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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Hua, Shubei Gao, Yang Ma, Bin Li, Xi State Grid Qinghai Elect Power Co Mkt Serv Ctr Xining 810000 Qinghai Peoples R China

ISBN: (纸本)9781665464215

The conventional method of abnormal data detection of electric energy meter mainly analyzes the abnormal data of users' electricity consumption behavior, ignoring the abnormal state of electric energy meters and the abnormal law of load dynamics, which leads to the decrease of the accuracy of abnormal data detection. Therefore, an anomaly detection method based on association rules is designed for the measurement data of the electric energy meter. The abnormal metering data of the electric energy meter is identified according to the power consumption behavior of a user. The abnormal state of the electric energy meter and the load state data, and an abnormal metering data detection model of the electric power meter is constructed under an association rule, so that the number of abnormal points existing in the metering process of an electric energy meter can be detected more accurately. The accurate detection of the abnormal metering data of the electricity meter is realized. The comparative experiments show that the method has higher accuracy of anomaly detection and can be applied to real life.

关键词： Association rules Watt-hour meter Metering data anomaly detection method

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anomaly detection for Charging Voltage Profiles in Battery Cells in an Energy Storage Station Based on Robust Principal Component Analysis

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7552页

作者： Yu, Jiaqi Guo, Yanjie Zhang, Wenjie Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030000 Peoples R China

Lithium-ion batteries, with their high energy density, long cycle life, and non-polluting advantages, are widely used in energy storage stations. Connecting lithium batteries in series to form a battery pack can achieve the required capacity and voltage. However, as the batteries are used for extended periods, some individual cells in the battery pack may experience abnormal failures, affecting the performance and safety of the battery pack. At the same time, as batteries operate in complex environments, the data collected by sensors are susceptible to random noise and drift interference, which can affect the accuracy of anomaly detection in individual battery cells. In order to solve this problem, this article proposes an anomaly detection method for battery cells based on Robust Principal Component Analysis (RPCA), taking the historical operation and maintenance data of a large-scale battery pack from an energy storage station as the research subject. Firstly, theRPCA is used to denoise the observed voltage data of the battery cells to an extreme degree, obtaining a baseline charging state curve for a cell consistency assessment. This also solves the problem of sensor outputs being affected by random noise. To further detect and identify abnormal battery cells, the RPCA is used to extract outlier components. Based on the Average Deviation-3 sigma principle and by utilizing Gaussian distribution probability characteristics, battery cells are conducted to screen, and the serial numbers of the anomaly cells are obtained. Finally, the effectiveness and accuracy of this anomaly detection method for battery cells are compared and verified through different statistical distributions.

关键词： lithium-ion battery robust principal component analysis anomaly detection method energy storage station

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Adaptive anomaly detection for Secure Federated Learning in IIoT Environments

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Procedia Computer Science 2025年 258卷 536-551页

作者： R C Karpagalakshmi J. Lenin P Rajaram V Balaji Rajasekar Rangasamy Akey Sungheetha School of Computer Science and Engineering Galgotias Universit India GITAM School of Technology GITAM University Bengaluru - 560010 India

Federated Learning (FL) systems train models collaboratively among Industrial Internet of Things (IIoT) devices but face prominent security issues, especially backdoor attacks due to diversified data. The presented work proposes an adaptive anomaly detection approach for security enhancement of FL in IIoT environments, considering model performance across diverse data contexts. The proposed approach leverages state-of-the-art graph-based data clustering together with differential privacy to classify data effectively into representative clusters, allowing it to detect anomalies in a localized fashion. It encompasses the most important components: hierarchical clustering, the adaptive anomaly identification module, and secure data aggregation protocol. These inventions reduce the possibility of backdoor attacks, enhance model robustness, and improve model accuracy. The role of this method was extensively validated with benchmark datasets such as Fashion-MNIST, Google Speech Commands, IoT-33, and Smart Manufacturing Systems SMS-IIOT in homogeneous and heterogeneous data settings. The results presented large reductions in backdoor attack success rates, firmly confirming the capability of the proposed solution for enhancing FL security and reliability in IIoT applications.

关键词： Federated learning anomaly detection method Industrial Internet of Things Smart Manufacturing Systems MNIST

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Analysis of Ionospheric Anomalies before the Tonga Volcanic Eruption on 15 January 2022

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REMOTE SENSING 2023年第19期15卷 4879页

作者： Feng, Jiandi Yuan, Yunbin Zhang, Ting Zhang, Zhihao Meng, Di Shandong Univ Technol Sch Civil Engn & Geomatics Zibo 255000 Peoples R China Chinese Acad Sci State Key Lab Space Weather Beijing 100190 Peoples R China Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol Wuhan 430071 Peoples R China

In this paper, GNSS stations' observational data, global ionospheric maps (GIM) and the electron density of FORMOSAT-7/COSMIC-2 occultation are used to study ionospheric anomalies before the submarine volcanic eruption of Hunga Tonga-Hunga Ha'apai on 15 January 2022. (i) We detect the negative total electron content (TEC) anomalies by three GNSS stations on 5 January before the volcanic eruption after excluding the influence of solar and geomagnetic disturbances and lower atmospheric forcing. The GIMs also detect the negative anomaly in the global ionospheric TEC only near the epicenter of the eruption on 5 January, with a maximum outlier exceeding 6 TECU. (ii) From 1 to 3 January (local time), the equatorial ionization anomaly (EIA) peak shifts significantly towards the Antarctic from afternoon to night. The equatorial ionization anomaly double peak decreases from 4 January, and the EIA double peak disappears and merges into a single peak on 7 January. Meanwhile, the diurnal maxima of TEC at TONG station decrease by nearly 10 TECU and only one diurnal maximum occurred on 4 January (i.e., 5 January of UT), but the significant ionospheric diurnal double-maxima (DDM) are observed on other dates. (iii) We find a maximum value exceeding NmF2 at an altitude of 100 similar to 130 km above the volcanic eruption on 5 January (i.e., a sporadic E layer), with an electron density of 7.5 x 10(5) el/cm(3).

关键词： Tonga volcanic eruption ionospheric anomaly anomaly detection method EIA

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Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2020年第1期14卷 17-34页

作者： Ghanbari, Maryam Kinsner, Witold Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract significant distinguishing features during data pre-processing. A support vector machine (SVM) was used for data post-processing. The implementation detected the DDoS attack with 87.35% accuracy.

关键词： anomaly detection method Convolutional Neural Network Cyber and Physical Network Security Discrete Wavelet Transform Distributed Denial of Service Attack Variance Fractal Dimension

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Extracting Features from Both the INput and the Output of a Convolutional Neural Network to Detect Distributed Denial of Service Attacks 17

Extracting Features from Both the INput and the Output of a ...

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17th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Ghanbari, Maryam Kinsner, Witold Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9781538633601

Distributed Denial-of-Service (DDoS) attacks are serious threats to a smart grid infrastructure services' availability, and can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). An improved version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the non-pure fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract distinguishing features during data pre-processing. A support vector machine (SVM) was used for post data-processing. The implementation detected the DDoS attack with 87.35% accuracy.

关键词： Cyber and physical network security anomaly detection method distributed denial of service attack discrete wavelet transform variance fractal dimension convolutional neural network

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Development of Drowsy Driving Accident Prediction by Heart Rate Variability Analysis

Development of Drowsy Driving Accident Prediction by Heart R...

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Annual Summit and Conference of Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA)

作者： Abe, Erika Fujiwara, Koichi Hiraoka, Toshihiro Yamakawa, Toshitaka Kano, Manabu Kyoto Univ Dept Syst Sci Kyoto Japan Kumamoto Univ Prior Org Innovat & Excellence Kumamoto Japan Kumamoto Univ Dept Comp Sci & Elect Engn Kumamoto Japan

ISBN: (纸本)9786163618238

Drowsy driving accidents can be prevented if it can be predicted in advance. The present work aims to develop a new method for predicting a drowsy driving accident based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is the fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in process control. Driving simulator experiments demonstrated that driver drowsiness was successfully predicted seven out of eight cases before drowsy driving accidents occur.

关键词： accident prevention electrocardiography medical signal detection neurophysiology statistical process control ECG HRV analysis MSPC RR interval anomaly detection method autonomic nervous function driver drowsiness drowsy driving accident prediction electrocardiogram heart rate variability analysis multivariate statistical process control Accidents Data models Feature extraction Heart rate variability Monitoring Process control Rail to rail inputs

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detection OF LOCAL SODIUM BOILING IN A SIMULATED LMFBR FUEL SUBASSEMBLY

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PROGRESS IN NUCLEAR ENERGY 1982年第C期9卷 107-121页

作者： INUJIMA, H OGINO, T HAGA, K KIKUCHI, Y POWER REACTOR & NUCL FUEL DEV CORP OARAI ENGN CTROARAIJAPAN KYOTO UNIV DEPT NUCL ENGNKYOTO 606JAPAN

Out-of-pile experiments were carried out to study the feasibility of the local sodium boiling detection by use of temperature, flow and acoustic noises. The studies showed that the temperature fluctuation transferred the information of local sodium boiling to the end of the bundle, but little to the outlet. A new anomaly detection method, which used the algorithm of whiteness test of the residual time series data of autoregressive model, especially using flow fluctuation, was effective detecting the anomaly such as local sodium boiling. On the other hand, acoustic noises with local sodium boiling became much higher than the background such as the mechanical and flow noises. A waveguide method, which was utilized for the present experiments, was useful for the boiling detection.

关键词： acoustic noises anomaly detection method autoregressive model flow fluctuation out-of-pile experiments Sodium boiling temperature fluctuation

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