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Research on the Identification Method of Series Arc Fault Based on the Feature Sensitivity Analysis and the TVA Coefficient Optimized Random Forest

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IEEE TRANSACTIONS ON POWER DELIVERY 2024年第2期39卷 751-762页

作者： Tong, Haixin Zeng, Xiangjun Yu, Kun Mu, Jingru Luo, Chen Liu, Baiyang Changsha Univ Sci & Technol State Key Lab Disaster Prevent & Reduct Power Grid Changsha 410114 Hunan Peoples R China Hunan Prov Key Lab Grids Operat & Control Multipower Sources Area Shaoyang 422000 Hunan Peoples R China

This paper aims to tackle the difficulties in identifying series arc faults with feature aliasing in low-voltage electricity scenarios. Supported by feature sensitivity analysis, a method for identifying low-voltage series arc faults using a time-variance-accuracy coefficient optimized random forest (TVA-RF) model is proposed. Firstly, in the quantitative analysis of real sample feature sensitivity, it is discovered that the main reason for the above issue is that the sensitivity of the sample features decreases in certain scenarios, thereby interfering with the judgment of traditional arc detection methods. Secondly, the random forest model, with its anti-interference capabilities derived from its ensemble operation mechanism, is utilized as the core model of proposed method. Thirdly, this paper constructs a TVA coefficient to achieve multi-objective optimization of random forest hyper-parameters and institutes a program module that uses low-voltage load state samples for training the random forest model. Additionally, a program for denoising and extracting time-frequency features is embedded into the input of the trained random forest model. Finally, the TVA-RF method hit 99.978% accuracy in laboratory tests, outperforming traditional methods in accuracy, training efficiency, and calculation speed. In real low-voltage systems, it achieved 98.096% accuracy. Overcoming feature overlap interference, the TVA-RF method accurately identified low-voltage series arc faults.

关键词： Sensitivity Low voltage Feature extraction Circuit faults Time-frequency analysis Eigenvalues and eigenfunctions Power supplies hyper-parameter optimization random forest sample feature sensitivity series arc fault

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Hybrid modeling of hetero-agglomeration processes: a framework for model selection and arrangement

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ENGINEERING WITH COMPUTERS 2024年第1期40卷 583-604页

作者： Rhein, Frank Hibbe, Leonard Nirschl, Hermann Karlsruhe Inst Technol KIT Inst Mech Proc Engn & Mech Str Forum 8 D-76131 Karlsruhe Germany

Modeling of hetero-agglomeration processes is invaluable for a variety of applications in particle technology. Traditionally, population balance equations (PBE) are employed;however, calculation of kinetic rates is challenging due to heterogeneous surface properties and insufficient material data. This study investigates how the integration of machine learning (ML) techniques-resulting in so-called hybrid models (HM)-can help to integrate experimental data and close this gap. A variety of ML algorithms can either be used to estimate kinetic rates for the PBE (serial HM) or to correct the PBE's output (parallel HM). As the optimal choice of the HM architecture is highly problem-dependent, we propose a general and objective framework for model selection and arrangement. A repeated nested cross-validation with integrated hyper-parameter optimization ensures a fair and meaningful comparison between different HMs. This framework was subsequently applied to experimental data of magnetic seeded filtration, where prediction errors of the pure PBE were reduced by applying the hybrid modeling approach. The framework helped to identify that for the given data set, serial outperforms parallel arrangement and that more advanced ML algorithms provide better interpolation ability. Additionally, it enables to draw inferences to general properties of the underlying PBE model and a statistical investigation of hyper-parameter optimization that paves the way for further improvements.

关键词： Hetero-agglomeration Population balance equations Machine learning Hybrid modeling hyper-parameter optimization Repeated nested cross-validation

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Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2023年第1期143卷 303-317页

作者： Guo, Hongwei Zhuang, Xiaoying Alajlan, Naif Rabczuk, Timon Bauhaus Univ Weimar Inst Struct Mech Weimar Germany Leibniz Univ Hannover Inst Photon Chair Computat Sci & Simulat Technol Hannover Germany Tongji Univ Dept Geotech Engn Shanghai Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai Peoples R China King Saud Univ Coll Comp & Informat Sci Comp Engn Dept Riyadh Saudi Arabia

We present an adaptive deep collocation method (DCM) based on physics-informed deep learning for the melting heat transfer analysis of a non-Newtonian (Sisko) fluid over a moving surface with nonlinear thermal radiation. Fitted neural network search (NAS) and model based transfer learning (TL) are developed to improve model computational efficiency and accuracy. The governing equations for this boundary-layer flow problem are derived using Buongiorno's and a nonlinear thermal radiation model. Next, similarity transformations are introduced to reduce the governing equations into coupled nonlinear ordinary differential equations (ODEs) subjected to asymptotic infinity boundary conditions. By incorporating physics constraints into the neural networks, we employ the proposed deep learning model to solve the coupled ODEs. The imposition of infinity boundary conditions is carried out by adding an inequality constraint to the loss function, with infinity added to the hyper-parameters of the neural network, which is updated dynamically in the optimization process. The effects of various dimensionless parameters on three profiles (velocity, temperature, concentration) are investigated. Finally, we demonstrate the performance and accuracy of the adaptive DCM with transfer learning through several numerical examples, which can be the promising surrogate model to solve boundary layer problems.

关键词： Deep learning Physics-informed neural networks Melting heat transfer Sisko fluid Boundary layer flow Sensitivity analysis hyper-parameter optimization Transfer learning

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Least squares auto-tuning

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ENGINEERING optimization 2021年第5期53卷 789-810页

作者： Barratt, Shane T. Boyd, Stephen P. Stanford Univ Dept Elect Engn Stanford CA 94305 USA

Least squares auto-tuning automatically finds hyper-parameters in least squares problems that minimize another (true) objective. The least squares tuning optimization problem is non-convex, so it cannot be solved efficiently. This article presents a powerful proximal gradient method for least squares auto-tuning that can be used to find good, if not the best, hyper-parameters for least squares problems. The application of least squares auto-tuning to data fitting is discussed. Numerical experiments on a classification problem using the MNIST dataset demonstrate the effectiveness of the method;it is able to cut the test error of standard least squares in half.

关键词： Least squares hyper-parameter optimization proximal gradient method

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Concurrent surrogate model selection (COSMOS): optimizing model type, kernel function, and hyper-parameters

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2018年第3期57卷 1093-1114页

作者： Mehmani, Ali Chowdhury, Souma Meinrenken, Christoph Messac, Achille Columbia Univ Data Sci Inst New York NY 10027 USA Columbia Univ Earth Inst New York NY 10027 USA SUNY Buffalo Dept Mech & Aerosp Engn Buffalo NY 14228 USA Howard Univ Coll Engn Architecture & Comp Sci Washington DC 20059 USA

This paper presents an automated surrogate model selection framework called the Concurrent Surrogate Model Selection or COSMOS. Unlike most existing techniques, COSMOS coherently operates at three levels, namely: 1) selecting the model type (e.g., RBF or Kriging), 2) selecting the kernel function type (e.g., cubic or multiquadric kernel in RBF), and 3) determining the optimal values of the typically user-prescribed hyper-parameters (e.g., shape parameter in RBF). The quality of the models is determined and compared using measures of median and maximum error, given by the Predictive Estimation of Model Fidelity (PEMF) method. PEMF is a robust implementation of sequential k-fold cross-validation. The selection process undertakes either a cascaded approach over the three levels or a more computationally-efficient one-step approach that solves a mixed-integer nonlinear programming problem. Genetic algorithms are used to perform the optimal selection. Application of COSMOS to benchmark test functions resulted in optimal model choices that agree well with those given by analyzing the model errors on a large set of additional test points. For the four analytical benchmark problems and three practical engineering applications - airfoil design, window heat transfer modeling, and building energy modeling - diverse forms of models/kernels are observed to be selected as optimal choices. These observations further establish the need for automated multi-level model selection that is also guided by dependable measures of model fidelity.

关键词： Automated surrogate model selection hyper-parameter optimization Kriging Mixed-integer non-linear programming (MINLP) Predictive estimation of model fidelity (PEMF) Radial basis functions (RBF) Support vector regression (SVR)

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Cyber-Attack Detection Using Principal Component Analysis and Noisy Clustering Algorithms: A Collaborative Machine Learning-Based Framework

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IEEE TRANSACTIONS ON SMART GRID 2022年第6期13卷 4848-4861页

作者： Parizad, Ali Hatziadoniu, Constantine Southern Illinois Univ Dept Elect & Comp Engn Carbondale IL 62901 USA Virginia Tech Adv Res Inst ARI Arlington VA 22203 USA

This paper proposes a collaborative machine learning-based framework to detect cyber-attacks in a power system, leading to deviation in the state variable behavior. Based on the proposed architecture, three different machine learning-based methods, i.e., visualization, classification, and clustering, are employed and compared to find the best one in the FDIA detection process. To this end, pre-processing is employed in the first stage. In the second stage, the patterns of the state vectors are transferred into features. Hence, 24 statistical features, including measures of central tendency, variability, measures of shape, and position, are extracted to find various properties. Then, in the third stage, a supervised algorithm is employed to rank and find the most crucial features in FDIA. In the fourth stage, an unsupervised dimensionality reduction technique (PCA) is applied to reduce the feature space. In the fifth and last stage, visualization, classification, and clustering-based methods are developed to detect FDIA. To simulate an attack, it is assumed that an intruder decreases or increases the state vectors at different buses with various attack parameters (i.e., 0.90, 0.95, 0.96, 0.97, 0.98, 1, 1.02, 1.03, 1.04, 1.05, and 1.10). The proposed method effectiveness is assessed on the New York Independent System Operator (NYISO) data applied to the IEEE 14-bus system. The results presented in the paper from different scenarios (i.e., phase angle (theta), voltage magnitude (V-m), measurements, and multiple attacks) on a real-world dataset demonstrate that the collaborative optimized PCA-Density-based machine learning technique can detect most of the attack samples with good performance scores (i.e., recall, precision, Fl) and outperforms the other investigated methods. Moreover, it is general and adaptable enough to cover the situation where either the system characteristics or the attack behavior changes.

关键词： False data injection attack (FDIA) principal component analysis noisy clustering algorithm hyper-parameter optimization adaptive semi-supervised learning detection method

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Multi-Objective Surrogate Modeling Through Transfer Learning for Telescopic Boom Forklift

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IEEE ACCESS 2023年 11卷 11629-11641页

作者： Lin, Jingliang Li, Haiyan Huang, Yunbao Liang, Junjie Zhou, Sheng Huang, Zeying Liang, Guiming Guangdong Ocean Univ Sch Mech Engn Zhanjiang 524088 Peoples R China Guangdong Univ Technol Coll Mech & Elect Engn Guangzhou 510006 Peoples R China

Simulation and optimization methods have been widely used in forklift design due to their cost-effectiveness. However, this type of method involves challenges such as the accuracy of the simulation model and the simulation solution time. These challenges reduce the stability and precision of the surrogate model and hence generate further optimization errors. In this paper, a multi-objective surrogate modeling (MSM) method for telescopic boom forklifts based on closed-loop transfer learning is proposed in order to solve these challenges. The MSM consists of the following two steps: to pre-train an initial deep neural network model (deep model) with a large amount of existing simulation data from the same type of forklift and to transfer the model with a small amount of measurement data collected on the current forklift. A general framework for deep neural network (DNN) training is introduced to improve the approximation ability of the initial model. Moreover, a novel uncertainty-analysis-based sampling method is suggested for measurement data development, and combined with transfer learning to form a closed-loop mode to improve the stability of the final model. The superiority of MSM is demonstrated through comparative studies with the fine-tuning method on a telescopic boom forklift with two objectives. The experimental results show that the Correlation coefficient (R) of the deep model can reach 0.9971 by using only 80 sets of training data. In addition, it can also achieve an improvement of at least a 13.25% reduction in Root Mean Squared Error (RMSE) and a 9.19% reduction on average in Maximum Absolute Error (MAE), as well as stronger robustness compared to the benchmarks. Furthermore, it will provide a valuable reference for the simulation optimization of complex electromechanical products.

关键词： Data models Adaptation models Transfer learning Telescopes Predictive models optimization Computational modeling parameter estimation Telescopic boom forklift surrogate model transfer learning uncertainty analysis hyper-parameter optimization

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Better stopping through cross validation in an iterative ensemble smoother: A perspective from supervised machine learning

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 243卷

作者： Luo, Xiaodong Ranazzi, Paulo Norwegian Res Ctr NORCE Nygardsgaten 112 N-5008 Bergen Norway Univ Sao Paulo Rua Reitoria 109Cidade Univ BR-05508030 Sao Paulo Brazil

Iterative ensemble smoothers (IES) are among the popular reservoir data assimilation (RDA) algorithms for reservoir characterization. The actual deployment of an IES algorithm requires implementing certain stopping criteria, normally adopted for runtime control (e.g., by stopping the IES when it reaches the maximum number of iterations) and/or safeguarding the RDA performance (e.g., by preventing the simulated data from overfitting the actual observations). In practice, for various reasons, it is often challenging for existing stopping criteria to simultaneously achieve both purposes. One noticeable issue, as illustrated in this work, is that in many situations, the qualities of the estimated reservoir models may already start to deteriorate before a conventional stopping criterion activates to terminate the iteration process. Following this observation, one practically important question arises: Is it possible to further improve the efficacy of the IES algorithm by designing a different stopping criterion so that the IES can stop earlier, saving computational costs while achieving better RDA performance? As one of the rare attempts in the community, this work aims to investigate the use of a new IES stopping criterion that has the potential to provide an affirmative answer to the above question. In this regard, our main idea is based on the concept of cross validation (CV), routinely adopted in supervised machine learning (SML) problems for early stopping to prevent SML models from overfitting the training data. Despite the noticed similarities between RDA and SML problems, some fundamental differences exist, making it fail to work well if one directly extends a vanilla CV procedure from SML to RDA. To tackle this identified challenge, we design an efficient CV procedure tailored for RDA problems, and inspect the performance of an IES algorithm equipped with this CV procedure (IES-CV) in both synthetic and real field case studies. Our numerical investigation indica

关键词： Ensemble data assimilation Supervised machine learning Iterative ensemble smoother Stopping criterion Cross validation hyper-parameter optimization

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On the analysis of hyper-parameter space for a genetic programming system with iterated F-Race

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SOFT COMPUTING 2020年第19期24卷 14757-14770页

作者： Trujillo, Leonardo Alvarez Gonzalez, Ernesto Galvan, Edgar Tapia, Juan J. Ponsich, Antonin Tecnol Nacl Mexico IT Tijuana Tijuana BC Mexico Maynooth Univ Dept Comp Sci Maynooth Kildare Ireland Inst Politecn Nacl CITEDI Ave Inst Politecn Nacl 1310 Colonia Nueva Tijuana Tijuana 22435 BC Mexico UAM Azcapotzalco Univ Autonoma Metropolitana Ave San Pablo 180 Reynosa 02200 Cdmx Mexico

Evolutionary algorithms (EAs) have been with us for several decades and are highly popular given that they have proved competitive in the face of challenging problems' features such as deceptiveness, multiple local optima, among other characteristics. However, it is necessary to define multiple hyper-parameter values to have a working EA, which is a drawback for many practitioners. In the case of genetic programming (GP), an EA for the evolution of models and programs, hyper-parameter optimization has been extensively studied only recently. This work builds on recent findings and explores the hyper-parameter space of a specific GP system called neat-GP that controls model size. This is conducted using two large sets of symbolic regression benchmark problems to evaluate system performance, while hyper-parameter optimization is carried out using three variants of the iterated F-Race algorithm, for the first time applied to GP. From all the automatic parametrizations produced by optimization process, several findings are drawn. Automatic parametrizations do not outperform the manual configuration in many cases, and overall, the differences are not substantial in terms of testing error. Moreover, finding parametrizations that produce highly accurate models that are also compact is not trivially done, at least if the hyper-parameter optimization process (F-Race) is only guided by predictive error. This work is intended to foster more research and scrutiny of hyper-parameters in EAs, in general, and GP, in particular.

关键词： hyper-parameter optimization Iterated F-Race Genetic programming

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Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第2期5卷

作者： Olson, Matthew L. Liu, Shusen Thiagarajan, Jayaraman J. Kustowski, Bogdan Wong, Weng-Keen Anirudh, Rushil Lawrence Livermore Natl Lab Livermore CA 94550 USA Oregon State Univ Portland OR USA

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

关键词： simulation machine learning deep learning inertial confinement fusion hyper-parameter optimization

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