisation of physics-based battery models can be performed by fitting the model to electrochemical impedance spectroscopy (EIS) data containing features related to the different physical processes. However, this requires an impedance model to be derived, which may be complex to obtain analytically. We have developed the open-source software PyBaMM-EIS that provides a fast method to compute the impedance of any PyBaMM model at any operating point using automatic differentiation. Using PyBaMM-EIS, we investigate the impedance of the single particle model, single particle model with electrolyte (SPMe), and Doyle-Fuller-Newman model, and identify the SPMe as a parsimonious option that shows the typical features of measured lithium-ion cell impedance data. We provide a grouped parameter SPMe and analyse the features in the impedance related to each parameter. Using the open-source software PyBOP, we estimate 18 grouped parameters both from simulated impedance data and from measured impedance data from a LG M50LT lithium-ion battery. The parameters that directly affect the response of the SPMe can be accurately determined and assigned to the correct electrode. Crucially, parameter fitting must be done simultaneously to data across a wide range of states-of-charge. Overall, this work presents a practical way to find the parameters of physics-based models.
Economic MPC with an Online Reference Trajectory for Battery Scheduling Considering Demand Charge Management
Authors: Cristian Cortes-Aguirre, Yi-An Chen, Avik Ghosh, Jan Kleissl, Adil Khurram
Date: 2024-12-14 00:00:00
Tasks: Management, Model Predictive Control, Scheduling

Monthly demand charges form a significant portion of the electric bill for microgrids with variable renewable energy generation. A battery energy storage system (BESS) is commonly used to manage these demand charges. Economic model predictive control (EMPC) with a reference trajectory can be used to dispatch the BESS to optimize the microgrid operating cost. Since demand charges are incurred monthly, EMPC requires a full-month reference trajectory for asymptotic stability guarantees that result in optimal operating costs. However, a full-month reference trajectory is unrealistic from a renewable generation forecast perspective. Therefore, to construct a practical EMPC with a reference trajectory, an EMPC formulation considering both non-coincident demand and on-peak demand charges is designed in this work for 24 to 48 h prediction horizons. The corresponding reference trajectory is computed at each EMPC step by solving an optimal control problem over 24 to 48 h reference (trajectory) horizon. Furthermore, BESS state of charge regulation constraints are incorporated to guarantee the BESS energy level in the long term. Multiple reference and prediction horizon lengths are compared for both shrinking and rolling horizons with real-world data. The proposed EMPC with 48 h rolling reference and prediction horizons outperforms the traditional EMPC benchmark with a 2% reduction in the annual cost, proving its economic benefits.
Lyapunov-based reinforcement learning for distributed control with stability guarantee
Authors: Jingshi Yao, Minghao Han, Xunyuan Yin
Date: 2024-12-14 00:00:00
Tasks: Chemical Process, reinforcement-learning, Reinforcement Learning

In this paper, we propose a Lyapunov-based reinforcement learning method for distributed control of nonlinear systems comprising interacting subsystems with guaranteed closed-loop stability. Specifically, we conduct a detailed stability analysis and derive sufficient conditions that ensure closed-loop stability under a model-free distributed control scheme based on the Lyapunov theorem. The Lyapunov-based conditions are leveraged to guide the design of local reinforcement learning control policies for each subsystem. The local controllers only exchange scalar-valued information during the training phase, yet they do not need to communicate once the training is completed and the controllers are implemented online. The effectiveness and performance of the proposed method are evaluated using a benchmark chemical process that contains two reactors and one separator.
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
Authors: Jia Hu, Xuerun Yan, Tian Xu, Haoran Wang
Date: 2024-12-14 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) offers a promising solution to enable evolutionary automated driving. However, the conventional RL method is always concerned with risk performance. The updated policy may not obtain a performance enhancement, even leading to performance deterioration. To address this challenge, this research proposes a High Confidence Policy Improvement Reinforcement Learning-based (HCPI-RL) planner. It is intended to achieve the monotonic evolution of automated driving. A novel RL policy update paradigm is designed to enable the newly learned policy performance consistently surpass that of previous policies, which is deemed as monotonic performance enhancement. Hence, the proposed HCPI-RL planner has the following features: i) Evolutionary automated driving with monotonic performance enhancement; ii) With the capability of handling scenarios with emergency; iii) With enhanced decision-making optimality. Results demonstrate that the proposed HCPI-RL planner enhances the policy return by 44.7% in emergent cut-in scenarios, 108.2% in emergent braking scenarios, and 64.4% in daily cruising scenarios, compared to the PPO planner. Adopting the proposed planner, automated driving efficiency is enhanced by 19.2% compared to the PPO planner, and by 30.7% compared to the rule-based planner.
Inertia-aware Unit Commitment and Remuneration Methods for Decarbonized Power System
Authors: Hyunjoong Kim, Jip Kim
Date: 2024-12-14 00:00:00

To maintain frequency stability in decarbonized power systems, inertia services from synchronous generators (SGs) and inverter-based resources must be procured. However, designing an inertia-aware system operation poses significant challenges in considering the variability and uncertainty of renewable energy sources (RES) and adopting a remuneration method for inertia provision due to SG commitment variables. To address this research gap, we renovate the inertia-aware chance constraints unit commitment model by incorporating time-coupling constraints for SGs and joint chance constraints for RES uncertainty. We investigate remuneration methods for inertia provision, including uplift, marginal pricing (MP), approximated convex hull pricing (aCHP), and average incremental cost pricing (AIP), applying these to the renovated model. Numerical experiments show that the model enhances frequency stability during a contingency. Among the remuneration methods, only aCHP guarantees revenue adequacy without requiring uplift while maximizing economic welfare. However, the MP requires the highest level of uplift to adequately compensate generation costs, as the price function fails to account for inertia provision.
Impact of Trip Distance Distribution Time Dependency and Aggregation Levels in Bathtub Models -- A Comparative Simulation Analysis
Authors: Jiayi Guo, Irene Martínez, Gonçalo Correia, Bart van Arem
Date: 2024-12-14 00:00:00

Bathtub models are used to study urban traffic within a certain area. They do not require to take into account the detailed network topology. The emergence of different bathtub models has raised the question of which model can provide more robust and accurate results under different demand scenarios and network properties. This paper presents a comparative simulation analysis of the accumulation-based model and trip-based models under static and dynamic trip distance distribution (TDD) scenarios. Network accumulation was used to validate and compare the performance of the bathtub models with results from the macroscopic traffic simulation with dynamic traffic assignment. Three networks were built to explore the effect of network properties on the accuracy of bathtub models. Two are from the network of Delft, the Netherlands, and one is a reference toy network. The findings show that the time dependency of TDD can increase the errors in bathtub models. Using TDD in different aggregation levels can significantly influence the performance of bathtub models during demand transition periods. The state transition speed of networks is also found to be influential. Future research could explore the effects of dynamic TDD under congested situations and develop enhanced bathtub models that can better account for different network state transition speeds.
$L_1$-Based Adaptive Identification with Saturated Observations
Authors: Xin Zheng, Lei Guo
Date: 2024-12-14 00:00:00
Tasks: parameter estimation, Prediction

It is well-known that saturated output observations are prevalent in various practical systems and that the $\ell_1$-norm is more robust than the $\ell_2$-norm-based parameter estimation. Unfortunately, adaptive identification based on both saturated observations and the $\ell_1$-optimization turns out to be a challenging nonlinear problem, and has rarely been explored in the literature. Motivated by this and the need to fit with the $\ell_1$-based index of prediction accuracy in, e.g., judicial sentencing prediction problems, we propose a two-step weighted $\ell_1$-based adaptive identification algorithm. Under certain excitation conditions much weaker than the traditional persistent excitation (PE) condition, we will establish the global convergence of both the parameter estimators and the adaptive predictors. It is worth noting that our results do not rely on the widely used independent and identically distributed (iid) assumptions on the system signals, and thus do not exclude applications to feedback control systems. We will demonstrate the advantages of our proposed new adaptive algorithm over the existing $\ell_2$-based ones, through both a numerical example and a real-data-based sentencing prediction problem.
An Active Parameter Learning Approach to The Identification of Safe Regions
Authors: Aneesh Raghavan, Karl H Johansson
Date: 2024-12-14 00:00:00
Tasks: Active Learning

We consider the problem of identification of safe regions in the environment of an autonomous system. The environment is divided into a finite collections of Voronoi cells, with each cell having a representative, the Voronoi center. The extent to which each region is considered to be safe by an oracle is captured through a trust distribution. The trust placed by the oracle conditioned on the region is modeled through a Bernoulli distribution whose the parameter depends on the region. The parameters are unknown to the system. However, if the agent were to visit a given region, it will receive a binary valued random response from the oracle on whether the oracle trusts the region or not. The objective is to design a path for the agent where, by traversing through the centers of the cells, the agent is eventually able to label each cell safe or unsafe. To this end, we formulate an active parameter learning problem with the objective of minimizing visits or stays in potentially unsafe regions. The active learning problem is formulated as a finite horizon stochastic control problem where the cost function is derived utilizing the large deviations principle (LDP). The challenges associated with a dynamic programming approach to solve the problem are analyzed. Subsequently, the optimization problem is relaxed to obtain single-step optimization problems for which closed form solution is obtained. Using the solution, we propose an algorithm for the active learning of the parameters. A relationship between the trust distributions and the label of a cell is defined and subsequently a classification algorithm is proposed to identify the safe regions. We prove that the algorithm identifies the safe regions with finite number of visits to unsafe regions. We demonstrate the algorithm through an example.
Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach
Authors: Zhirui Liang, Qi Li, Anqi Liu, Yury Dvorkin
Date: 2024-12-13 00:00:00
Tasks: Uncertainty Quantification

This paper presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs in two-stage power markets, where the first stage involves a distributionally robust optimal power flow (DR-OPF) model. Unlike traditional methods that adjust forecast parameters and uncertainty quantification (UQ) separately, this framework jointly optimizes both the forecast model parameters and the decision conservativeness, which determines the size of the ambiguity set in the DR-OPF model. The framework aligns UQ with actual uncertainty realizations by directly optimizing downstream operational costs, a process referred to as cost-oriented calibration. The calibration is achieved using a gradient descent approach. To enable efficient differentiation, the DR-OPF problem is reformulated into a convex form, and the Envelope Theorem is leveraged to simplify gradient derivation in the two-stage setting. Additionally, the framework supports distributed implementation, enhancing data privacy and reducing computational overhead. By proactively calibrating forecast parameters and prescribing optimal decision conservativeness, the framework significantly enhances cost efficiency and reliability in power system operations. Numerical experiments on an IEEE 5-bus system demonstrate the effectiveness and efficiency of the proposed approach.
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection
Authors: Kyle Dunlap, Nathaniel Hamilton, Kerianne L. Hobbs
Date: 2024-12-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As the number of spacecraft in orbit continues to increase, it is becoming more challenging for human operators to manage each mission. As a result, autonomous control methods are needed to reduce this burden on operators. One method of autonomous control is Reinforcement Learning (RL), which has proven to have great success across a variety of complex tasks. For missions with multiple controlled spacecraft, or agents, it is critical for the agents to communicate and have knowledge of each other, where this information is typically given to the Neural Network Controller (NNC) as an input observation. As the number of spacecraft used for the mission increases or decreases, rather than modifying the size of the observation, this paper develops a scalable observation space that uses a constant observation size to give information on all of the other agents. This approach is similar to a lidar sensor, where determines ranges of other objects in the environment. This observation space is applied to a spacecraft inspection task, where RL is used to train multiple deputy spacecraft to cooperate and inspect a passive chief spacecraft. It is expected that the scalable observation space will allow the agents to learn to complete the task more efficiently compared to a baseline solution where no information is communicated between agents.
Invariant Kalman Filter for Relative Dynamics
Authors: Tejaswi K. C., Maneesha Wickramasuriya, Taeyoung Lee
Date: 2024-12-13 00:00:00

This paper presents an invariant Kalman filter for estimating the relative trajectories between two dynamic systems. Invariant Kalman filters formulate the estimation error in terms of the group operation, ensuring that the error state does not depend on the current state estimate - a property referred to as state trajectory independence. This is particularly advantageous in extended Kalman filters, as it makes the propagation of the error covariance robust to large estimation errors. In this work, we construct invariant Kalman filters to the trajectory of one system relative to another. Specifically, we show that if the relative dynamics can be described solely by relative state variables, they automatically satisfy state trajectory independence, allowing for the development of an invariant Kalman filter. The corresponding relative invariant Kalman filter is formulated in an abstract fashion and is demonstrated numerically for the attitude dynamics of a rigid body.
Model Order Reduction of Large-Scale Wind Farms: A Data-Driven Approach
Authors: Zilong Gong, Junyu Mao, Adrià Junyent-Ferré, Giordano Scarciotti
Date: 2024-12-13 00:00:00

This paper proposes a data-driven algorithm for model order reduction (MOR) of large-scale wind farms and studies the effects that the obtained reduced-order model (ROM) has when this is integrated into the power grid. With respect to standard MOR methods, the proposed algorithm has the advantages of having low computational complexity and not requiring any knowledge of the high order model. Using time-domain measurements, the obtained ROM achieves the moment matching conditions at selected interpolation points (frequencies). With respect to the state of the art, the method achieves the so-called two-sided moment matching, doubling the accuracy by doubling the interpolated points. The proposed algorithm is validated on a combined model of a 200-turbine wind farm (which is reduced) interconnected to the IEEE 14-bus system (which represents the unreduced study area) by comparing the full-order model and the reduced-order model in terms of their Bode plots, eigenvalues and the point of common coupling voltages in extensive fault scenarios of the integrated power system.
Exploiting structural observability and graph colorability for optimal sensor placement in water distribution networks
Authors: J. J. H. van Gemert, V. Breschi, D. R. Yntema, K. J. Keesman, M. Lazar
Date: 2024-12-13 00:00:00

Water distribution networks (WDNs) are critical systems for our society and detecting leakages is important for minimizing losses and water waste. This makes optimal sensor placement for leakage detection very relevant. Existing sensor placement methods rely on simulation-based scenarios, often lacking structure and generalizability, or depend on the knowledge of specific parameters of the WDN as well as on initial sensor data for linearization and demand estimation. Motivated by this, this paper investigates the observability of an entire WDN, based on structural observability theory. This allows us to establish the conditions for the observability of the WDN model, independently of parameter uncertainties and without the need for initial sensor data. Additionally, a sensor placement algorithm is proposed, that leverages such observability conditions and graph theory and accounts for the industrial and material costs. To demonstrate the effectiveness of our approach, we apply it to a hydraulic-transient WDN model.