A Prediction Model for Time Series of Dissolved Gas Content in Transformer Oil Based on LSTM
Chuye Hu,Yang Zhong,Yiqi Lu,Xiaotong Luo,Shaorong Wang +4 more
- 01 Oct 2020
- Vol. 1659, Iss: 1, pp 012030
TL;DR: In this article, a prediction model based on long short time memory (LSTM) network for time series of dissolved gas content in oil is proposed, which takes advantage of LSTM network's ability to deal with long-sequence prediction problems.
read more
Abstract: By combining with dissolved gas analysis, time series prediction of dissolved gas content in oil provides a basis for transformer fault diagnose and early warning. In the view of that, a prediction model based on long short time memory (LSTM) network for time series of dissolved gas content in oil is proposed, which takes advantage of LSTM network's ability to deal with long-sequence prediction problems. Five characteristic gas concentrations are used as input to the model, and the hyper parameters of the model is optimized by Bayesian optimization algorithm to further improve prediction accuracy, then a LSTM prediction model is constructed. By case study, it is verified that the proposed model can precisely predict time series of dissolved gas content. Compared with gray model, BP neural network and support vector machine, the proposed model has higher prediction accuracy and can better track the trend of time series of dissolved gas content in oil.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Prediction Model of Dissolved Gas in Transformer Oil Based on VMD‐SMA‐LSSVM
TL;DR: In this article , a VMD-SMA-LSSVM combined prediction model was proposed by using variational modal decomposition and least square support vector machine optimized by slime mold algorithm.
10
A long short-term memory based prediction model for transformer fault diagnosis using dissolved gas analysis with digital twin technology
TL;DR: In this paper , a long short-term memory (LSTM) based prediction model is developed to train the digital twin for identifying the essential fault in the transformer via DGA.
4
Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective
Andrew Adewunmi Adekunle,I. Fofana,P. Picher,E. Rodriguez-Celis,Oscar H. Arroyo-Fernandez +4 more
TL;DR: This review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.
3
Lifetime Estimation and Optimal Maintenance Scheduling of Urban Oil-Immersed Distribution-Transformers Considering Weather-Dependent Intelligent Load Model and Unbalanced Loading
01 Oct 2022
TL;DR: In this article , an analytical approach is presented for predicting lifetime of DTs based on degree of polymerization, which is used to forecast driven load according to the ambient temperature and humidity and day-hour schedules.
2
A long short-term memory based prediction model for transformer fault diagnosis using dissolved gas analysis with digital twin technology
TL;DR: In this paper , a long short-term memory (LSTM) based prediction model is developed to train the digital twin for identifying the essential fault in the transformer via DGA.
References
•Journal Article
Random search for hyper-parameter optimization
James Bergstra,Yoshua Bengio +1 more
TL;DR: This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.
•Proceedings Article
Algorithms for Hyper-Parameter Optimization
James Bergstra,Rémi Bardenet,Yoshua Bengio,Balázs Kégl +3 more
- 12 Dec 2011
TL;DR: This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.
From feedforward to recurrent LSTM neural networks for language modeling
TL;DR: This paper compares count models to feedforward, recurrent, and long short-term memory (LSTM) neural network variants on two large-vocabulary speech recognition tasks, and analyzes the potential improvements that can be obtained when applying advanced algorithms to the rescoring of word lattices on large-scale setups.
603
Statistical Machine Learning and Dissolved Gas Analysis: A Review
Piotr Mirowski,Yann LeCun +1 more
TL;DR: The results confirm that nonlinear decision functions, such as neural networks, support vector machines with Gaussian kernels, or local linear regression can theoretically provide slightly better performance than linear classifiers or regressors.