EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams -- With Application to Power Quality Disturbance Detection and Classification

• URL: http://arxiv.org/abs/2004.09986v1
• Date: Fri, 17 Apr 2020 07:08:17 GMT
• Title: EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams -- With Application to Power Quality Disturbance Detection and Classification
• Authors: Daniel Leite, Leticia Decker, Marcio Santana, Paulo Souza
• Abstract summary: Real-time detection and classification of disturbances are deemed essential to industry standards. We propose an Evolving Gaussian Fuzzy Classification framework for semi-supervised disturbance detection and classification. Online data-stream-based EGFC method is able to learn disturbance patterns autonomously from never-ending data streams.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages. Real-time detection and classification of disturbances are deemed essential to industry standards. We propose an Evolving Gaussian Fuzzy Classification (EGFC) framework for semi-supervised disturbance detection and classification combined with a hybrid Hodrick-Prescott and Discrete-Fourier-Transform attribute-extraction method applied over a landmark window of voltage waveforms. Disturbances such as spikes, notching, harmonics, and oscillatory transient are considered. Different from other monitoring systems, which require offline training of models based on a limited amount of data and occurrences, the proposed online data-stream-based EGFC method is able to learn disturbance patterns autonomously from never-ending data streams by adapting the parameters and structure of a fuzzy rule base on the fly. Moreover, the fuzzy model obtained is linguistically interpretable, which improves model acceptability. We show encouraging classification results.

Related papers

• AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency [6.7236795813629]
  In streaming setups data flows are affected by factors that yield non-stationarities in the patterns (concept drift) We propose a novel approach, AiGAS-dEVL, which relies on growing neural gas to characterize the distributions of all concepts detected within the stream over time. Our approach exposes that the online analysis of the behavior of these points over time facilitates the definition of the evolution of concepts in the feature space.
  arXiv  Detail & Related papers  (2024-07-07T14:04:57Z)
• Kalman Filter for Online Classification of Non-Stationary Data [101.26838049872651]
  In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps. We introduce a probabilistic Bayesian online learning model by using a neural representation and a state space model over the linear predictor weights. In experiments in multi-class classification we demonstrate the predictive ability of the model and its flexibility to capture non-stationarity.
  arXiv  Detail & Related papers  (2023-06-14T11:41:42Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Power Quality Event Recognition and Classification Using an Online Sequential Extreme Learning Machine Network based on Wavelets [0.0]
  This study implements and tests a prototype of an Online Sequential Extreme Learning Machine (OS-ELM) classifier based on wavelets for detecting power quality problems under transient conditions. Several types of transient events were used to demonstrate the classifier's ability to detect and categorize various types of power disturbances.
  arXiv  Detail & Related papers  (2022-12-27T06:33:46Z)
• 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)
• "Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection [4.371371475735559]
  Anomaly detection is at the forefront of finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability.
  arXiv  Detail & Related papers  (2022-04-19T01:55:58Z)
• Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery [2.580765958706854]
  Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. Traditional Fault Detection and Diagnosis (FDD) frameworks get poor performances when dealing with real-world circumstances. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal-based FDD system.
  arXiv  Detail & Related papers  (2022-02-09T01:09:59Z)
• Overcoming Mode Collapse with Adaptive Multi Adversarial Training [5.09817514580101]
  Generative Adversarial Networks (GANs) are a class of generative models used for various applications. GANs have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator. We introduce a novel training procedure that adaptively spawns additional discriminators to remember previous modes of generation.
  arXiv  Detail & Related papers  (2021-12-29T05:57:55Z)
• Churn Reduction via Distillation [54.5952282395487]
  We show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines.
  arXiv  Detail & Related papers  (2021-06-04T18:03:31Z)
• Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures [100.55816326422773]
  We study the phenomenon of the maximum margin classifier for linear classification problems. Our results precisely characterize the condition under which benign overfitting can occur.
  arXiv  Detail & Related papers  (2021-04-28T08:25:16Z)
• Principal Component Density Estimation for Scenario Generation Using Normalizing Flows [62.997667081978825]
  We propose a dimensionality-reducing flow layer based on the linear principal component analysis (PCA) that sets up the normalizing flow in a lower-dimensional space. We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
  arXiv  Detail & Related papers  (2021-04-21T08:42:54Z)

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.