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The Role of Machine learning-based Surrogate Models for Wind Power Curtailment Prediction from Electricity Market data

The Role of Machine Learning-based Surrogate Models for Wind...

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International conference on Smart Energy systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Iuliano, Silvia De Caro, Fabrizio Vaccaro, Alfredo Univ Sannio Dept Engn Benevento Italy

ISBN: (纸本)9798350386509;9798350386493

The proliferation of distributed low-inertia generators combined with the large number of power transactions ruled by complex electricity market dynamics and the increased penetration of non-linear loads make modern electrical grids more vulnerable to dynamic perturbations. Hence, to enhance the power system resilience to these perturbations, it is incumbent to develop enhanced tools for Dynamic Security Assessment (DSA), which try inferring from large aggregated datasets actionable information helpful in defining corrective actions to mitigate the dynamic impacts of severe perturbation phenomena. In this context, the curtailment of renewable power generators is one of the most critical actions that DSA should identify for enhancing the power system stability after severe grid contingencies. To face this challenging issue, this paper explores the potential role of machine learning-based surrogate modeling in discovering the hidden relationships between aggregated grid data describing the electricity market clearing and the corresponding wind power curtailment identified by a real DSA tool. Experimental results obtained by processing the Italian electricity market data are presented and discussed to assess the performance of the analyzed techniques, outlining the most revealing future research paths of this research.

关键词： Power System Stability data-driven Machine learning Surrogate Modeling Wind Power Curtailment

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Quality control in Extrusion-Based Additive Manufacturing: A Review of Machine learning Approaches

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ieee TRANSACTIONS ON CYBERNETICS 2025年第6期55卷 2522-2534页

作者： Pereira, Adailton Gomes Barbosa, Gustavo Franco Godinho Filho, Moacir Shiki, Sidney Bruce da Silva, Andrea Lago Univ Fed Sao Carlos Dept Prod Engn BR-13565905 Sorocaba Brazil Univ Fed Sao Carlos Dept Mech Engn BR-13565905 Sao Carlos Brazil EM Normandie Business Sch Metis Lab F-76600 Le Havre France

Additive manufacturing (AM) revolutionizes product creation with its unique layer-by-layer construction method but faces obstacles in widespread industrial use due to quality assurance and defect challenges. Integrating machine learning (ML) into AM quality control (QC) systems presents a viable solution, utilizing ML's ability to autonomously detect patterns and extract important data, reducing the reliance on manual intervention. This study conducts an in-depth literature review to scrutinize the role of ML in augmenting QC mechanisms within extrusion-based AM processes. Our primary objective is to pinpoint ML models that excel in monitoring manufacturing activities and facilitating instantaneous defect corrections via parameter adjustments. Our analysis highlights the efficacy of convolutional neural networks (CNNs) models in defect detection, leveraging camera-based systems for an in-depth examination of printed parts. For 1-D data processing, support vector machines (SVMs) and long short-term memory (LSTM) networks have shown significant application and effectiveness. Furthermore, the study classifies various sensors and defects that can effectively benefit from ML-driven QC approaches. Our findings accentuate the essential role of ML, especially CNNs, in detecting and rectifying production flaws and also detail the synergy between different sensor technologies in creating a comprehensive monitoring framework. By integrating ML with a multisensor approach and employing real-time corrective strategies, such as dynamic parameter adjustments and the use of advanced control systems, this research underscores ML's transformative potential in elevating AM QC. Thus, our contribution lays the groundwork for harnessing ML technologies to ensure superior quality parts production in AM, paving the way for its broader industrial adoption.

关键词： Additive manufacturing (AM) defect detection extrusion-based extrusion-based machine learning (ML) machine learning (ML) monitoring process monitoring process monitoring process

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data-driven Adaptive Consensus learning From Network Topologies

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2022年第8期33卷 3487-3497页

作者： Chi, Ronghu Hui, Yu Huang, Biao Hou, Zhongsheng Bu, Xuhui Qingdao Univ Sci & Technol Sch Automat & Elect Engn Inst Artificial Intelligence & Control Qingdao 266061 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China

The problem of consensus learning from network topologies is studied for strongly connected nonlinear nonaffine multiagent systems (MASs). A linear spatial dynamic relationship (LSDR) is built at first to formulate the dynamic I/O relationship between an agent and all the other agents that are communicated through the networked topology. The LSDR consists of a linear parametric uncertain term and a residual nonlinear uncertain term. Utilizing the LSDR, a data-driven adaptive learning consensus protocol (DDALCP) is proposed to learn from both time dynamics of agent itself and spatial dynamics of the whole MAS. The parametric uncertainty and nonlinear uncertainty are estimated through an estimator and an observer respectively to improve robustness. The proposed DDALCP has a strong learning ability to improve the consensus performance because time dynamics and network topology information are both considered. The proposed consensus learning method is data-driven and has no dependence on the system model. The theoretical results are demonstrated by simulations.

关键词： Network topology Uncertainty Topology Consensus control Nonlinear dynamical systems Observers Vehicle dynamics Consensus learning data-driven nonaffine nonlinear multiagent systems (MASs) spatial data model spatial dynamic linearization

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data driven Positive Subspace System Identification 20

Data Driven Positive Subspace System Identification

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20th IFAC Symposium on System Identification (SYSID)

作者： Wang, Yueyang Shafai, Bahram Northeastern Univ Elect & Comp Engn Dept Boston MA 02115 USA

This paper considers the data-driven subspace system identification for positive systems. The application of subspace system identification (SSID) method to collected data of positive systems does not guarantee the positivity of the identified system. Thus, two approaches are proposed to obtain the state space parameters of the positive system using the data collected from its input and output. The first approach offers a procedure based on nonnegative least square algorithm applied to data-driven state space representation. The second approach applies a nonnegative matrix factorization to the data collected Hankel matrix followed by generalized realization algorithm. The effectiveness of two approaches in positive SSID are demonstrated by numerical examples. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： linear system realization subspace system identification data-driven method nonnegative matrix factorization least squares

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Feature Model-based Integration of Machine learning in Software Product Lines 21

Feature Model-based Integration of Machine Learning in Softw...

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21st International conference on Software Architecture (ICSA)

作者： Kholkar, Deepali Thapa, Suraj Pal, Akhilesh Roychoudhury, Suman TCS Res Pune India

ISBN: (纸本)9798350366266;9798350366259

Machine learning techniques are finding widespread application in industry for data-driven analysis and decision making. Software product vendors increasingly need to support data analysis use cases in their products. However integrated software engineering of ML components with software products or systems has not received much attention. This paper presents a feature-model based method and framework for integrating ML development into software product lines from the requirements stage. Our approach includes auto-generation of ML code from specifications to make ML development more accessible for product engineering teams and also enable configurability of ML solutions through their lifetime. We illustrate the approach using a real-world customer ticket classification case study.

关键词： machine learning software engineering for ML feature models MDD ML code generation ML for product lines ML integration

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Local-Global Dynamic Information Fusion Graph learning for Traffic Flow Prediction 13

Local-Global Dynamic Information Fusion Graph Learning for T...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Xu, Yuan Qin, Fan Ke, Wei He, Yan-Lin Zhang, Ming-Qing Zhu, Qun-Xiong Zhang, Yang College of Information Science & Technology Beijing University of Chemical Technology Beijing100029 China Engineering Research Center of Intelligent Pse Ministry of Education of China Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350361674

In the field of intelligent transportation systems, accurately predicting traffic flow is a challenging endeavor for enabling efficient services in urban road networks. While current research has made significant advancements in capturing nonlinear features and spatiotemporal dependencies at a fixed geographic node level, it often overlooks the dynamic interactions of traffic flow across different regions. In this work, we present a novel local-global dynamic information fusion graph learning model (LGDF-GL) for traffic flow prediction. Different from other graph learning model, we first design a dynamic adjacency matrix by region-adaptive parameter learning, and fuse it with a static adjacency matrix based on node geographic information by dynamic information fusion module, and the fused result serves as the diagonal elements of the local adjacency matrix. Subsequently, we input it into the graph convolutional network (GCN) and gated recurrent unit (GRU) aggregation model to extract local feature information containing dynamic spatiotemporal features from traffic flow data. The local feature information is pruned and concatenated to obtain global feature representations. Then we achieve global feature representation prediction through multiple fully connected layers. Finally, we conducted experiments on two real-world traffic flow datasets to evaluate the performance of our proposed method. The results clearly demonstrate that the LGDF-GL method achieves superior prediction ability compared to several baseline models. © 2024 ieee.

关键词： Information fusion

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An Accurate Classification and Recommendation Method of Competitive Math Problems 10

An Accurate Classification and Recommendation Method of Comp...

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ieee 10th International conference on Big data Computing Service and Machine learning Applications (ieee BigdataService)

作者： Shao, Yourui BASIS Independent Silicon Valley San Jose CA 95126 USA

ISBN: (纸本)9798350366396;9798350366389

Personalized and feedback-based learning frequently produces better learning outcomes in the classroom. Digital adaptive learning systems rely upon the categorization of problems and course materials to accurately recommend personalized content to users. While adaptive learning systems have already been widely adopted in math, the problems are often manually categorized and created. This paper discusses the multilabel classification application for categorizing competitive math problems into widely recognized categories using problem text, answer choices, and user-generated solutions with DistilBERT, a Transformer-based language representation model. Additionally, by adding LaTeX control sequences to the tokenizer vocabulary, the trained model was able to recognize symbolic math equations and thus more accurately predicted problem categorization compared to using a tokenizer without additional vocabulary. The trained model with additional vocabulary mostly accurately predicted problem labels across all five general categories, thus demonstrating DistilBERT's ability for problem categorization and the significance of representing symbolic equations with vocabulary. Based on the classification results, a problem recommendation algorithm adaptive to user performance is introduced, to illustrate an application of categorization in adaptive learning systems.

关键词： math problem classification adaptive learning platforms DistilBERT

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Comprehensive Production Index Prediction Using Dual-Scale Deep learning in Mineral Processing

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2024年第4期36卷 6780-6791页

作者： Zhang, Kesheng Yu, Wen Jia, Yao Chai, Tianyou Northeastern Univ Key Lab Integrated Automat Proc Ind Shenyang 110004 Peoples R China Natl Polytech Inst CINVESTAV IPN Dept Control Automat Mexico City 07360 Mexico

In mineral processing, the dynamic nature of industrial data poses challenges for decision-makers in accurately assessing current production statuses. To enhance the decision-making process, it is crucial to predict comprehensive production indices (CPIs), which are influenced by both human operators and industrial processes, and demonstrate a strong dual-scale property. To improve the accuracy of CPIs' prediction, we introduce the high-frequency (HF) unit and low-frequency (LF) unit within our proposed dual-scale deep learning (DL) network. This architecture enables the exploration of nonlinear dynamic mapping in dual-scale industrial data. By integrating the Cloud-Edge collaboration mechanism with DL, our training strategy mitigates the dominance of HF data and guides networks to prioritize different frequency information. Through self-tuning training via Cloud-Edge collaboration, the optimal model structure and parameters on the cloud server are adjusted, with the edge model self-updating accordingly. Validated through online industrial experiments, our method significantly enhances CPIs' prediction accuracy compared to the baseline approaches.

关键词： Minerals Materials processing Predictive models data models Training Indexes Feature extraction Cloud-Edge comprehensive production index (CPI) deep learning (DL) dual-scale mineral processing

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Robust Self-learning Fault-Tolerant control for Hypersonic Flight Vehicle Based on ADHDP

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第9期53卷 5295-5306页

作者： Liang, Shuai Xu, Bin Zhang, Youmin Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Concordia Univ Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada

In this article, a robust self-learning fault-tolerant control (FTC) strategy is proposed to deal with the tracking control problem of the hypersonic flight vehicle (HFV) with uncertainties, actuator faults, and external disturbances. First, an adaptive baseline controller is constructed to achieve stable tracking, in which neural networks are introduced to approximate the unknown dynamics, adaptive laws are formulated to compensate the unknown lumped disturbances, and the Nussbaum technique is applied to address the time-varying actuator faults. Then, to improve the command tracking performance of the baseline controller, a data-driven auxiliary controller which can adaptively adjust the action-critic network weights over time along with the tracking deviation to obtain the optimal control signals in the sense of performance index is developed based on action-dependent heuristic dynamic programming technology. Finally, a comprehensive robust self-learning FTC law is constructed by synthesizing the baseline controller and the auxiliary controller, which leads to good robustness and tracking performance of the closed-loop HFV system. The stability and the superiority of the proposed control algorithm are verified by the Lyapunov theory and comparative numerical simulations, respectively.

关键词： Action-dependent heuristic dynamic programming (ADHDP) adaptive control fault-tolerant control (FTC) hypersonic flight vehicle (HFV) neural network (NN)

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Reinforcement learning Environment for Cyber-Resilient Power Distribution System

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ieee ACCESS 2023年 11卷 127216-127228页

作者： Sahu, Abhijeet Venkatraman, Venkatesh Macwan, Richard Natl Renewable Energy Lab Boulder CO 80305 USA

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. Reinforcement learning (RL)-based techniques provide a credible alternative to conventional, optimization-based solvers especially when there is uncertainty in the environment, such as renewable generation or cyber system performance. Efficiently training an agent, however, requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for power systems, and, similarly, there are environments for communication systems. Most cyber system simulators are based in a UNIX environment, while the power system simulators are based in the Windows operating system. Hence the generation of a cyber-physical, mixed-domain RL environment has been challenging. Existing co-simulation methods are efficient, but are resource and time intensive to generate large-scale data sets for training RL agents. Hence, this work focuses on the development and validation of a mixed-domain RL environment using OpenDSS for the power system and leveraging a discrete event simulator Python package, SimPy for the cyber system, which is operating system agnostic. Further, we present the results of co-simulation and training RL agents for a cyber-physical network reconfiguration and Volt-Var control problem in a power distribution feeder.

关键词： OpenDSS SimPy OpenAI gym network reconfiguration re-routing Reinforcement learning

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