Review for AI-based Open-Circuit Faults Diagnosis Methods in Power Electronics Converters

• URL: http://arxiv.org/abs/2209.14058v1
• Date: Mon, 26 Sep 2022 06:07:17 GMT
• Title: Review for AI-based Open-Circuit Faults Diagnosis Methods in Power Electronics Converters
• Abstract summary: It is of great significance to carry out power electronics converters open-circuit faults monitoring and intelligent fault diagnosis to avoid secondary faults, reduce time and cost of operation and maintenance, and improve the reliability of power electronics system. Some AI-based fault diagnosis methods and application examples in power electronics converters are reviewed, and a fault diagnosis method based on the combination of random forests and transient fault features is proposed for three-phase power electronics converters.
• Score: 5.708038620963581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Power electronics converters have been widely used in aerospace system, DC transmission, distributed energy, smart grid and so forth, and the reliability of power electronics converters has been a hotspot in academia and industry. It is of great significance to carry out power electronics converters open-circuit faults monitoring and intelligent fault diagnosis to avoid secondary faults, reduce time and cost of operation and maintenance, and improve the reliability of power electronics system. Firstly, the faults features of power electronic converters are analyzed and summarized. Secondly, some AI-based fault diagnosis methods and application examples in power electronics converters are reviewed, and a fault diagnosis method based on the combination of random forests and transient fault features is proposed for three-phase power electronics converters. Finally, the future research challenges and directions of AI-based fault diagnosis methods are pointed out.

Related papers

• A Review of Intelligent Device Fault Diagnosis Technologies Based on Machine Vision [0.0]
  The paper details the structure, working principles, and benefits of Transformers, particularly their self-attention mechanism and parallel computation capabilities. It highlights key Transformer model variants, such as Vision Transformers (ViT) and their extensions, which leverage self-attention to improve accuracy and efficiency in visual tasks. Despite these advancements, challenges remain, including the reliance on extensive labeled datasets, significant computational demands, and difficulties in deploying models on resource-limited devices.
  arXiv  Detail & Related papers  (2024-12-11T07:06:53Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods [0.0]
  This work is a methodology that combines conventional strategy of Motor Current Signature Analysis with functional dimensionality reduction methods, for detecting and classifying fault conditions in induction motors. The results obtained from the proposed scheme are very encouraging, revealing a potential use in the future not only for real-time detection of the presence of a fault in an induction motor, but also in the identification of a greater number of types of faults present through an offline analysis.
  arXiv  Detail & Related papers  (2023-06-14T06:46:58Z)
• The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature [1.450405446885067]
  Transformer fault diagnosis (TFD) is a critical aspect of power system maintenance and management. This review paper provides a comprehensive overview of the current state of the art in TFD using artificial intelligence (AI) and dissolved gas analysis (DGA)
  arXiv  Detail & Related papers  (2023-04-24T07:50:35Z)
• Industrial computed tomography based intelligent non-destructive testing method for power capacitor [10.836558520619706]
  Internal structure of power capacitors would be scanned by the ICT device and then defects could be recognized by the SSD algorithm. The data data augmentation algorithm is used to extend the image set to improve the stability and accuracy of the trained SSD model.
  arXiv  Detail & Related papers  (2023-02-06T07:39:59Z)
• Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches [2.22076315914821]
  The open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter.
  arXiv  Detail & Related papers  (2022-10-31T04:33:53Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Transfer Learning for Fault Diagnosis of Transmission Lines [55.971052290285485]
  A novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. It is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source neural network to predict a dissimilar target dataset.
  arXiv  Detail & Related papers  (2022-01-20T06:36:35Z)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study [0.0]
  Key Gases, Rogers Ratios, IEC Ratios, Duval Triangles, Logarithmic Nomograph used for DGA results. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods.
  arXiv  Detail & Related papers  (2021-08-02T13:09:53Z)
• Massive MIMO As an Extreme Learning Machine [83.12538841141892]
  A massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM) By adding random biases to the received signals and optimizing the ELM output weights, the system can effectively tackle hardware impairments.
  arXiv  Detail & Related papers  (2020-07-01T04:15:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.