pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Modeling and Stability Analysis for Repeated LVRT Process of Wind Turbine Based on Switched System Theory
Authors: Qiping Lai, Chen Shen, Dongsheng Li
Date: 2024-04-01 00:00:00

The significant electrical distance between wind power collection points and the main grid poses challenges for weak grid-connected wind power systems. A new type of voltage oscillation phenomenon induced by repeated low voltage ride-through (LVRT) of the wind turbine has been observed, threatening the safe and stable operation of such power systems. Therefore, exploring dynamic evolution mechanisms and developing stability analysis approaches for this phenomenon have become pressing imperatives. This paper introduces switched system theory for dynamic modeling, mechanism elucidation, and stability analysis of the repeated LVRT process. Firstly, considering the external connection impedance and internal control dynamics, a novel wind turbine grid-side converter (WT-GSC) switched system model is established to quantitatively characterize the evolution dynamic and mechanism of the voltage oscillation. Subsequently, a sufficient stability criterion and index grounded in the common Lyapunov function are proposed for stability analysis and assessment of the WT-GSC switched system. Moreover, to enhance the system stability, the Sobol' global sensitivity analysis method is adopted to identify dominant parameters, which can be further optimized via the particle swarm optimization (PSO) algorithm. Finally, simulations conducted on a modified IEEE 39-bus test system verify the effectiveness of the proposed dynamic modeling and stability analysis methods.
Probability-Based Optimal Control Design for Soft Landing of Short-Stroke Actuators
Authors: Eduardo Moya-Lasheras, Edgar Ramirez-Laboreo, Carlos Sagues
Date: 2024-04-01 00:00:00
Tasks: Position, Trajectory Planning

The impact forces during switching operations of short-stroke actuators may cause bouncing, audible noise and mechanical wear. The application of soft-landing control strategies to these devices aims at minimizing the impact velocities of their moving components to ultimately improve their lifetime and performance. In this paper, a novel approach for soft-landing trajectory planning, including probability functions, is proposed for optimal control of the actuators. The main contribution of the proposal is that it considers uncertainty in the contact position and hence the obtained trajectories are more robust against system uncertainties. The problem is formulated as an optimal control problem and transformed into a two-point boundary value problem for its numerical resolution. Simulated and experimental tests have been performed using a dynamic model and a commercial short-stroke solenoid valve. The results show a significant improvement in the expected velocities and accelerations at contact with respect to past solutions in which the contact position is assumed to be perfectly known.
Second-Order Newton-Based Extremum Seeking for Multivariable Static Maps
Authors: Azad Ghaffari, Tiago Roux Oliveira
Date: 2024-04-01 00:00:00

A second-order Newton-based extremum seeking (SONES) algorithm is presented to estimate directional inflection points for multivariable static maps. The design extends the first-order Newton-based extremum seeking algorithm that drives the system toward its peak point. This work provides perturbation matrices to estimate the second- and third-order derivatives necessary for implementation of the SONES. A set of conditions are provided for the probing frequencies that ensure accurate estimation of the derivatives. A differential Riccati filter is used to calculate the inverse of the third-order derivative. The local stability of the new algorithm is proven for general multivariable static maps using averaging analysis. The proposed algorithm ensures uniform convergence toward directional inflection point without requiring information about the curvature of the map and its gradient. Simulation results show the effectiveness of the proposed algorithm.
Optimal Bidding Strategies in Network-Constrained Demand Response: A Distributed Aggregative Game Theoretic Approach
Authors: Xiupeng Chen, Jacquelien M. A. Scherpen, Nima Monshizadeh
Date: 2024-04-01 00:00:00

Demand response has been a promising solution for accommodating renewable energy in power systems. In this study, we consider a demand response scheme within a distribution network facing an energy supply deficit. The utility company incentivizes load aggregators to adjust their pre-scheduled energy consumption and generation to match the supply. Each aggregator, which represents a group of prosumers, aims to maximize its revenue by bidding strategically in the demand response scheme. Since aggregators act in their own self-interest and their revenues and feasible bids influence one another, we model their competition as a network-constrained aggregative game. This model incorporates power flow constraints to prevent potential line congestion. Given that there are no coordinators and aggregators can only communicate with their neighbours, we introduce a fully distributed generalized Nash equilibrium seeking algorithm to determine the optimal bidding strategies for aggregators in this game. Within this algorithm, only estimates of the aggregate and certain auxiliary variables are communicated among neighbouring aggregators. We demonstrate the convergence of this algorithm by constructing an equivalent iteration using the forward-backward splitting technique.
Orchestrating UAVs for Prioritized Data Harvesting: A Cross-Layer Optimization Perspective
Authors: Bharath Keshavamurthy, Nicolo Michelusi
Date: 2024-04-01 00:00:00
Tasks: Scheduling, Traveling Salesman Problem

This work describes the orchestration of a fleet of rotary-wing Unmanned Aerial Vehicles (UAVs) for harvesting prioritized traffic from random distributions of heterogeneous users with Multiple Input Multiple Output (MIMO) capabilities. In a finite-horizon offline setting, the goal is to optimize the beam-forming design, the 3D UAV positioning and trajectory solution, and the user association/scheduling policy, to maximize the cumulative fleet-wide reward obtained by satisfying the quality-of-service mandates imposed on each user uplink request, subject to an average per-UAV mobility power constraint. With a probabilistic air-to-ground channel model, a multi-user MIMO uplink communication model with prioritized traffic, and a novel 3D mobility model for rotary-wing UAVs, the fleet-wide reward maximization problem is solved via a cross-layer optimization framework: first, K-means clustering is employed to obtain user clusters; then, equipped with a zero-forcing beam-forming design, the positions of the UAVs are optimized via two-stage grid search; next, treating these optimal positions as the graph vertices of a fully-connected mesh, the 3D UAV trajectories (i.e., graph edges) are designed via a learning based competitive swarm optimization algorithm, under an average UAV power consumption constraint, coupled with projected subgradient ascent for dual optimization; consequently, the user association/scheduling strategy is solved via a graphical branch-and-bound method on the underlying multiple traveling salesman problem. Numerical evaluations demonstrate that the proposed solution outperforms static UAV deployments, adaptive Voronoi decomposition techniques, and state-of-the-art iterative fleet control algorithms, with respect to user quality-of-service and per-UAV average power consumption.
Scalable second-order consensus of hierarchical groups
Authors: Jiamin Wang, Jian Liu, Feng Xiao, Ning Xi, Yuanshi Zheng
Date: 2024-03-31 00:00:00

Motivated by widespread dominance hierarchy, growth of group sizes, and feedback mechanisms in social species, we are devoted to exploring the scalable second-order consensus of hierarchical groups. More specifically, a hierarchical group consists of a collection of agents with double-integrator dynamics on a directed acyclic graph with additional reverse edges, which characterize feedback mechanisms across hierarchical layers. As the group size grows and the reverse edges appear, we investigate whether the absolute velocity protocol and the relative velocity protocol can preserve the system consensus property without tuning the control gains. It is rigorously proved that the absolute velocity protocol is able to achieve completely scalable second-order consensus but the relative velocity protocol cannot. This result theoretically reveals how the scalable coordination behavior in hierarchical groups is determined by local interaction rules. Moreover, we develop a hierarchical structure in order to achieve scalable second-order consensus for networks of any size and with any number of reverse edges.
Hierarchical Climate Control Strategy for Electric Vehicles with Door-Opening Consideration
Authors: Sanghyeon Nam, Hyejin Lee, Youngki Kim, Kyoung hyun Kwak, Kyoungseok Han
Date: 2024-03-31 00:00:00
Tasks: Management, Model Predictive Control

This study proposes a novel climate control strategy for electric vehicles (EVs) by addressing door-opening interruptions, an overlooked aspect in EV thermal management. We create and validate an EV simulation model that incorporates door-opening scenarios. Three controllers are compared using the simulation model: (i) a hierarchical non-linear model predictive control (NMPC) with a unique coolant dividing layer and a component for cabin air inflow regulation based on door-opening signals; (ii) a single MPC controller; and (iii) a rule-based controller. The hierarchical controller outperforms, reducing door-opening temperature drops by 46.96% and 51.33% compared to single layer MPC and rule-based methods in the relevant section. Additionally, our strategy minimizes the maximum temperature gaps between the sections during recovery by 86.4% and 78.7%, surpassing single layer MPC and rule-based approaches, respectively. We believe that this result opens up future possibilities for incorporating the thermal comfort of passengers across all sections within the vehicle.
Reduced-order Koopman modeling and predictive control of nonlinear processes
Authors: Xuewen Zhang, Minghao Han, Xunyuan Yin
Date: 2024-03-31 00:00:00
Tasks: Chemical Process

In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse identification of nonlinear dynamics method is employed to select lifting functions for Koopman identification. The selected lifting functions are used to project the original nonlinear state-space into a higher-dimensional linear function space, in which Koopman-based linear models can be constructed for the underlying nonlinear process. To curb the significant increase in the dimensionality of the resulting full-order Koopman models caused by the use of lifting functions, we propose a reduced-order Koopman modeling approach based on proper orthogonal decomposition. A computationally efficient linear robust predictive control scheme is established based on the reduced-order Koopman model. A case study on a benchmark chemical process is conducted to illustrate the effectiveness of the proposed method. Comprehensive comparisons are conducted to demonstrate the advantage of the proposed method.
Creating synthetic energy meter data using conditional diffusion and building metadata
Authors: Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller
Date: 2024-03-31 00:00:00

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Frechet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future.
Analysis of Fairness-promoting Optimization Schemes of Photovoltaic Curtailments for Voltage Regulation in Power Distribution Networks
Authors: Rahul K. Gupta, Daniel K. Molzahn
Date: 2024-03-30 00:00:00
Tasks: Fairness

Active power curtailment of photovoltaic (PV) generation is commonly exercised to mitigate over-voltage issues in power distribution networks. However, fairness concerns arise as certain PV plants may experience more significant curtailments than others depending on their locations within the network. Existing literature tackles this issue through fairness-promoting/aware optimization schemes. These schemes can be broadly categorized into two types. The first type maximizes an additional fairness objective along with the main objective of curtailment minimization. The second type is formulated as a feedback controller, where fairness is accounted for by assigning different weights (as feedback) in the curtailment minimization objective for each PV plant based on previous curtailment actions. In this work, we combine these two schemes and provide extensive analyses and comparisons of these two fairness schemes. We compare the performance in terms of fairness and net curtailments for several benchmark test networks.
Inverse Optimal Cardano-Lyapunov Feedback for PDEs with Convection
Authors: Mohamed Camil Belhadjoudja, Miroslav Krstic, Mohamed Maghenem, Emmanuel Witrant
Date: 2024-03-30 00:00:00

We consider the problem of inverse optimal control design for systems that are not affine in the control. In particular, we consider some classes of partial differential equations (PDEs) with quadratic convection and counter-convection, for which the L2 norm is a control Lyapunov function (CLF) whose derivative has either a depressed cubic or a quadratic dependence in the boundary control input. We also consider diffusive PDEs with or without linear convection, for which a weighted L2 norm is a CLF whose derivative has a quadratic dependence in the control input. For each structure on the derivative of the CLF, we achieve inverse optimality with respect to a meaningful cost functional. For the case where the derivative of the CLF has a depressed cubic dependence in the control, we construct a cost functional for which the unique minimizer is the unique real root of a cubic polynomial: the Cardano-Lyapunov controller. When the derivative of the CLF is quadratic in the control, we construct a cost functional that is minimized by two distinct feedback laws, that correspond to the two distinct real roots of a quadratic equation. We show how to switch from one root to the other to reduce the control effort.
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
Authors: Runze Lin, Junghui Chen, Lei Xie, Hongye Su
Date: 2024-03-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Transfer Learning

In the context of Industry 4.0 and smart manufacturing, the field of process industry optimization and control is also undergoing a digital transformation. With the rise of Deep Reinforcement Learning (DRL), its application in process control has attracted widespread attention. However, the extremely low sample efficiency and the safety concerns caused by exploration in DRL hinder its practical implementation in industrial settings. Transfer learning offers an effective solution for DRL, enhancing its generalization and adaptability in multi-mode control scenarios. This paper provides insights into the use of DRL for process control from the perspective of transfer learning. We analyze the challenges of applying DRL in the process industry and the necessity of introducing transfer learning. Furthermore, recommendations and prospects are provided for future research directions on how transfer learning can be integrated with DRL to enhance process control. This paper aims to offer a set of promising, user-friendly, easy-to-implement, and scalable approaches to artificial intelligence-facilitated industrial control for scholars and engineers in the process industry.
Managing power balance and reserve feasibility in the AC unit commitment problem
Authors: Robert Parker, Carleton Coffrin
Date: 2024-03-30 00:00:00

Incorporating the AC power flow equations into unit commitment models has the potential to avoid costly corrective actions required by less accurate power flow approximations. However, research on unit commitment with AC power flow constraints has been limited to a few relatively small test networks. This work investigates large-scale AC unit commitment problems for the day-ahead market and develops decomposition algorithms capable of obtaining high-quality solutions at industry-relevant scales. The results illustrate that a simple algorithm that only seeks to satisfy unit commitment, reserve, and AC power balance constraints can obtain surprisingly high-quality solutions to this AC unit commitment problem. However, a naive strategy that prioritizes reserve feasibility leads to AC infeasibility, motivating the need to design heuristics that can effectively balance reserve and AC feasibility. Finally, this work explores a parallel decomposition strategy that allows the proposed algorithm to obtain feasible solutions on large cases within the two hour time limit required by typical day-ahead market operations.
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
Authors: Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Zou Hu, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
Date: 2024-04-02 00:00:00
Tasks: Survey

Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine learning algorithms and applied to cut-out-centered galaxies. However, according to the design and survey strategy of optical surveys by CSST, preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images. Moreover, given that multi-color images of strong lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong lensing systems in images with any number of channels. As evaluated using CSST mock data based on an Semi-Analytic Model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. 61 new strong lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.
A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification
Authors: Anumanchi Agastya Sai Ram Likhit, Divyansh Tripathi, Akshay Agarwal
Date: 2024-04-01 00:00:00
Tasks: Classification, Survey

This paper introduces a novel sector-based methodology for star-galaxy classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-DR18). By strategically segmenting the sky into sectors aligned with SDSS observational patterns and employing a dedicated convolutional neural network (CNN), we achieve state-of-the-art performance for star galaxy classification. Our preliminary results demonstrate a promising pathway for efficient and precise astronomical analysis, especially in real-time observational settings.
A spiking-domain implementation of electronic structure theory
Authors: Aakash Yadav, Daniel Hedman, Hongsik Jeong
Date: 2024-03-29 00:00:00

Electronic Structure Theory (EST) describes the behavior of electrons in matter and is used to predict material properties. Conventionally, this involves forming a Hamiltonian and solving the Schr\"odinger equation through discrete computation. Here, a new perspective to EST is provided by treating a perfectly crystalline material as a Linear Translation Invariant (LTI) system. The validity of this LTI-EST formalism is demonstrated by determining band structures for a one-dimensional chain of atoms, including the phenomenon of band structure folding in super cells. The proposed formalism allows for analytical traceability of band structure folding and offers computational advantage by bypassing the O(N) eigenvalue calculations. The spike-based computing nature of the proposed LTI-EST formalism is highlighted; thereby implying potential for material simulations solely in the spiking domain.
BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
Authors: Rafael Orozco, Abhinav Gahlot, Felix J. Herrmann
Date: 2024-03-28 00:00:00
Tasks: Experimental Design

CO$_2$ sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO$_2$ plumes to prevent risks such as leakage, induced seismicity, or breaching licensed boundaries. To address this, project managers use borehole wells for direct CO$_2$ and pressure monitoring at specific locations. Given the high costs associated with drilling, it is crucial to strategically place a limited number of wells to ensure maximally effective monitoring within budgetary constraints. Our approach for selecting well locations integrates fluid-flow solvers for forecasting plume trajectories with generative neural networks for plume inference uncertainty. Our methodology is extensible to three-dimensional domains and is developed within a Bayesian framework for optimal experimental design, ensuring scalability and mathematical optimality. We use a realistic case study to verify these claims by demonstrating our method's application in a large scale domain and optimal performance as compared to baseline well placement.