An intelligent protection scheme based on support vector machine for fault detection in microgrid using transient signals in protection scheme by Tiwari Shankarshan Prasad

“…This paper presents a protection scheme based on support vector machines to detect faults under such tedious conditions. …”
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Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults by Lijun Wang, Shengfei Ji, Nanyang Ji

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Winding Fault Detection in Power Transformers Based on Support Vector Machine and Discrete Wavelet Transform Approach by Bonginkosi A. Thango

“…This paper presents a novel diagnostic framework combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classification to improve the detection of TWFs. …”
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Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection by Eleonora Iunusova, Andreas Archenti

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Assessing HMM and SVM for Condition-Based Monitoring and Fault Detection in HEV Electrical Machines by Riham Ginzarly, Nazih Moubayed, Ghaleb Hoblos, Hassan Kanj, Mouhammad Alakkoumi, Alaa Mawas

“…Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). …”
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Failure State Identification and Fault Diagnosis Method of Vibrating Screen Bolt Under Multiple Excitation of Combine Harvester by Jiaojiao Xu, Tiantian Jing, Meng Fang, Pengcheng Li, Zhong Tang

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Hybrid Deep Learning Approach for Accurate Detection and Multiclass Classification of Broken Conductor Faults in Power Distribution Systems by Firas Saadoon Mohammed Al-Jumaili, Mustafa Onat

“…It is shown that the proposed method has higher fault detection and classification accuracy compared to three traditional classification approaches, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and three state-of-the-art methods: 1) Stockwell transform +SVM, 2) Fast Fourier Transform + SVM, and 3) Hilbert-Huang transform of vibration data and power spectral density + Artificial Neural Network. …”
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Detection of Broken Rotor Bars in Presence of Load Oscillations by Klemen Drobnic, Mitja Nemec, Henrik Lavric, Vanja Ambrozic, Rastko Fiser

“…If the detected spectral peak pair corresponds to a BRB, the fault indicator exceeds the threshold. …”
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Infrared Thermography-Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning by Usman Shafique, Syed Muhammad Alam, Umar Rashid, Wahab Javed, Haris Anwaar, Malik Shah Zeb, Talha Ahmad, Uzair Imtiaz, Frederic Nzanywayingoma

“…This paper addresses the critical issue of insulator fault detection in electric substations, emphasizing the importance of timely identification to prevent accidents. …”
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A Fault Identification Method for Electric Submersible Pumps Based on DAE-SVM by Peihao Yang, Jiarui Chen, Hairong Zhang, Sheng Li

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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

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Self-Healing of Active Distribution Networks by Accurate Fault Detection, Classification, and Location by Sally El-Tawab, Hassan S. Mohamed, Amr Refky, A. M. Abdel-Aziz

“…The proposed algorithm utilized a discrete wavelet transform (DWT) to decompose the measured current and zero sequence current component of only one terminal (substation) to detect and classify all fault types with the identification of the faulted phase (s). …”
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Research on series arc fault detection method household loads based on voltage signals by Bin Li, Jiahui Shu, Feifan Cui

“…Compared with the detection results of various algorithms, it is verified that this method has more advantages in the identification of series arc fault. …”
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Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks by Atefeh Pour Shafei, J.Fernando A. Silva, J. Monteiro

“…The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. …”
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Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis by Mohammed Bouzidi, Abdelfatah Nasri, Omar Ouledali, Messaoud Hamouda

“…Research advocates for the integration of artificial neural networks with other machine learning methodologies, such as support vector machines, to improve fault prediction precision. …”
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Advanced Non-Unit Protection Strategy for MMC-HVDC Grids Leveraging Two-Dimensional High-Frequency Characteristics via HHT and SVM by Chenglin Ren, Yige Hu, Xiang Lu, Yue Chen, Kepeng Xia, Hailin Li

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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…Traditional fault detection and isolation methods rely on rule-based diagnostics, which frequently struggle for speed, precision, and adaptability to changing fault conditions. …”
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Early Fault Detection in Electro-Pneumatic Actuators using Mathematical Modelling and Machine Learning: A Bottling Company Case Study by Samuel Olufemi Amudipe, Adeyinka Moses Adeoye, Aderonke Oluwaseunfunmi Akinwumi, Rotimi Adedayo Ibikunle, Segun Adebayo

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SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces by M. P. Michalak, C. Gerhards, P. Menzel

“…<p>The study presents a novel approach for fault detection on subsurface geological homoclinal interfaces (slopes) using a supervised learning algorithm and careful input variable (feature) selection. …”
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An Improved Diagnosis Approach for Short-Circuit Fault Diagnosis in MPC-Based Current Source Inverter System by Jonggrist Jongudomkarn, Pirat Khunkitti, Apirat Siritaratiwat

“…The duration and value of this unchanging optimal vector depend on the type and location of the switch fault, enabling precise fault detection and localization using both phase information and the MPC optimal current vector. …”
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