The study of optimized grey model using for transformer fault prediction

2021 ◽  
Author(s):  
Peng Xue ◽  
Yiming Xv ◽  
Naijia Liu
Keyword(s):  
Fault Prediction ◽  
Grey Model ◽  
Transformer Fault

Weak thruster fault prediction method for autonomous underwater vehicles based on grey model

2018 ◽  
Vol 233 (3) ◽  
pp. 348-356
Author(s):  
Weixin Liu ◽  
Mingjun Zhang ◽  
Yujia Wang
Keyword(s):  
Differential Equation ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Grey Model ◽  
Original Sequence ◽  
Background Value ◽  
Operation Sequence

When adopting the conventional grey model (GM(1,1)) to predict weak thruster fault for autonomous underwater vehicles, the prediction error is not always satisfactory. In order to solve the problem, this article develops a new weak thruster fault prediction method based on an improved GM(1,1). In the developed GM(1,1) based fault prediction method, this article mainly makes improvement in the following aspects: construction of grey background value, solution of whiting differential equation and construction of predicted sequence. Specifically, the integral operation is used in range of the two adjacent steps to obtain the grey background value at first. Second, in the solving of whiting differential equation, the point corresponding to the least difference between the accumulated generation sequence and its predicted sequence is determined, and then this special point’s value in the original sequence is considered as the initial condition of the whiting differential equation. Third, in the construction of predicted sequence, another predicted value is obtained based on the error sequence between the accumulated generating operation sequence and its predicted sequence, and then the new predicted result is used to re-adjust the accumulated generating operation sequence, so as to guarantee the re-adjustability of the fault prediction result. Finally, experiments are performed on Beaver 2 autonomous underwater vehicle to evaluate the prediction performance of the developed method.

scholarly journals Bayesian Duval Triangle Method for Fault Prediction and Assessment of Oil Immersed Transformers

2017 ◽  
Vol 50 (4) ◽  
pp. 103-109 ◽  
Author(s):  
Abdelaziz Lakehal ◽  
Fouad Tachi
Keyword(s):  
Bayesian Network ◽  
Gas Analysis ◽  
Fault Prediction ◽  
Dissolved Gas ◽  
Bayesian Network Model ◽  
Prediction Ability ◽  
Dissolved Gas Analysis ◽  
Triangle Method ◽  
Electrical Transformers ◽  
Transformer Fault

Dissolved gas analysis of transformer insulating oil is considered the best indicator of a transformer’s overall condition and is most widely used. In this study, a Bayesian network was developed to predict failures of electrical transformers. The Duval triangle method was used to develop the Bayesian model. The proposed prediction model represents a transformer fault prediction, possible faulty behaviors produced by this transformer (symptoms), along with results of possible dissolved gas analysis. The model essentially captures how possible faults of a transformer can manifest themselves by symptoms (gas proportions). Using our model, it is possible to produce a list of the most likely faults and a list of the most informative gas analysis. Also, the proposed approach helps to eliminate the uncertainty that could exist, regarding the fault nature due to gases trapped in the transformer, or faults that result in more simultaneous gas percentages. The model accurately provides transformer fault diagnosis and prediction ability by calculating the probability of released gases. Furthermore, it predicts failures based on their relationships in the Bayesian network. Finally, we show how the approach works for five distinct electrical transformers of a power plant, by describing the advantages of having available a Bayesian network model based on the Duval triangle method for the fault prediction tasks.

Fast Fault Prediction Model Based on Rough Sets and Grey Model

2013 ◽  
Vol 10 (6) ◽  
pp. 1460-1464 ◽  
Author(s):  
Wei Niu ◽  
Juan Cheng ◽  
Guoqing Wang ◽  
Zhengjun Zhai
Keyword(s):  
Prediction Model ◽  
Rough Sets ◽  
Fault Prediction ◽  
Grey Model ◽  
Model Based

Prediction Research of Transformer Fault Based on Regular Extreme Learning Machine

2014 ◽  
Vol 1049-1050 ◽  
pp. 1205-1209
Author(s):  
Xue Zhen Chen ◽  
Yong Li Zhu ◽  
Fei Pei
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Fault Prediction ◽  
Transformer Oil ◽  
Risk Minimization ◽  
Prediction Ability ◽  
Generalization Performance ◽  
Minimization Principle ◽  
Learning Machine ◽  
Transformer Fault

To predict the concentration of dissolved gas in transformer oil, and then realize the transformer latent fault prediction, can effectively prevent unnecessary loss caused by the transformer faults .In order to improve the transformer fault prediction ability,this paper proposes a new transformer fault prediction model--Regular Extreme Learning Machine (RELM) prediction model。RELM algorithm introduce structure risk minimization principle on the basis of traditional ELM, using the balance factor to weigh the empirical risk and the risk of structure size, further enhance the generalization performance of ELM. Verified by examples, the proposed prediction model based on the RELM in this paper achieve better generalization performance and prediction accuracy in the forecast of gases concentration dissolved in transformer oil.

scholarly journals Fault Prediction using a Grey-Markov Model from the Dissolved Gases Contents in Transformer Oils

2021 ◽  
Vol 256 ◽  
pp. 01038
Author(s):  
Yang Liu ◽  
Yu Du ◽  
Zhiwu Wang ◽  
Guangming Feng ◽  
Shaowei Rao ◽  
...  
Keyword(s):  
Markov Model ◽  
Measurement Data ◽  
Adaptive Method ◽  
Gas Analysis ◽  
Fault Prediction ◽  
Grey Model ◽  
Forecasting Model ◽  
Time Interval ◽  
Dissolved Gases ◽  
Fibonacci Sequences

A novel method to predict transformer fault by forecasting the variation trend of the dissolved gases content is proposed. After the content of each feature gas, such as hydrogen and methane, is obtained by the proposed forecasting model, the fault type can be diagnosed by the dissolved gas analysis (DGA) technologies. Firstly, the GM (1,1) grey model with unequal time interval is introduced to generate a general forecasting model for each feature gas. The introduced grey model with unequal time interval will enforce no constrain on the historical measurement data. Consequently, the time intervals of the two adjacent measuring points can be either constant or variant. To address the deficiency that the existing grey model is unable to describe the fluctuation of the predicted object in time domain, the Markov chain is introduced to improve the accuracy of the grey forecasting model. An adaptive method to automatically divide the state space based on the number of states and the relative error of the grey model is presented by using Fibonacci sequences. Practical measurements are used to verify the accuracy of the proposed forecasting model. The numerical results show that there is high probability (86%) that the proposed grey-Markov model acquires a smaller prediction residual as compared to the original GM(1,1) grey model.

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