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data-driven detection of Hot Spots in Photovoltaic Energy Systems

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年第8期49卷 1731-1738页

作者： Chen, Hongtian Yi, Hui Jiang, Bin Zhang, Kai Chen, Zhiwen Nanjing Univ Aeronaut & Astronaut Div Automat Engn Coll Automat Engn Nanjing 211106 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Jiangsu Key Lab Internet Things & Control Technol Nanjing 211106 Jiangsu Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211800 Jiangsu Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

Hot spots are common abnormalities in photovoltaic (PV) energy systems. Their presence can potentially cause damage to PV modules, such as performance degradation or even unexpected fire to PV energy systems. By sufficiently mining the information hidden in the test data collected from PV modules, this paper develops a space-to-space projection method, which at its core is a linear approach via preserving the locally geometrical structure with respect to time series. Based on the nonlinear model of PV modules established via the proposed projection, data-driven detection of hot spots in PV energy systems can be directly achieved with three key advantages: 1) its implementation does not depend on any mathematical model or physical knowledge of PV energy systems;2) it is of high-computational efficiency especially in the online detection phase;and 3) it can capture the dynamic characteristic because the local structure of samplings regarding time is given sufficient consideration. The effectiveness and feasibility of the proposed approach are first presented by theoretical analysis and, then, convictively demonstrated via 15 sets of hot spot experiments on practical PV modules.

关键词： data-driven detection hot spots photovoltaic (PV) modules

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data-driven detection of Adverse Events in Robotic Needle Steering

Data-driven Detection of Adverse Events in Robotic Needle St...

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International Symposium on Medical Robotics (ISMR)

作者： Narayan, Meenakshi Fey, Ann Majewicz Choti, Michael A. Univ Texas Dallas Mech Engn Richardson TX 75083 USA UT Southwestern Dept Surg Dallas TX USA

ISBN: (纸本)9781538625125

In robotic needle steering, adverse events such as needle buckling, undesired curvature changes, and tissue displacement can occur during needle insertions into tissue, preventing accurate needle placement. This paper focuses on detecting tissue displacement and needle curvature changes using data-driven detection algorithms, informed solely by needle and tissue position measurements. The algorithms were evaluated for different needle insertion velocities and duty-cycles, and analyzed using a Kruskal-Wallis statistical test. Results show that with increased insertion velocity, tissues displace sooner (p < 0.01), while duty-cycles have no effect. Furthermore, with increased insertion velocity and lower duty-cycles, the needle curvature radius decreases (p < 0.01). Additionally, the algorithms were evaluated for needle insertions into ex vivo liver tissue, under live fluoroscopic imaging. The goal of this study was to evaluate the effects of constrained or unconstrained boundary conditions on adverse events. As expected, unconstrained tissue experienced more displacement events. Fluoroscopic videos confirmed accurate and timely prediction of the adverse events. Finally, these algorithms were designed to detect multiple adverse events, simultaneously, and can be implemented in real time.

关键词： data-driven detection adverse events tissue displacement robotic needle steering

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data-driven detection of Needle Buckling Events in Robotic Needle Steering

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Journal of Medical Robotics Research 2019年第2期4卷

作者： Narayan, Meenakshi Choti, Michael A. Fey, Ann Majewicz Department of Mechanical Engineering University of Texas at Dallas 800 W. Campbell Road Richardson 75080 TX United States Department of Surgery UT Southwestern Medical Center Dallas 75390 TX United States

In robotic needle steering, flexible asymmetric-tip needles can steer around obstacles to reach targets deep within tissue. Due to tissue inhomogeneity and needle flexibility, needle buckling can occur, preventing accurate placement. This paper focuses on detecting needle buckling using axial force and needle-tip position readings from sensors. Our algorithm uses errors between the force readings and a predictive force model generated from those readings to track rapid changes in the measured forces. Using this prediction error and needle-tip position, the algorithm detects unexpected force increase, strict needle buckling, and buckling with sliding events at the needle-tip. The metrics for the detections are derived using a standard three-sigma rule and a sigmoid function to ensure generalizability of this method to a variety of tissue types. Our algorithm was tested using insertions into a gelatin tissue with an embedded rectangular obstacle designed to elicit buckling events. Needle buckling was detected at a maximum of 2mm after collision with the obstacle. Our algorithm was tested for robustness with insertions in an ex vivo tissue under different boundary conditions. Our algorithm was also able to detect buckling events 1-2s sooner than human detection times, showing significance for future autonomous control. © 2019 The Author(s).

关键词： data-driven detection image-guided surgery needle buckling robotic needle steering

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A data-driven framework for abnormally high building energy demand detection with weather and block morphology at community scale

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JOURNAL OF CLEANER PRODUCTION 2022年 354卷

作者： Lin, Qi Liu, Ke Hong, Boyeong Xu, Xiaodong Chen, Jiayu Wang, Wei Univ N Carolina Dept City & Reg Planning Chapel Hill NC 27599 USA Columbia Univ Grad Sch Architecture Planning & Preservat 1172 Amsterdam Ave New York NY 10027 USA Southeast Univ Sch Architecture Sipailou 2 Nanjing 210018 Jiangsu Peoples R China NYU Marron Inst Urban Management New York NY 10011 USA City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Y6621AC1Tat Chee Ave Hong Kong Peoples R China

Buildings are one of the most important energy use sectors in cities, and forecasting the abnormal increase in building energy demand in certain climatic conditions is necessary to adjust building energy operations and implement energy policy. Accordingly, this research proposes a data-driven abnormally high energy demand detection framework in urban buildings based on their design parameters and local weather data, with the support of machine learning techniques. In this study, 71 public buildings with energy records in Jianhu city, Jiangsu province, China, were selected to abstract urban morphologies at community scale. The weather profile for the city was obtained from year 2015-2018 to create weather characteristics. Three machine learning algorithms-random forest, support vector machine, and artificial neural network-were applied to identify the months of abnormally high electricity consumption in different building types. This framework also explores key variables in the data and provides the basis for a system that prioritizes the acquisition of variables when complete data is unavailable. The results show that, with complete data, the accuracy score of the system in this study can reach 0.854 with the SVM algorithm, and the model returned an accuracy of 0.865 with the RF model after the key variable selection. Based on those results, the framework in this study can generate preemptive warnings for months with an expected abnormally high energy consumption in target buildings as a prerequisite of energy policy.

关键词： Abnormal high energy demand data-driven detection Machine learning techniques Urban morphology Weather condition

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Observer and data-driven-model-based fault detection in power plant coal mills

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2008年第2期23卷 659-668页

作者： Odgaard, Peter Fogh Lin, Bao Jorgensen, Sten Bay Univ Aalborg Dept Elect Syst DK-9220 Aalborg Denmark KK Elect AS DK-8260 Viby J Denmark Tech Univ Denmark CAPEC Dept Chem Engn DK-2800 Lyngby Denmark

This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain.

关键词： fault detection data-driven detection power plants unknown input observer

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A data-driven Monitoring System for the Early Pest detection in the Precision Agriculture of Hazelnut Orchards 7th

A Data-Driven Monitoring System for the Early Pest Detection...

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7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： Lippi, Martina Carpio, Renzo Fabrizio Contarini, Mario Speranza, Stefano Gasparri, Andrea Univ Roma Tre Rome Italy Univ Tuscia Tuscia Italy

Identifying pests and treating them in a timely manner is a crucial aspect of the Precision Agriculture (PA) paradigm. driven by the needs of the H2020 European Project Pantheon, focused on precision farming in hazelnut orchards, we propose a pest management system for gall-mites, which cause severe symptoms on generative and vegetative buds. We develop a data-driven monitoring system based on You Only Look Once (YOLO) framework enabling the early detection of the pest infestation with mean average precision of 86.7% on a holdout dataset. We perform a thorough analysis on its performance using several data augmentation methods as well as validate its real-time computation capability on a NVIDIA Jetson Xavier, which can be easily integrated into any robotic platform. Finally, we contextualize the role of the proposed detection framework in a comprehensive pest management system. Copyright (C) 2022 The Authors.

关键词： Pest Monitoring data-driven detection Precision Agriculture

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A data-driven Monitoring System for the Early Pest detection in the Precision Agriculture of Hazelnut Orchards

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IFAC-PapersOnLine 2022年第32期55卷 42-47页

作者： Martina Lippi Renzo Fabrizio Carpio Mario Contarini Stefano Speranza Andrea Gasparri Università degli Studi Roma Tre Italy Università degli Studi della Tuscia Italy

关键词： Pest Monitoring data-driven detection Precision Agriculture

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Robust Electricity Theft detection Against data Poisoning Attacks in Smart Grids

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IEEE TRANSACTIONS ON SMART GRID 2021年第3期12卷 2675-2684页

作者： Takiddin, Abdulrahman Ismail, Muhammad Zafar, Usman Serpedin, Erchin Texas A&M Univ Qatar ECEN Program Doha Qatar Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA Hamad Bin Khalifa Univ Qatar Environm & Energy Res Inst Doha Qatar Texas A&M Univ ECEN Dept College Stn TX 77843 USA

data-driven electricity theft detectors rely on customers' reported energy consumption readings to detect malicious behavior. One common implicit assumption in such detectors is the correct labeling of the training data. Unfortunately, these detectors are vulnerable against data poisoning attacks that assume false labels during training. This article addresses three major problems: What is the impact of data poisoning attacks on the detector's performance? Which detector is more robust against data poisoning attacks, i.e., generalized or customer-specific detectors? How to improve the detector's robustness against data poisoning attacks? Our investigations reveal that: (a) Shallow and deep learning-based detectors suffer from data poisoning attacks that may lead to a significant deterioration of detection rate of up to 17%. Furthermore, deep detectors offer 12% performance improvement over shallow detectors. (b) Generalized detectors present 4% performance improvement over customer-specific detectors even in the presence of data poisoning attacks. To enhance the detectors' robustness against data poisoning attacks, we propose a sequential ensemble detector based on a deep auto-encoder with attention (AEA), gated recurrent units (GRUs), and feed forward neural networks. The proposed robust detector retains a stable detection performance that is deteriorated only by 1 - 3% in the presence of strong data poisoning attacks.

关键词： Detectors Training Neural networks Energy consumption Feeds Support vector machines Smart meters Electricity theft data poisoning robust detector machine learning data-driven detection

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Adaptive Energy Theft detection in Smart Grids Using Self-Learning With Dual Neural Network

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第2期20卷 2776-2786页

作者： Althobaiti, Ahlam Rotsos, Charalampos Marnerides, Angelos K. Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 21944 Saudi Arabia Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Scotland

Energy theft is an extremely prominent challenge causing significant energy and revenue losses for utility providers worldwide. The introduction of advanced metering infrastructures consisting of smart meter deployments has undeniably extended the attack surface, enabling individual consumers or prosumers to trigger composite energy theft attack vectors. In this work, we introduce an energy theft detection system capable of distinguishing properties of power consumption and generation theft with possible misconfigurations caused by nonmalicious intent. The proposed approach is adaptive through a self-learning operation that is updated continuously as new measurements become available. With the synergistic use of measurements collected by real PV installations and openly available weather information, the system achieves high accuracy and precision result in theft identification over streamed data measurements. Thus, it promotes low computational costs and its architecture can be easily integrated within smart grid infrastructures to realize next-generation cross-batch energy theft detection.

关键词： Smart grids Loss measurement Transmission line measurements Convolutional neural networks Behavioral sciences Meters Meteorology Cybersecurity data-driven detection energy theft smart grid smart meters (SM)

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Model-Based and data-driven Detectors for Time Synchronization Attacks Against PMUs

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2020年第1期38卷 169-179页

作者： Shereen, Ezzeldin Dan, Gyorgy KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Network & Syst Engn S-11428 Stockholm Sweden

Precise time synchronization of Phasor Measurement Units (PMUs) is critical for monitoring and control of smart grids. Thus, time synchronization attacks (TSAs) against PMUs pose a severe threat to smart grid security. In this paper we present an approach for detecting TSAs based on the interaction between the time synchronization system and the power system. We develop a phasor measurement model and use it to derive an accurate closed form expression for the correlation between the frequency adjustments made by the PMU clock and the resulting change in the measured phase angle, without an attack. We then propose one model-based and three data-driven TSA detectors that exploit the change in correlation due to a TSA. Using extensive simulations, we evaluate the proposed detectors under different strategies for implementing TSAs, and show that the proposed detectors are superior to state-of-the-art clock frequency anomaly detection, especially for unstable clocks.

关键词： Clocks Phasor measurement units Synchronization Detectors Phase measurement Frequency measurement Power system stability PMU time synchronization attacks phasor measurements security cyber-physical systems correlation clock synchronization data-driven detection machine learning

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