Intelligent Protection & Classification of Transients in Two-Core Symmetric Phase Angle Regulating Transformers

• URL: http://arxiv.org/abs/2006.09865v1
• Date: Wed, 17 Jun 2020 13:42:58 GMT
• Title: Intelligent Protection & Classification of Transients in Two-Core Symmetric Phase Angle Regulating Transformers
• Authors: Pallav Kumar Bera, Can Isik
• Abstract summary: Internal fault is detected with a balanced accuracy of 99.9%, the faulty unit is localized with balanced accuracy of 98.7% and the no-fault transients are classified with balanced accuracy of 99.5%. The proposed scheme can supervise the operation of existing microprocessor-based differential relays resulting in higher stability and dependability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the applicability of time and time-frequency features based classifiers to distinguish internal faults and other transients - magnetizing inrush, sympathetic inrush, external faults with current transformer saturation, and overexcitation - for Indirect Symmetrical Phase Angle Regulating Transformers (ISPAR). Then the faulty transformer unit (series/exciting) of the ISPAR is located, or else the transient disturbance is identified. An event detector detects variation in differential currents and registers one-cycle of 3-phase post transient samples which are used to extract the time and time-frequency features for training seven classifiers. Three different sets of features - wavelet coefficients, time-domain features, and combination of time and wavelet energy - obtained from exhaustive search using Decision Tree, random forest feature selection, and maximum Relevance Minimum Redundancy are used. The internal fault is detected with a balanced accuracy of 99.9%, the faulty unit is localized with balanced accuracy of 98.7% and the no-fault transients are classified with balanced accuracy of 99.5%. The results show potential for accurate internal fault detection and localization, and transient identification. The proposed scheme can supervise the operation of existing microprocessor-based differential relays resulting in higher stability and dependability. The ISPAR is modeled and the transients are simulated in PSCAD/EMTDC by varying several parameters.

Related papers

• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• Variability-Aware Approximate Circuit Synthesis via Genetic Optimization [13.42152378784862]
  One of the major barriers that CMOS devices face at nanometer scale is increasing parameter variation due to manufacturing imperfections. Previous efforts impose pessimistic and performance-degrading timing guardbands atop the operating frequency. We propose a circuit-agnostic automated framework for generating variability-aware approximate circuits.
  arXiv  Detail & Related papers  (2023-12-23T18:29:26Z)
• Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets [0.0]
  We present a novel deep probabilistic learning approach, the 'Stochastic Latent Transformer' (SLT) The SLT accurately reproduces system dynamics across various integration periods, validated through quantitative diagnostics. It achieves a five-order-of-magnitude speedup in emulating the zonally-averaged flow.
  arXiv  Detail & Related papers  (2023-10-25T16:17:00Z)
• Compatible Transformer for Irregularly Sampled Multivariate Time Series [75.79309862085303]
  We propose a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2023-10-17T06:29:09Z)
• Robust and Efficient Fault Diagnosis of mm-Wave Active Phased Arrays using Baseband Signal [10.64967731507698]
  One key communication block in 5G and 6G radios is the active phased array (APA) This paper proposes a novel method exploiting a Deep Neural Network (DNN) tailored to extract the features hidden in the baseband in-phase and quadrature signals for classifying the different faults. It requires only a single probe in one measurement point for fast and accurate diagnosis of the faulty elements and components in APAs.
  arXiv  Detail & Related papers  (2023-06-07T11:46:14Z)
• Diagnostic Spatio-temporal Transformer with Faithful Encoding [54.02712048973161]
  This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex-temporal (ST) dependency. We formalize the problem as supervised dependency discovery, where the ST dependency is learned as a side product of time-series classification. We show that temporal positional encoding used in existing ST transformer works has a serious limitation capturing frequencies in higher frequencies (short time scales) We also propose a new ST dependency discovery framework, which can provide readily consumable diagnostic information in both spatial and temporal directions.
  arXiv  Detail & Related papers  (2023-05-26T05:31:23Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features [2.382536770336505]
  A three-phase pulse-width modulation (PWM) can usually maintain operation when opencircuit faults occur in insulated-gate bipolar transistors (IGBTs) Data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely effectively.
  arXiv  Detail & Related papers  (2022-11-02T05:48:30Z)
• Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features [0.0]
  A fault diagnosis method based on deep feedforward network with transient synthetic features is proposed. The average fault diagnosis accuracy can reach 97.85% for transient synthetic fault data. Online fault diagnosis experiments show that the method can accurately locate the fault IGBTs.
  arXiv  Detail & Related papers  (2022-11-01T02:32:20Z)
• The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers [59.87030906486969]
  This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse. We show that sparsity is a prevalent phenomenon that occurs for both natural language processing and vision tasks. We discuss how sparsity immediately implies a way to significantly reduce the FLOP count and improve efficiency for Transformers.
  arXiv  Detail & Related papers  (2022-10-12T15:25:19Z)
• 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)

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.