Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers

• URL: http://arxiv.org/abs/2211.11013v1
• Date: Sun, 20 Nov 2022 16:20:26 GMT
• Title: Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers
• Abstract summary: Nuclear power plant operates a lot of power transformers with extended service life, which exceeds the designated 25 years. An important method for monitoring power transformers is Chromatographic Analysis of Dissolved Gas. This study describes the stages of developing an algorithm to detect defects and faults in transformers automatically using machine learning and data analysis methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Power transformers are an important component of a nuclear power plant (NPP). Currently, the NPP operates a lot of power transformers with extended service life, which exceeds the designated 25 years. Due to the extension of the service life, the task of monitoring the technical condition of power transformers becomes urgent. An important method for monitoring power transformers is Chromatographic Analysis of Dissolved Gas. It is based on the principle of controlling the concentration of gases dissolved in transformer oil. The appearance of almost any type of defect in equipment is accompanied by the formation of gases that dissolve in oil, and specific types of defects generate their gases in different quantities. At present, at NPPs, the monitoring systems for transformer equipment use predefined control limits for the concentration of dissolved gases in the oil. This study describes the stages of developing an algorithm to detect defects and faults in transformers automatically using machine learning and data analysis methods. Among machine learning models, we trained Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks. The best of them were then combined into an ensemble (StackingClassifier) showing F1-score of 0.974 on a test sample. To develop mathematical models, we used data on the state of transformers, containing time series with values of gas concentrations (H2, CO, C2H4, C2H2). The datasets were labeled and contained four operating modes: normal mode, partial discharge, low energy discharge, low-temperature overheating.

Related papers

• Performance Monitoring of Proton Exchange Membrane Water Electrolyzer by Transformers-Based Machine Learning Model [6.608674167337045]
  Green hydrogen plays an essential role in decarbonization, with capacity projected to scale to 560 GW by 2030.<n>We present a machine learning framework that performs virtual electrochemical characterization during normal operation.<n>Inspired by patch-based sequence tokenization, we segment the inputs into patches and encode them to form meaningful tokens.
  arXiv  Detail & Related papers  (2026-05-18T20:49:56Z)
• Quantitative Bounds for Length Generalization in Transformers [58.175107357008876]
  We study the problem of length generalization (LG) in transformers.<n>LG occurs when the internal behavior of the transformer on longer sequences can be "simulated" by its behavior on shorter sequences.
  arXiv  Detail & Related papers  (2025-10-30T21:31:36Z)
• Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights [47.62295798627317]
  This work establishes a theoretical foundation by analyzing the performance of transformers for regression tasks involving noisy input data on a manifold.<n>We prove approximation and generalization errors which crucially depend on the intrinsic dimension of the manifold.<n>Our results demonstrate that transformers can leverage low-complexity structures in learning task even when the input data are perturbed by high-dimensional noise.
  arXiv  Detail & Related papers  (2025-05-06T05:41:46Z)
• One-Layer Transformer Provably Learns One-Nearest Neighbor In Context [48.4979348643494]
  We study the capability of one-layer transformers learning the one-nearest neighbor rule. A single softmax attention layer can successfully learn to behave like a one-nearest neighbor.
  arXiv  Detail & Related papers  (2024-11-16T16:12:42Z)
• Graph Transformers Dream of Electric Flow [72.06286909236827]
  We show that the linear Transformer, when applied to graph data, can implement algorithms that solve canonical problems. We present explicit weight configurations for implementing each such graph algorithm, and we bound the errors of the constructed Transformers by the errors of the underlying algorithms.
  arXiv  Detail & Related papers  (2024-10-22T05:11:45Z)
• In-Context Convergence of Transformers [63.04956160537308]
  We study the learning dynamics of a one-layer transformer with softmax attention trained via gradient descent. For data with imbalanced features, we show that the learning dynamics take a stage-wise convergence process.
  arXiv  Detail & Related papers  (2023-10-08T17:55:33Z)
• Detecting broken Absorber Tubes in CSP plants using intelligent sampling and dual loss [0.0]
  Concentrated solar power (CSP) is one of the growing technologies that is leading the process of changing from fossil fuels to renewable energies. Currently, automatic fault detection in CSP plants using Parabolic Trough Collector systems evidences two main drawbacks. We address both gaps by combining the data extracted with the use of an Unmaned Aerial Vehicle, and the data provided by sensors placed within 7 real plants.
  arXiv  Detail & Related papers  (2022-11-25T12:53:52Z)
• Condition Monitoring of Transformer Bushings Using Computational Intelligence [0.0]
  Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of bushings on large power transformers. This work investigates which gases in DGA are related to each other and which ones are important for making decisions.
  arXiv  Detail & Related papers  (2022-04-20T10:24:30Z)
• Transformers in Time Series: A Survey [66.50847574634726]
  We systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification.
  arXiv  Detail & Related papers  (2022-02-15T01:43:27Z)
• 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)
• Scalable Transformers for Neural Machine Translation [86.4530299266897]
  Transformer has been widely adopted in Neural Machine Translation (NMT) because of its large capacity and parallel training of sequence generation. We propose a novel scalable Transformers, which naturally contains sub-Transformers of different scales and have shared parameters. A three-stage training scheme is proposed to tackle the difficulty of training the scalable Transformers.
  arXiv  Detail & Related papers  (2021-06-04T04:04:10Z)
• Gaze Estimation using Transformer [14.26674946195107]
  We consider two forms of vision transformer which are pure transformers and hybrid transformers. We first follow the popular ViT and employ a pure transformer to estimate gaze from images. On the other hand, we preserve the convolutional layers and integrate CNNs as well as transformers.
  arXiv  Detail & Related papers  (2021-05-30T04:06:29Z)
• Efficient pre-training objectives for Transformers [84.64393460397471]
  We study several efficient pre-training objectives for Transformers-based models. We prove that eliminating the MASK token and considering the whole output during the loss are essential choices to improve performance.
  arXiv  Detail & Related papers  (2021-04-20T00:09:37Z)
• Environmental Pollution Prediction of NOx by Process Analysis and Predictive Modelling in Natural Gas Turbine Power Plants [0.0]
  This paper incorporates ambient weather conditions, electrical output as well as turbine performance factors to build a machine learning model to predict NOx emissions. The model can be used to optimize the operational processes for reduction in harmful emissions and increasing overall operational efficiency.
  arXiv  Detail & Related papers  (2020-11-05T18:01:58Z)
• On Layer Normalization in the Transformer Architecture [112.40350994368741]
  We first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. We show in experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines.
  arXiv  Detail & Related papers  (2020-02-12T00:33:03Z)

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.