Dynamic Fault Prediction of Power Transformers Based on Hidden Markov Model of Dissolved Gases Analysis

Jiang, Jun; Chen, Ruyi; Chen, Min; Wang, Wenhao; Zhang, Chaohai

doi:10.1109/tpwrd.2019.2900543

Cited by 91 publications

(27 citation statements)

References 28 publications

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“…In [20], a hidden-Markov model (HMM) is utilised to determine a transformer fault model. The model is utilised to determine the dissolved gas concentration.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault detection method in subsea power distribution systems using statistical optimisation

Nademi

Vanfretti

Pretlove

2020

IET Energy Systems Integration

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Developing automation solutions that enable remote communications, monitoring and control for subsea applications are key steps in designing subsea power distribution systems. These systems require fast local control to protect the multiple electrical loads and the capability of transferring prompt real-time trip signals. This study introduces a data-driven distributed fault detection and identification algorithm to monitor multiple subsea loads. The proposed scheme is divided into three steps. First, a stochastic hidden-Markov model (HMM) is developed to model the dynamic evolution of different potential conditions of multiple subsea loads. Simultaneously, the second step computes a model of the transition probability between the current operating condition and the potential response of an individual load. In the third step, using real-time measurements, the HMM is updated to predict an unobserved degradation of the load's current condition. This is achieved through an integrated perturbation analysis and sequential quadratic programming method. An assessment of case studies on subsea AC power system is presented, which includes different subsea motor loads for compressors and pumps. Results show robustness against uncertainty in measurement noise and changes in equipment mean time between failures, providing enhanced reliability.

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“…In [20], a hidden-Markov model (HMM) is utilised to determine a transformer fault model. The model is utilised to determine the dissolved gas concentration.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault detection method in subsea power distribution systems using statistical optimisation

Nademi

Vanfretti

Pretlove

2020

IET Energy Systems Integration

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“…As one of the main pillars of the current economy, electric energy is gradually accelerating the pace of its intelligent construction, and the scale is also expanding. The oil-immersed transformer, as the key hub of a power system, undertakes the task of power transmission and transformation of the whole power grid, and its operation condition will directly affect the safety of the power network and users [1][2][3][4]. However, insulation faults like partial discharge and partial overheating inevitably exist during oil-immersed transformer long running process [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

The Adsorption of H2 and C2H2 on Ge-Doped and Cr-Doped Graphene Structures: A DFT Study

Liao

Peng

et al. 2021

Nanomaterials

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In order to find an excellent sensing material for dissolved gases in transformer oil, the adsorption structures of intrinsic graphene (IG), Ge-doped graphene (GeG), and Cr-doped graphene (CrG) to H2 and C2H2 gas molecules were built. It was found that the doping site right above C atom (T) was the most stable structure by studying three potential doping positions of the Ge and Cr atom on the graphene surface. Then, the structural parameters, density of states, and difference state density of these adsorption systems were calculated and analyzed based on the density functional calculations. The results show that the adsorption properties of GeG and CrG systems for H2 and C2H2 are obviously better than the IG system. Furthermore, by comparing the two doping systems, CrG system exhibits more outstanding adsorption performances to H2 and C2H2, especially for C2H2 gas. Finally, the highest adsorption energy (−1.436 eV) and the shortest adsorption distance (1.981 Å) indicate that Cr-doped graphene is promising in the field of C2H2 gas-sensing detection.

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“…The cloud-based reasoning and semi-Markov model are used to predict and diagnose transformer faults [4]. Hidden Markov model together with Gaussian mixture model is applied to the dynamic fault prediction of power transformers [5]. The prediction problem of transformer faults is converted into a multi-dimensional regression one and then solved by robust optimizations [6].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wang

et al. 2021

E3S Web Conf.

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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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Dynamic Fault Prediction of Power Transformers Based on Hidden Markov Model of Dissolved Gases Analysis

Cited by 91 publications

References 28 publications

Fault detection method in subsea power distribution systems using statistical optimisation

Fault detection method in subsea power distribution systems using statistical optimisation

The Adsorption of H2 and C2H2 on Ge-Doped and Cr-Doped Graphene Structures: A DFT Study

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

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