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Optimal inverter-based droop control in active distribution network with evolutionary strategy-embedded deep reinforcement learning

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ELECTRIC POWER systems RESEARCH 2025年 247卷

作者： Wang, He Li, Jinling Li, Shiqiang Liu, Xiao Bian, Jing Yu, Huanan Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Jilin Province Peoples R China Univ Sydney Sch Elect & Comp Engn Sydney NSW 2006 Australia

Inverter-based photovoltaics based on a Volt/Var curve can support Voltage/Var control (VVC) and have been widely adopted in active distribution networks. Due to its data-driven characteristics, deep reinforcement learning (DRL) has been widely applied to improve the benefits of the VVC. However, since the traditional default droop control lacks system-wide optimum, a comprehensive droop control function is proposed to improve voltage regulation performance. This work of optimal droop control function is challenging as the Volt/ Var curve introduces products of voltage variables, leading to an intractable bilinear optimization problem. Moreover, a decision space too large for conventional DRL leads to a local optimum. Thus, we propose an evolutionary strategy embedding the conventional DRL to enhance exploration and sample efficiency, deriving optimal droop control functions. The proposed method's efficiency and superiority in power loss reduction and voltage regulation enhancement are verified through case studies involving the modified ieee 33-bus distribution systems.

关键词： Comprehensive droop control function Deep reinforcement learning Evolutionary strategy Inverter-based PV Volt/Var control

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ResNet-18-Based Interturn Short-Circuit Fault Diagnosis of PMSMs With Consideration of Speed and Current Loop Bandwidths

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ieee TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第3期10卷 5805-5818页

作者： Wei, Dong Liu, Kan Wang, Jianbo Zhou, Shichao Li, Kaiqing Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

A residual network (ResNet)-18-based model for the online interturn short-circuit (ITSC) fault diagnosis of permanent magnet synchronous machines (PMSMs) widely employed in traction systems of electric vehicles and locomotives is proposed in this article, which is a data-driven solution and robust to bandwidth variation in both speed and current controllers. It is found that the extracted fault features at the early stage of the ITSC are usually quite small and will be significantly influenced by the bandwidths of both speed and current controllers, which will potentially lead to a misjudgment of fault degree. Hence, an ResNet-18 deep learning model for ITSC fault is designed in this article, which has considered the variation of bandwidths of speed and current controllers by improving the input of the network. It can accurately detect and classify the fault degrees concerning adjacent levels of insulation deterioration thanks to its high performance in feature representation. Multivariate signals including speed, $dq$ -axis currents, and reference voltages are employed as inputs with five channels to train the proposed diagnostic model, which can be easily measured from the PMSM drive system. The diagnosis results of ITSC fault on a prototype PMSM show a high average accuracy being up to 97.81%.

关键词： Circuit faults Bandwidth Fault diagnosis Transportation Insulation Harmonic analysis Feature extraction control bandwidth data-driven method fault degree classification interturn short circuit (ITSC) permanent magnet synchronous machine (PMSM)

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Investigating the Effectiveness of Reinforcement learning in Closed-Loop systems with Time Delays

Investigating the Effectiveness of Reinforcement Learning in...

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American control conference (ACC)

作者： Wafi, Moh Kamalul Siami, Milad Sznaier, Mario Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

ISBN: (纸本)9798350382662;9798350382655

data-driven controllers have gained prominence in diverse control applications, attributed to their inherent flexibility and adaptability to complex system dynamics. However, managing time delays in closed-loop systems remains a significant challenge in their deployment. These delays can arise from various sources, such as computational latency, actuator reaction time, and communication delays. Unaddressed, these time lags can induce system instability and degrade performance. This paper rigorously analyzes the impact of time delays on data-driven controllers and introduces methodologies to mitigate their adverse effects. Specifically, we explore the integration of the Smith predictor with Deep Reinforcement learning (SP-DRL) to formulate a control law capable of effectively managing both time delays and system uncertainties, while ensuring robust performance. We demonstrate that this DRL-based framework, initially trained in stable environments, generalizes well to unstable systems. Our investigation delineates the scenarios conducive to the successful application of this approach and identifies factors influencing its effectiveness. To substantiate our findings, we present a case study involving a first-order delayed linear system with nonlinear actuation modules. Numerical simulations are employed to compare the robustness of SP-DRL scheme against the DRL standalone and the classical controls, such as PID and Linear Quadratic Regulator (LQR), in the presence of delays.

关键词： Deep reinforcement learning

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Computational Cost Analysis and data-driven Predictive Modeling of Cloud-Based Online-NILM Algorithm

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ieee TRANSACTIONS ON CLOUD COMPUTING 2022年第4期10卷 2409-2423页

作者： Asres, Mulugeta Weldezgina Ardito, Luca Patti, Edoardo Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy

Online non-intrusive load monitoring algorithms have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as a safety control, anomaly detection, and demand-side management. However, the computational energy cost for executing such algorithms should not overcome the promised energy efficiency from the disaggregated appliance specific consumption information feed-backs. Moreover, the energy efficiency of cloud computing systems is also becoming a concern for the environment due to carbon emission. This study analyzes the energy spent to execute NILM algorithms via computation cost estimation and prediction using computing system-level power monitoring and data-driven approaches. A generic framework for an automated algorithm cost monitoring and modeling methodologies is devised for large load scale deployment of Cloud-based Online-NILM algorithms. The efficacy of the proposed approach was examined and validated on two computing system use-cases, i.e., Dedicated Server and Cloud Virtual Server. The prediction models, developed using statistical and machine learning tools, demonstrate the promising applicability of the data-driven approach with a very high prediction accuracy without detailed knowledge of the computing systems and the algorithm.

关键词： NILM load disaggregation data-driven computational cost algorithm machine learning cloud systems

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Feature-Based Test Scenario Selection in Automated Driving: Insights from SHAP Values 27

Feature-Based Test Scenario Selection in Automated Driving: ...

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27th ieee International conference on Intelligent Transportation systems, ITSC 2024

作者： Sohn, Tin Stribor Gorhan, Nora Dillitzer, Maximilian Ewecker, Lukas Bruhl, Tim Schwager, Robin Nagele, Ann Therese Sax, Eric Dr. Ing. H.c. F. Porsche Ag Weissach Germany Karlsruher Institut für Technologie Karlsruhe Germany

ISBN: (纸本)9798331505929

Scenario-based testing is seen as a promising approach for evaluating automated driving systems, by grouping potential real-world events into representative test scenarios within their intended operational design domain. However, the complexity of the open world creates countless scenario variations, making it essential to select relevant scenarios efficiently while avoiding those that are not relevant for the test object. Traditional expert-driven methods struggle with this complexity, as causes for certain events related to the target domain are often unknown leading to incomplete specifications of the test space. This is highlighting the need for data-driven approaches to analyse and identify key parameters contributing to unwanted system behavior. In this paper a novel method for selecting scenario parameters is introduced, using explainable machine learning. It consists of two parts: a model trained on vehicle measurement data describing the operational design domain in order to predict test outcomes and a feature explainer based on SHapely Additive exPlanations to highlight important features contributing to these outcomes. The effectiveness of the proposed method is demonstrated using real driving data from a predictive Adaptive Cruise control system. The results reveal the potential to improve testing efficiency and effectiveness in automated driving systems development through a test outcome prediction model and its feature explanations, reducing the reliance on domain-specific expertise for manual or rule-based test scenario selection, analysis and specification. © 2024 ieee.

关键词： Adaptive control systems

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Koopman-Based data-driven Techniques for Adaptive Cruise control System Identification 27

Koopman-Based Data-Driven Techniques for Adaptive Cruise Con...

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27th ieee International conference on Intelligent Transportation systems, ITSC 2024

作者： Meng, Yiming Li, Hangyu Ornik, Melkior Li, Xiaopeng University of Wisconsin-Madison Department of Civil and Environmental Engineering MadisonWI53706 United States University of Illinois Urbana-Champaign Coordinated Science Laboratory UrbanaIL61801 United States University of Illinois Urbana-Champaign Department of Aerospace Engineering UrbanaIL61801 United States

ISBN: (纸本)9798331505929

Accurately identifying the intrinsic model of Adaptive Cruise control has the potential to enhance the prediction of automated car-following behavior, helping vehicles' decision-making and contributing to safer and more efficient traffic flows. Moreover, white box models offer an analytical base for evaluating the impact of automated driving functions on macroscopic traffic dynamics, consequently aiding the management of the whole intelligent transportation system. Many existing system identification techniques have been applied to automated vehicles. However, most of these studies focus on identifying parameters for models of a fixed prototype. Their reliance on accurate estimation of state time derivatives prevents their real applications, challenged by low sampling rates, noisy measurements, and limited observation periods. In contrast, the Koopman operator learning framework presents a promising improvement that can identify the nonlinear evolutionary properties of continuous-time systems. In this study, we apply Koopman-based methods to data driven Adaptive Cruise control model identification. Additionally, as the challenge remains in establishing a practical relationship between identification accuracy and sampling rate, we numerically compared the performance of three Koopman-based learning frameworks, finite-difference, Koopman-logarithm, and a newly devised resolvent-type method, with that of a commonly used offline simulation-based batch optimization approach. We introduce a novel modification to the resolvent-type method, and the experimental results demonstrate its state of the art performance, particularly in identifying the potential existence of parametric noise at lower sampling rates. © 2024 ieee.

关键词： Continuous time systems

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AI driven PROCESS control BY MACHINE learning BASED VIRTUAL METROLOGY FOR HIGH PRODUCT MIX MANUFACTURING

AI DRIVEN PROCESS CONTROL BY MACHINE LEARNING BASED VIRTUAL ...

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conference of Science and Technology for Integrated Circuits (CSTIC)

作者： Lee, Hyung Joo Choi, Sanghyun Greeneltch, Nathan Jayaram, Srividya Zhang, Shiwei Wan, Qijian Du, Chunshan Siemens EDA Digital Ind Software Seongnam 13524 Gyeonggi South Korea Siemens EDA Digital Ind Software Wilsonville OR 97070 USA Siemens EDA Digital Ind Software Shanghai 200127 Peoples R China

ISBN: (纸本)9798350362206;9798350362190

The semiconductor foundry industry faces challenges in managing diverse customer demands and complex manufacturing processes. Variations in the chemical vapor deposition (CVD) process affect transistor parameters and yield. Siemens' Calibre (R) software with artificial intelligence / machine learning ( AI/ML) techniques create a virtual metrology (VM) model that outperforms traditional methods. An advanced process control (APC) system, incorporating design features and real-time data, improves process capability and reduces film thickness variations in high-mix product foundry fabs, as confirmed by control simulations.

关键词： Foundries

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Revolutionizing Fault Prediction in MetroPT datasets: Enhanced Diagnosis and Efficient Failure Prediction through Innovative data Refinement 3

Revolutionizing Fault Prediction in MetroPT Datasets: Enhanc...

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3rd International conference on Power, control and Computing Technologies, ICPC2T 2024

作者： Neamah, Osamah N. Bayir, Raif Graduate Institute Karabuk University Department of Mechatronics Engineering Karabuk Turkey Karabuk University Department of Mechatronics Engineering Karabuk Turkey

ISBN: (纸本)9798350349207

This scientific paper presents groundbreaking advancements in Predictive Maintenance (PdM) within Industry 4.0, employing cutting-edge machine learning classification algorithms for fault prediction and diagnosis in Air Production Unit (APU) systems like MetroPT and MetroPT-3. This research uses data-driven methodologies to optimize feature extraction techniques to enhance fault prediction and improve diagnostic accuracy. A robust and versatile model emerges through comprehensive testing, displaying exceptional potential in fault prediction and diagnosis for complex systems. The paper highlights the significance of enhanced analytical techniques, such as cross-validation, ensuring the reliability and robustness of the model, contributing to refined and accurate fault prediction and diagnosis, all without succumbing to overfitting. This work significantly advances the application of machine learning in predicting malignancy within Industry 4.0, showcasing the promise of these methodologies in fault prediction and diagnosis for intricate systems. © 2024 ieee.

关键词： Machine learning

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Distribution Network Digital Twin: a Basic Machine learning based Voltage Estimator 14

Distribution Network Digital Twin: a Basic Machine Learning ...

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14th ieee International conference on Communications, control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Iaali, Mohamad El Razi, Reza Bruyere, Antoine Francois, Bruno Soares, Joao Univ. Lille Arts et Metiers Institute of Technology Centrale Lille Junia Ulr 2697 - L2EP LilleF-59000 France Gecad - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Lasi - Intelligent Systems Associate LAboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781665455541

The increasing integration of renewable energy sources, and the widespread use of electric vehicles have introduced greater complexity and dynamics in power flows in distribution systems. With bidirectional power flows enhancing the observability of the grid becomes essential to identify critical feeders. In this context, data-driven methods offer a promising approach to enhance situational awareness for power system operators and improve their understanding of the system behavior under variabilities. This paper proposes a voltage estimation method based on a Machine learning algorithm to address this challenge. By leveraging data-driven techniques, the proposed method aims to provide accurate and efficient voltage estimations in distribution networks, contributing to the effective management and control of modern power systems. © 2023 ieee.

关键词： Distributed power generation

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Robust Driving Intention Prediction Based on Multi-Stage learning Under Vehicle-Infrastructure Cooperative Perception

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年

作者： Liu, Kexin Yuan, Xiaofang Li, Zhe Pan, Xiangcheng Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Vis & Control Technol Changsha 410082 Peoples R China

In mixed traffic of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs), it is essential to predict the driving intention of HDVs to avoid potential risks. data quality is crucial to intention prediction under a cooperative vehicle-infrastructure system, whereas the data collection of HDVs relies on the vehicle-infrastructure cooperative perception, which is inevitably exposed to perception errors. In this paper, a robust driving intention prediction framework based on multi-stage learning is proposed in mixed traffic under the vehicle-infrastructure cooperative perception situation. To address this issue, different information sources from vehicles and traffic are considered to derive the implied vehicle dynamic interaction relation and traffic flow context. A feature extraction module is developed to respectively capture the local and global features based on convolutional neural network (CNN) for reducing the impact of noise, which ensures the prominent detailed and overall descriptions of driving intention can be comprehensively acquired. Then, the deep multi-scale technique and multi-layer perceptron network are introduced to further extract deep features, and improve the model adaptability by complementary feature learning mechanism and nonlinear mapping ability. Experiment results on a real-world dataset confirm the effectiveness of our proposal in reducing the impact of poor data quality and accurately predicting driving intention.

关键词： Predictive models Feature extraction Convolutional neural networks TV Sensors Vehicle-to-everything Vehicle dynamics Trajectory Hidden Markov models data integrity Driving intention prediction mixed traffic cooperative perception error multi-stage learning convolutional neural network

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