pwc-archive/papers-with-abstracts huggingface dataset:
Reducing real-time complexity via sub-control Lyapunov functions: from theory to experiments
Authors: Huu-Thinh Do, Franco Blanchini, Stefano Miani, Ionela Prodan
Date: 2025-01-15 00:00:00

The techniques to design control Lyapunov functions (CLF), along with a proper stabilizing feedback, possibly in the presence of constraints, often provide control laws that are too complex for proper implementation online, especially when an optimization problem is involved. In this work, we show how to acquire an alternative, computationally attractive feedback. Given a nominal CLF and a nominal state feedback, we say that a different positive definite function is a Sub-control Lyapunov function (SCLF) if its Lyapunov derivative is negative-definite and bounded above by the Lyapunov derivative of the nominal function with the nominal control. It turns out that if we consider a family of basis functions, then a SCLF can be computed by linear programming, with an infinite number of constraints. The idea is that although the offline computational burden to achieve the new controller and solve the linear program is considerable, the online computational burden is drastically reduced. Comprehensive simulations and experiments on drone control are conducted to demonstrate the effectiveness of the study.
Efficient Planning in Large-scale Systems Using Hierarchical Finite State Machines
Authors: Elis Stefansson, Karl H. Johansson
Date: 2025-01-15 00:00:00

We consider optimal planning in a large-scale system formalised as a hierarchical finite state machine (HFSM). A planning algorithm is proposed computing an optimal plan between any two states in the HFSM, consisting of two steps: A pre-processing step that computes optimal exit costs of the machines in the HFSM, with time complexity scaling with the number of machines; and a query step that efficiently computes an optimal plan by removing irrelevant subtrees of the HFSM using the optimal exit costs. The algorithm is reconfigurable in the sense that changes in the HFSM are handled with ease, where the pre-processing step recomputes only the optimal exit costs affected by the change. The algorithm can also exploit compact representations that groups together identical machines in the HFSM, where the algorithm only needs to compute the optimal exit costs for one of the identical machines within each group, thereby avoid unnecessary recomputations. We validate the algorithm on large systems with millions of states and a robotic application. It is shown that our approach outperforms Dijkstra's algorithm, Bidirectional Dijkstra and Contraction Hierarchies.
Integrating Cybersecurity in Predictive Cost-Benefit Power Scheduling: A DeepStack Model with Dynamic Defense Mechanism
Authors: Ali Peivand, Seyyed Mostafa Nosratabadi
Date: 2025-01-15 00:00:00
Tasks: Scheduling

This paper introduces a novel, deep learning-based predictive model tailored to address wind curtailment in contemporary power systems, while enhancing cybersecurity measures through the implementation of a Dynamic Defense Mechanism (DDM). The augmented BiLSTM architecture facilitates accurate short-term predictions for wind power. In addition, a ConvGAN-driven step for stochastic scenario generation and a hierarchical, multi-stage optimization framework, which includes cases with and without Battery Energy Storage (BES), significantly minimizes operational costs. The inclusion of DDM strategically alters network reactances, thereby obfuscating the system's operational parameters to deter cyber threats. This robust solution not only integrates wind power more efficiently into power grids, leveraging BES potential to improve the economic efficiency of the system, but also boosting the cyber security of the system. Validation using the Illinois 200-bus system demonstrates the model's potential, achieving a 98% accuracy in forecasting and substantial cost reductions of over 3.8%. The results underscore the dual benefits of enhancing system reliability and security through advanced deep learning architectures and the strategic application of cybersecurity measures.
Achieving Stability and Optimality: Control Strategy for a Wind Turbine Supplying an Electrolyzer in the Islanded Storage-less Microgrid
Authors: Bosen Yang, Kang Ma, Jin Lin, Mingjun Zhang, QiweiDuan, Zhendong Ji, Zhi Liu, Yonghua Song
Date: 2025-01-15 00:00:00

Wind power generation supplying electrolyzers in islanded microgrids is an essential technical pathway for green hydrogen production, attracting growing attention in the transition towards net zero carbon emissions. Both academia and industry widely recognize that islanded AC microgrids normally rely on battery energy storage systems (BESSs) for grid-forming functions. However, the high cost of BESS significantly increases the levelized cost of hydrogen (LCOH), compromising economic feasibility. To address this challenge and reduce the LCOH, this paper focuses on a wind turbine (WT) supplying an electrolyzer in a storage-less microgrid and identifies a unique characteristic that challenges the conventional understanding of this microgrid: active power is coupled with microgrid voltage rather than frequency, the latter being entirely decoupled from active power balance. Based on this unique characteristic, this paper develops a new control strategy that maintains power balance, stabilizes the voltage and frequency, and maximizes hydrogen production. The effectiveness of the control strategy is validated through case studies conducted in Matlab/Simulink, especially its capability to maintain stability while maximizing hydrogen production under various conditions.
A Bayesian Hierarchical Model for Generating Synthetic Unbalanced Power Distribution Grids
Authors: Henrique O. Caetano, Rahul K. Gupta, Marco Aiello, Carlos Dias Maciel
Date: 2025-01-15 00:00:00
Tasks: Transfer Learning

The real-world data of power networks is often inaccessible due to privacy and security concerns, highlighting the need for tools to generate realistic synthetic network data. Existing methods leverage geographic tools like OpenStreetMap with heuristic rules to model system topology and typically focus on single-phase, balanced systems, limiting their applicability to real-world distribution systems, which are usually unbalanced. This work proposes a Bayesian Hierarchical Model (BHM) to generate unbalanced three-phase distribution systems learning from existing networks. The scheme takes as input the base topology and aggregated demand per node and outputs a three-phase unbalanced system. The proposed scheme achieves a Mean Absolute Percentage Error (MAPE) of less than $8\%$ across all phases, with computation times of 20.4 seconds for model training and 3.1 seconds per sample generation. The tool is applied to learn from publicly available SMART-DS dataset and applied to generate European 906 and IEEE-123 systems. We demonstrate the transfer learning capability of the proposed tool by leveraging a model trained on an observed system to generate a synthetic network for an unobserved system. Specifically, the tool is trained using the publicly available SMART-DS dataset and subsequently applied to generate synthetic networks for the European 906-bus system and the IEEE 123-bus system. This tool allows researchers to simulate realistic unbalanced three-phase power data with high accuracy and speed, enhancing planning and operational analysis for modern power grids.
Cultivating Precision: Comparative Analysis of Sensor-Based Yogurt Fermentation Monitoring Techniques
Authors: Ege Keskin, İhsan Ozan Yıldırım
Date: 2025-01-15 00:00:00

Fermented dairy products, including yogurt, are widely consumed for their nutritional and health benefits. While numerous methods exist to monitor and understand yogurt fermentation, the literature lacks an integrated evaluation of diverse sensing approaches within a single experimental framework. To address this gap, this study systematically examines and compares multiple measurement techniques--electrical impedance, DC resistance, pH, optical transparency, carbon dioxide concentration, ambient temperature, and relative humidity--in tracking the yogurt fermentation process. By presenting a unified set of experimental results and assessing each method's observational characteristics, this work offers an encompassing reference point for researchers seeking to understand the relative merits and limitations of different sensing modalities. Rather than establishing definitive guidelines or practical recommendations, the findings provide a foundation for subsequent investigations into sensor-based fermentation monitoring, thereby contributing to a more comprehensive understanding of yogurt fermentation dynamics.
Some remarks on practical stabilization via CLF-based control under measurement noise
Authors: Patrick Schmidt, Pavel Osinenko, Stefan Streif
Date: 2025-01-15 00:00:00

Practical stabilization of input-affine systems in the presence of measurement errors and input constraints is considered in this brief note. Assuming that a Lyapunov function and a stabilizing control exist for an input-affine system, the required measurement accuracy at each point of the state space is computed. This is done via the Lyapunov function-based decay condition, which describes along with the input constraints a set of admissible controls. Afterwards, the measurement time points are computed based on the system dynamics. It is shown that between these self-triggered measurement time points, the system evolves and converges into the so-called target ball, i.e. a vicinity of the origin, where it remains. Furthermore, it is shown that the approach ensures the existence of a control law, which is admissible for all possible states and it introduces a connection between measurement time points, measurement accuracy, target ball, and decay. The results of the approach are shown in three examples.
Scenarios Generation-based Multiple Interval Prediction Method for Electricity Prices
Authors: Lu Xin
Date: 2025-01-15 00:00:00
Tasks: Diversity, Generative Adversarial Network, Prediction, Time Series

This paper introduces an innovative interval prediction methodology aimed at addressing the limitations of current evaluation indicators while enhancing prediction accuracy and reliability. To achieve this, new evaluation metrics are proposed, offering a comprehensive assessment of interval prediction methods across both all-sample and single-sample scenarios. Additionally, a novel Pattern-Diversity Conditional Time-Series Generative Adversarial Network (PDCTSGAN) is developed, designed to generate realistic scenarios and support a new interval prediction framework based on scenario generation. The PDCTSGAN model incorporates unique modifications to random noise inputs, enabling the creation of pattern-diverse and realistic scenarios. These scenarios are then utilized to produce multiple interval patterns characterized by high coverage probability and reduced average width. The proposed approach is validated through detailed case studies, and the paper concludes with a discussion of future research directions to further refine interval prediction techniques.
A Novel Multiple Interval Prediction Method for Electricity Prices based on Scenarios Generation: Definition and Method
Authors: Lu Xin
Date: 2025-01-15 00:00:00
Tasks: Diversity, Generative Adversarial Network, Prediction, Time Series

This paper presents interval prediction methodology to address limitations in existing evaluation indicators and improve prediction accuracy and reliability. First, new evaluation indicators are proposed to comprehensively assess interval prediction methods, considering both all-sample and single-sample scenarios. Second, a novel Pattern-Diversity Conditional Time-Series Generative Adversarial Network (PDCTSGAN) is introduced to generate realistic scenarios, enabling a new interval prediction approach based on scenario generation. The PDCTSGAN model innovatively incorporates modifications to random noise inputs, allowing the generation of pattern-diverse realistic scenarios. These scenarios are further utilized to construct multiple interval patterns with high coverage probability and low average width. The effectiveness of the proposed methodology is demonstrated through comprehensive case studies. The paper concludes by highlighting future research directions to further enhance interval prediction methods.
A Survey on IBR Penetrated Power System Stability Analysis Using Frequency Scanning
Authors: Shuvangkar Chandra Das, Lokesh Saravana, Le Minh Vu, Manh Bui, Tuyen Vu, Jianhua Zhang, Thomas Ortmeyer
Date: 2025-01-15 00:00:00

The rapid rise in inverter-based renewable resources has heightened concerns over subsynchronous resonance and oscillations, thereby challenging grid stability. This paper reviews approaches to identify and mitigate these issues, focusing on frequency scanning methods for stability assessment. It categorizes white-, black-, and gray-box modeling techniques, compares positive-sequence, dq-frame, and alpha-beta domain scanning, and examines perturbation shapes like step, ramp, and chirp. A comparative study highlights their strengths, limitations, and suitability for specific scenarios. By summarizing past events and surveying available tools, this work guides operators and researchers toward more effective, reliable stability analysis methods in grids with high renewable penetration.
Nonlinear Modeling of a PEM Fuel Cell System; a Practical Study with Experimental Validation
Authors: Seyed Mehdi Rakhtala, Roja Eini
Date: 2025-01-14 00:00:00

In this paper, a nonlinear six order model is proposed for a proton exchange membrane fuel cell (PEMFC) as a control-oriented electrochemical model. Its validation is performed on a specific single cell PEMFC with effective dimension of 5 cm5 cm. This model is described in the nonlinear state space form with 6 state variables. Load current and DC voltage are considered as measurable disturbance and control input respectively. Besides, the model includes fuel cell stack and its auxiliary components as well. In this survey, a nonlinear state space representation is derived by arranging nonlinear equations and combining them with auxiliary components model. The proposed model can be successfully used to design nonlinear controller and nonlinear observer systems. The analyzed PEMFC system consists of air compressor motor dynamic equations, air and fuel supply subsystems, a perfect air humidifier and a fuel cell stack. An experimentally validated nonlinear model that reproduces the most typical features of a laboratory PEMFC system is presented. This model is derived based on physics law in stack, including system gases dynamics. The objective of this paper is to introduce a fully analytical model which has been fully validated on a fuel cell system and its auxiliary components. The proposed method can be used as a general modeling guideline for control-oriented purposes. Moreover, it can be successfully implemented in composing a dynamic subsystem, like hydrogen subsystem, as part of the whole nonlinear model.
A Novel Method for Detecting Dust Accumulation in Photovoltaic Systems: Evaluating Visible Sunlight Obstruction in Different Dust Levels and AI-based Bird Droppings Detection
Authors: Md Shahriar Kabir, Khalid Mahmud Niloy, S. M. Imrat Rahman, Md Imon Hossen, Sumaiya Afrose, Md. Ismail Hossain Mofazzol, Md Lion Ahmmed
Date: 2025-01-14 00:00:00

This paper presents an innovative method for automatically detecting dust accumulation on a PV system and notifying the user to clean it instantly. The accumulation of dust, bird, or insect droppings on the surface of photovoltaic (PV) panels creates a barrier between the solar energy and the panel's surface to receive sufficient energy to generate electricity. The study investigates the effects of dust on PV panel output and visible sunlight (VSL) block amounts to utilize the necessity of cleaning and detection. The amount of blocked visible sunlight while passing through glass due to dust determines the accumulated dust level. Visible sunlight can easily pass through the clean, transparent glass but reflects when something like dust obstructs it. Based on those concepts, a system is designed with a light sensor that is simple, effective, easy to install, hassle-free, and can spread the technology. The study also explores the effectiveness of the detection system developed by using image processing and machine learning algorithms to identify dust levels and bird or insect droppings accurately. The experimental setup in Gazipur, Bangladesh, found that excessive dust can block up to 55% of visible sunlight, wasting 55% of solar energy in the visible spectrum, and cleaning can recover 3% of power weekly. The data from the dust detection system is correlated with the 400W capacity solar panels' naturally lost efficiency data to validate the system. This research measured visible sunlight obstruction and loss due to dust. However, the addition of an infrared radiation sensor can draw the entire scenario of energy loss by doing more research.
Economic Model Predictive Control for Periodic Operation: A Quadratic Programming Approach
Authors: Jose A. Borja-Conde, Juan M. Nadales, Filiberto Fele, Daniel Limon
Date: 2025-01-14 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Periodic dynamical systems, distinguished by their repetitive behavior over time, are prevalent across various engineering disciplines. In numerous applications, particularly within industrial contexts, the implementation of model predictive control (MPC) schemes tailored to optimize specific economic criteria was shown to offer substantial advantages. However, the real-time implementation of these schemes is often infeasible due to limited computational resources. To tackle this problem, we propose a resource-efficient economic model predictive control scheme for periodic systems, leveraging existing single-layer MPC techniques. Our method relies on a single quadratic optimization problem, which ensures high computational efficiency for real-time control in dynamic settings. We prove feasibility, stability and convergence to optimum of the proposed approach, and validate the effectiveness through numerical experiments.
A Comparative Analysis of Transformer-less Inverter Topologies for Grid-Connected PV Systems: Minimizing Leakage Current and THD
Authors: Shashwot Shrestha, Rachana Subedi, Swodesh Sharma, Sushil Phuyal, Indraman Tamrakar
Date: 2025-01-14 00:00:00

The integration of distributed energy resources (DERs), particularly photovoltaic (PV) systems, into power grids has gained major attention due to their environmental and economic benefits. Although traditional transformer-based grid-connected PV inverters provide galvanic isolation for leakage current, they suffer from major drawbacks of high cost, lower efficiency, and increased size. Transformer-less grid-connected PV inverters (TLGI) have emerged as a prominent alternative, as they achieve higher efficiency, compact design, and lower cost. However, due to a lack of galvanic isolation, TLGIs are highly affected by leakage current caused by the fluctuation of common-mode voltage (CMV). This paper investigates three topologies H4, H5, and HERIC with comparisons between their CMV, differential-mode voltage (DMV), total harmonic distortion (THD), and leakage current. A simulation was conducted for each topology in MATLAB/Simulink R2023a, and the results demonstrate that the H5 topology achieves a balance between low leakage current, reduced THD, and optimal operational efficiency, making it suitable for practical application.
Range-Only Dynamic Output Feedback Controller for Safe and Secure Target Circumnavigation
Authors: Anand Singh, Anoop Jain
Date: 2025-01-14 00:00:00

The safety and security of robotic systems are paramount when navigating around a hostile target. This paper addresses the problem of circumnavigating an unknown target by a unicycle robot while ensuring it maintains a desired safe distance and remains within the sensing region around the target throughout its motion. The proposed control design methodology is based on the construction of a joint Lyapunov function that incorporates: (i) a quadratic potential function characterizing the desired target-circumnavigation objective, and (ii) a barrier Lyapunov function-based potential term to enforce safety and sensing constraints on the robot's motion. A notable feature of the proposed control design is its reliance exclusively on local range measurements between the robot and the target, realized using a dynamic output feedback controller that treats the range as the only observable output for feedback. Using the Lyapunov stability theory, we show that the desired equilibrium of the closed-loop system is asymptotically stable, and the prescribed safety and security constraints are met under the proposed controllers. We also obtain restrictive bounds on the post-design signals and provide both simulation and experimental results to validate the theoretical contributions.
Analysis of Power Losses and the Efficacy of Power Minimization Strategies in Multichannel Electrical Stimulation Systems
Authors: Francesc Varkevisser, Wouter A. Serdijn, Tiago L. Costa
Date: 2025-01-14 00:00:00

Neuroprosthetic devices require multichannel stimulator systems with an increasing number of channels. However, there are inherent power losses in typical multichannel stimulation circuits caused by a mismatch between the power supply voltage and the voltage required at each electrode to successfully stimulate tissue. This imposes a bottleneck towards high-channel-count devices, which is particularly severe in wirelessly-powered devices. Hence, advances in the power efficiency of stimulation systems are critical. To support these advances, this paper presents a methodology to identify and quantify power losses associated with different power supply scaling strategies in multichannel stimulation systems. The proposed methodology utilizes distributions of stimulation amplitudes and electrode impedances to calculate power losses in multichannel systems. Experimental data from previously published studies spanning various stimulation applications were analyzed to evaluate the performance of fixed, global, and stepped supply scaling methods, focusing on their impact on power dissipation and efficiency. Variability in output conditions results in low power efficiency in multichannel stimulation systems across all applications. Stepped voltage scaling demonstrated substantial efficiency improvements, achieving an increase of 67 % to 146 %, particularly in high-channel-count applications with significant variability in tissue impedance. Global scaling, by contrast, was more advantageous for systems with fewer channels. The findings highlight the importance of tailoring power management strategies to specific applications to optimize efficiency while minimizing system complexity. The proposed methodology offers a framework for evaluating efficiency-complexity trade-offs, advancing the design of scalable neurostimulation systems.
A resource management approach for concurrent operation of RF functionalities
Authors: Pascal Marquardt, Sebastian Durst, Kilian Barth, Tobias Müller
Date: 2025-01-14 00:00:00
Tasks: Management

Future multifunction RF systems will be able to not only perform various different radar, communication and electronic warfare functionalities but also to perform them simultaneously on the same aperture. This ability of concurrent operations requires new, cognitive approaches of resource management compared to classical methods. This paper presents such a new approach using a combination of quality of service based resource management and Monte Carlo tree search.