Unsupervised clustering of disturbances in power systems via deep convolutional autoencoders

• URL: http://arxiv.org/abs/2306.06124v1
• Date: Thu, 8 Jun 2023 04:41:34 GMT
• Title: Unsupervised clustering of disturbances in power systems via deep convolutional autoencoders
• Authors: Md Maidul Islam, Md Omar Faruque, Joshua Butterfield, Gaurav Singh, Thomas A. Cooke
• Abstract summary: Power quality (PQ) events are recorded by PQ meters whenever anomalous events are detected on the power grid. Many of the waveforms captured during a disturbance in the power system need to be labeled for supervised learning. This paper presents an autoencoder and K-means clustering-based unsupervised technique that can be used to cluster PQ events.
• Score: 2.0736732081151366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Power quality (PQ) events are recorded by PQ meters whenever anomalous events are detected on the power grid. Using neural networks with machine learning can aid in accurately classifying the recorded waveforms and help power system engineers diagnose and rectify the root causes of problems. However, many of the waveforms captured during a disturbance in the power system need to be labeled for supervised learning, leaving a large number of data recordings for engineers to process manually or go unseen. This paper presents an autoencoder and K-means clustering-based unsupervised technique that can be used to cluster PQ events into categories like sag, interruption, transients, normal, and harmonic distortion to enable filtering of anomalous waveforms from recurring or normal waveforms. The method is demonstrated using three-phase, field-obtained voltage waveforms recorded in a distribution grid. First, a convolutional autoencoder compresses the input signals into a set of lower feature dimensions which, after further processing, is passed to the K-means algorithm to identify data clusters. Using a small, labeled dataset, numerical labels are then assigned to events based on a cosine similarity analysis. Finally, the study analyzes the clusters using the t-distributed stochastic neighbor embedding (t-SNE) visualization tool, demonstrating that the technique can help investigate a large number of captured events in a quick manner.

Related papers

• DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems [3.44012349879073]
  We present DeepHYDRA (Deep Hybrid DBSCAN/Reduction-Based Anomaly Detection) It combines DBSCAN and learning-based anomaly detection. It is shown to reliably detect different types of anomalies in both large and complex datasets.
  arXiv  Detail & Related papers  (2024-05-13T13:47:15Z)
• 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)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders [0.0]
  We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, without targeting a specific kind of source.
  arXiv  Detail & Related papers  (2021-07-27T09:56:49Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Automatic Feature Extraction for Heartbeat Anomaly Detection [7.054093620465401]
  We focus on automatic feature extraction for raw audio heartbeat sounds, aimed at anomaly detection applications in healthcare. We learn features with the help of an autoencoder composed by a 1D non-causal convolutional encoder and a WaveNet decoder.
  arXiv  Detail & Related papers  (2021-02-24T13:55:24Z)
• Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems [6.4332733596587115]
  We introduce a novel deep learning-based approach for fault detection and test it on a real data set, namely, the Kaggle platform for a partial discharge detection task. Our solution adopts a Long-Short Term Memory architecture with attention mechanism to extract time series features, and uses a 1D-Convolutional Neural Network structure to exploit frequency information of the signal for prediction.
  arXiv  Detail & Related papers  (2020-09-15T02:01:47Z)
• Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data [0.0]
  This paper proposes an anomaly detection method based on a deep autoencoder for in-situ wastewater systems monitoring data. Anomaly detection is then performed based on the reconstruction error of the decoding stage.
  arXiv  Detail & Related papers  (2020-02-07T09:53:46Z)

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.