pwc-archive/papers-with-abstracts huggingface dataset:
Early Detection and Classification of Hidden Contingencies in Modern Power Systems: A Learning-based Stochastic Hybrid System Approach
Authors: Erfan Mehdipour Abadi, Hamid Varmazyari, Masoud H. Nazari
Date: 2024-07-30 00:00:00
Tasks: State Estimation

This paper introduces a novel learning-based Stochastic Hybrid System (LSHS) approach for detecting and classifying various contingencies in modern power systems. Specifically, the proposed method is capable of identifying hidden contingencies that cannot be captured by existing sensing and monitoring systems, such as failures in protection systems or line outages in distribution networks. The LSHS approach detects contingencies by analyzing system outputs and behaviors. It then categorizes them based on their impact on the SHS model into physical, control network, and measurement contingencies. The stochastic hybrid system (SHS) model is further extended into an advanced closed-loop framework incorporating both system dynamics and observer-based state estimation error dynamics. Machine learning methods within the LSHS framework are employed for contingency classification and rapid detection. The practicality and effectiveness of the proposed methodology are validated through simulations on an enhanced IEEE-33 bus system. The results demonstrate that the LSHS framework significantly improves the accuracy and speed of contingency detection compared to state-of-the-art methods, offering a promising solution for enhancing power system contingency detection.
Multi-Task Learning for Few-Shot Online Adaptation under Signal Temporal Logic Specifications
Authors: Andres Arias, Chuangchuang Sun
Date: 2024-07-30 00:00:00
Tasks: Multi-Task Learning

Multi-task learning (MTL) seeks to improve the generalized performance of learning specific tasks, exploiting useful information incorporated in related tasks. As a promising area, this paper studies an MTL-based control approach considering Signal Temporal Logic (STL). Task compliance is measured via the Robustness Degree (RD) which is computed by using the STL semantics. A suitable methodology is provided to solve the learning and testing stages, with an appropriate treatment of the non-convex terms in the quadratic objective function and using Sequential Convex Programming based on trust region update. In the learning stage, an ensemble of tasks is generated from deterministic goals to obtain a strong initializer for the testing stage, where related tasks are solved with a larger impact of perturbation. The methodology demonstrates to be robust in two dynamical systems showing results that meet the task specifications in a few shots for the testing stage, even for highly perturbed tasks.
Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models
Authors: Kaveri Mahapatra, Bhaskar Mitra, Soumya Kundu
Date: 2024-07-30 00:00:00

Recent Odessa disturbance events have brought attention to the challenges associated with the interaction between Inverter-Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted several issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection performance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long-term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs).
Robust LQ Optimal Control for Wind Turbine Power Tracking Operation
Authors: Aaron Grapentin, Christian A. Hans, Jörg Raisch
Date: 2024-07-30 00:00:00

In this paper, a robust linear quadratic optimal control approach for accurate active power tracking of wind turbines is presented. For control synthesis, linear matrix inequalities are employed using an augmented wind turbine state model with uncertain parameters. The resulting controller ensures robust stability in different operating regions. In a case study, the novel approach is compared to existing controllers from literature. Simulations indicate that the controller improves power tracking accuracy while leading to similar mechanical wear as existing approaches.
Impact of Geographical Separation on Spectrum Sharing Markets
Authors: Kangle Mu, Zongyun Xie, Igor Kadota, Randall Berry
Date: 2024-07-30 00:00:00
Tasks: Management

With the increasing demand for wireless services, spectrum management agencies and service providers (SPs) are seeking more flexible mechanisms for spectrum sharing to accommodate this growth. Such mechanisms impact the market dynamics of competitive SPs. Prior market models of spectrum sharing largely focus on scenarios where competing SPs had identical coverage areas. We depart from this and consider a scenario in which two competing SPs have overlapping but distinct coverage areas. We study the resulting competition using a Cournot model. Our findings reveal that with limited shared bandwidth, SPs might avoid overlapping areas to prevent potential losses due to interference. Sometimes SPs can strategically cooperate by agreeing not to provide service in the overlapping areas and, surprisingly, customers might also benefit from such cooperation under certain circumstances. Overall, market outcomes exhibit complex behaviors that are influenced by the sizes of coverage areas and the bandwidth of the shared spectrum.
Input Convex Neural Network-Assisted Optimal Power Flow in Distribution Networks: Modeling, Algorithm Design, and Applications
Authors: Rui Cheng, Yuze Yang, Wenxia Liu, Nian Liu, Zhaoyu Wang
Date: 2024-07-30 00:00:00

This paper proposes an input convex neural network (ICNN)-Assisted optimal power flow (OPF) in distribution networks. Instead of relying purely on optimization or machine learning, the ICNN-Assisted OPF is a combination of optimization and machine learning. It utilizes ICNN to learn the nonlinear but convex mapping from control variables to system state variables, followed by embedding into constrained optimization problems as convex constraints. Utilizing a designed ICNN structure, a fast primal-dual gradient method is developed to solve the ICNN-Assisted OPF, with the chain rule of deep learning applied to accelerate the algorithmic implementation. Convergence and optimally properties of the algorithm design are further established. Finally, different distribution network applications are discussed and proposed by means of the ICNN-Assisted OPF.
Bounded-Time Nonblocking Supervisory Control of Timed Discrete-Event Systems
Authors: Renyuan Zhang, Jiale Wu, Junhua Gou, Yabo Zhu, Kai Cai
Date: 2024-01-28 00:00:00

Recently an automaton property of quantitative nonblockingness was proposed in supervisory control of untimed discrete-event systems (DES), which quantifies the standard nonblocking property by capturing the practical requirement that all tasks be completed within a bounded number of steps. However, in practice tasks may be further required to be completed in specific time. To meet this new requirement, in this paper we introduce the concept of bounded-time nonblockingness, which extends the concept of quantitative nonblockingness from untimed DES to timed DES. This property requires that each task must be completed within a bounded time counted by the number of ticks, rather than bounded number of transition steps in quantitative nonblockingness. Accordingly, we formulate a new bounded-time nonblocking supervisory control problem (BTNSCP) of timed DES, and characterize its solvability in terms of a new concept of bounded-time language completability. Then we present an approach to compute the maximally permissive solution to the new BTNSCP.
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems
Authors: Jean-Baptiste Bouvier, Kartik Nagpal, Negar Mehr
Date: 2024-07-29 00:00:00
Tasks: Reinforcement Learning (RL)

In this paper, we develop a method for learning a control policy guaranteed to satisfy an affine state constraint of high relative degree in closed loop with a black-box system. Previous reinforcement learning (RL) approaches to satisfy safety constraints either require access to the system model, or assume control affine dynamics, or only discourage violations with reward shaping. Only recently have these issues been addressed with POLICEd RL, which guarantees constraint satisfaction for black-box systems. However, this previous work can only enforce constraints of relative degree 1. To address this gap, we build a novel RL algorithm explicitly designed to enforce an affine state constraint of high relative degree in closed loop with a black-box control system. Our key insight is to make the learned policy be affine around the unsafe set and to use this affine region to dissipate the inertia of the high relative degree constraint. We prove that such policies guarantee constraint satisfaction for deterministic systems while being agnostic to the choice of the RL training algorithm. Our results demonstrate the capacity of our approach to enforce hard constraints in the Gym inverted pendulum and on a space shuttle landing simulation.
Meta-Learning for Adaptive Control with Automated Mirror Descent
Authors: Sunbochen Tang, Haoyuan Sun, Navid Azizan
Date: 2024-07-29 00:00:00
Tasks: Meta-Learning

Adaptive control achieves concurrent parameter learning and stable control under uncertainties that are linearly parameterized with known nonlinear features. Nonetheless, it is often difficult to obtain such nonlinear features. To address this difficulty, recent progress has been made in integrating meta-learning with adaptive control to learn such nonlinear features from data. However, these meta-learning-based control methods rely on classical adaptation laws using gradient descent, which is confined to the Euclidean geometry. In this paper, we propose a novel method that combines meta-learning and adaptation laws based on mirror descent, a popular generalization of gradient descent, which takes advantage of the potentially non-Euclidean geometry of the parameter space. In our approach, meta-learning not only learns the nonlinear features but also searches for a suitable mirror-descent potential function that optimizes control performance. Through numerical simulations, we demonstrate the effectiveness of the proposed method in learning efficient representations and real-time tracking control performance under uncertain dynamics.
Emission Reduction in Urban Environments by Replacing Conventional City Buses with Electric Bus Technology: A Case Study of Pakistan
Authors: Muhammad Haris Saleem, S. Wajahat Ali, Sheikh Abdullah Shehzad
Date: 2024-07-29 00:00:00

The global transportation industry has become one of the main contributors to air pollution. Consequently, electric buses and green transportation are gaining popularity as crucial steps to reduce emission concerns. Many developed countries have already adopted the concept of Battery Electric Buses (BEBs), while the developing ones are just starting with it. However, BEB fleets have advantages, such as lower fuel, higher efficiency, lower maintenance, and energy security. Yet, several obstacles must be overcome to support the mass deployment of BEBs. These incorporate forthright expense charges, arranging loads, BEB reach, and newness to BEB innovation. Stakeholders like policymakers, private company owners, and government leaders have a lot to consider before introducing BEBs at any level in Pakistan. As a result, to operate an electric bus system profitably, it is crucial to develop a proper electric bus network and fleet, especially for bus operators who need to buy enough electric buses at the appropriate time. As a result, this paper aims to investigate if operating an electric bus could be an alternative to regular bus operations. The proposed methodology develops modeling software to cater to various scenarios to determine a proper-designed electric bus operating system in terms of the electric bus route, service frequency, and quantity. This research work simulates and financially analyses an operating Public Transport Infrastructure with a proposed Green Solution. The results show that regardless of the high upfront costs of BEB infrastructure, it becomes profitable in 6-7 years, resulting in a decreased Total Cost of Ownership (TCO) of approximately 30% of its counterpart. The study also provides a clear policy pathway to help stakeholders make informed decisions related to the electrification of public transport in Pakistan.
As Transparent as Possible, System Implementation Governance Using Process Mining
Authors: Negin Maddah
Date: 2024-07-29 00:00:00
Tasks: Management

This paper advocates for guiding an effective system implementation approach at a business process level. It details a case study of a food product manufacturer that transitioned to a new local information system. 41 units' data (10160 cases) over the pre-maturity phase of the system were then structured into event logs and analyzed. This analysis identified deviant process paths, questioning whether the new system efficiently supports procurement operations immediately post-implementation. The findings reveal critical implementation risks with conformance-checking of the as-is process with the to-be process model; this includes incomplete cases, unauthorized activities, irregular payment practices, stemming from organizational bottlenecks, or violating internal control regulations. Challenges are attributed to technical shortcomings in system design and cultural misalignments, necessitating immediate interventions or longer-term cultural and training solutions. This study's contribution is its demonstration of a transparent, process-driven approach to system governance, highlighting the strategic benefits of this integration for organizational management.
Minimum Time Consensus of Multi-agent System under Fuel Constraints
Authors: Akansha Rautela, Deepak Patil, Ameer Mulla, Indra Narayan Kar
Date: 2024-07-29 00:00:00
Tasks: All, Triplet

This work addresses the problem of finding a consensus point in the state space ($\mathbb{R}^2$) for a multi-agent system that is comprised of $N$ identical double integrator agents. It is assumed that each agent operates under constrained control input (i.e., $|u_i(t)| \leq 1$ $\forall i = 1, \hdots N$). Further, a fixed fuel budget is also assumed i.e., the total amount of cumulative input that can be expended is limited by $\int_0^{t_f}|u(t)|dt \le \beta$. First, the attainable set $\mathcal{A}(t,x_0,\beta)$ at time $t$, which is the set of all states that an agent can attain starting from initial conditions $x_0$ under the fuel budget constraints at time $t$ is computed for every agent. This attainable set is a convex set for all $t\ge0$. Then the minimum time to consensus is the minimum time $\bar{t}$ at which attainable sets of all agents intersect, and the consensus point is the point of intersection. A closed-form expression for the minimum time consensus point is provided for the case of three agents. Then, using Helly's theorem, the intersection will be non-empty at a time when all the $N \choose 3$ triplets of agents have non-empty intersection. The computation of minimum time consensus for all $N \choose 3$ triplets is performed independently and can be distributed among all the $N$ agents. Finally, the overall minimum time to consensus is given by the triplet that has the highest minimum time to consensus. Further, the intersection of all the attainable sets of this triplet gives the minimum time consensus point for all $N$ agents.
Culsans: An Efficient Snoop-based Coherency Unit for the CVA6 Open Source RISC-V application processor
Authors: Riccardo Tedeschi, Luca Valente, Gianmarco Ottavi, Enrico Zelioli, Nils Wistoff, Massimiliano Giacometti, Abdul Basit Sajjad, Luca Benini, Davide Rossi
Date: 2024-07-29 00:00:00

Symmetric Multi-Processing (SMP) based on cache coherency is crucial for high-end embedded systems like automotive applications. RISC-V is gaining traction, and open-source hardware (OSH) platforms offer solutions to issues such as IP costs and vendor dependency. Existing multi-core cache-coherent RISC-V platforms are complex and not efficient for small embedded core clusters. We propose an open-source SystemVerilog implementation of a lightweight snoop-based cache-coherent cluster of Linux-capable CVA6 cores. Our design uses the MOESI protocol via the Arm's AMBA ACE protocol. Evaluated with Splash-3 benchmarks, our solution shows up to 32.87% faster performance in a dual-core setup and an average improvement of 15.8% over OpenPiton. Synthesized using GF 22nm FDSOI technology, the Cache Coherency Unit occupies only 1.6% of the system area.
ProRuka: A highly efficient HMI algorithm for controlling a novel prosthetic hand with 6-DOF using sonomyography
Authors: Vaheh Nazari, Yong-Ping Zheng
Date: 2024-07-29 00:00:00
Tasks: Electromyography (EMG), regression, Transfer Learning

Sonomyography (SMG) is a novel human-machine interface that controls upper-limb prostheses by monitoring forearm muscle activity using ultrasonic imaging. SMG has been investigated for controlling upper-limb prostheses during the last two decades. The results show that this method, in combination with artificial intelligence, can classify different hand gestures with an accuracy of more than 90%, making it a great alternative control system compared to electromyography (EMG). However, up to now there are few reports of a system integrating SMG together with a prosthesis for testing on amputee subjects to demonstrate its capability in relation to daily activities. In this study, we developed ProRuka, a novel low-cost 6-degree-of-freedom prosthetic hand integrated with the control provided by a SMG system with a wearable ultrasound imaging probe. The classification of hand gestures using different machine learning classification/regression algorithms including KNN, nearest neighbor regression, random forest, decision tree classifier, decision tree regression, support vector regression and support vector machine in combination with a transfer learning model (VGG16) was first evaluated off-line to determine its reliability and precision. Additionally, the developed controlling system were evaluated on two amputees, in real-time experiments using a variety of hand function test kits. The results from an off-line study including ten healthy participants indicated that nine different hand motions can be classified with a success rate of 100%. In addition, the hand function test in real time (using 4 different hand gestures) confirmed that the designed prosthesis with the SMG controlling system can assist amputees to perform a variety of hand movements needed in daily activities.
Perspective on the Marine Simulator for Autonomous Vessel Development
Authors: Ryouhei Sawada, Yoshiki Miyauchi, Suisei Wada, Takuya Tanigushi, Satoru Hamada, Hiroaki Koike, Kouki Wakita, Atsuo Maki
Date: 2024-07-29 00:00:00
Tasks: Autonomous Navigation, Position

There is a growing demand for simulators for the research and development of maritime autonomous surface ships (MASS) and the approval of autonomous navigation algorithms. Simulators are used for purposes such as evaluation and training and are taken on various configurations accordingly. The ship maneuvering mathematical model used in such a simulator is an important element that characterizes the simulator. In this paper, we discuss the dynamic model of the hull and its position in the simulator that will be required for MASSs in the future. It also discusses guidelines for selecting an appropriate model, which has not been discussed extensively in previous studies. Finally, we discuss the functional requirements that simulators should have.
Passivity based Stability Assessment for Four types of Droops for DC Microgrids
Authors: Muhammad Anees, Lisa Qi, Mario Schweizer, Srdjan Lukic
Date: 2024-07-28 00:00:00

DC microgrids are getting more and more applications due to simple converters, only voltage control and higher efficiencies compared to conventional AC grids. Droop control is a well know decentralized control strategy for power sharing among converter interfaced sources and loads in a DC microgrid. This work compares the stability assessment and control of four types of droops for boost converters using the concept of passivity. EN standard 50388-2 for railway systems provides a reference to ensure system stability in perspectives of converters and system integration. Low pass filter (LPF) in the feedback of the droop control is used to ensure converter passivity. Bus impedance is derived to ensure system passivity with less conservativeness. Analytical approach for design of passive controller for all four types of droops is verified through time domain simulations of a single boost converter based microgrid feeding a Constant Power Load (CPL).
A Baseline Approach for Modeling and Characterization of Commercial Off-The-Shelf (COTS) Droop Controlled Converter
Authors: Muhammad Anees, Lisa Qi, Mehnaz Khan, Srdjan Lukic
Date: 2024-07-28 00:00:00

Due to advancements in power electronics, new converter topologies are introduced day by day. It's hard to get an equivalent model from any manufacturer of any Commercial Off-The-Shelf (COTS) power electronics converters because of intellectual property (IP) and safety concerns. Most COTS products don't reveal the exact topology of the converter as well as the control architecture and corresponding control gains. Because of these reasons, it's very hard to implement an exact equivalent model of COTS converters. Hierarchical control is widely used in microgrid applications, where different control layers are decoupled on the timescale. This article gives a baseline approach to build an equivalent model of any droop controlled COTS converter to mimic its control performance. Bandwidths of the different control loops are estimated along with the control logic from experiments on COTS converter and then hierarchical control is followed to come up with the equivalent model. The approach is verified on COTS CE+T Stabiliti 30C3 converter with its equivalent model build in MATLAB Simulink.
Symmetric Locality: Definition and Initial Results
Authors: Giordan Escalona, Dylan McKellips, Chen Ding
Date: 2024-07-27 00:00:00

In this short paper, we characterize symmetric locality. In designing algorithms, compilers, and systems, data movement is a common bottleneck in high-performance computation, in which we improve cache and memory performance. We study a special type of data reuse in the form of repeated traversals, or re-traversals, which are based on the symmetric group. The cyclic and sawtooth traces are previously known results in symmetric locality, and in this work, we would like to generalize this result for any re-traversal. Then, we also provide an abstract framework for applications in compiler design and machine learning models to improve the memory performance of certain programs.