A space hybridization theory for dealing with data insufficiency in intelligent power equipment diagnosis

Duan, Jiajun; He, Yigang; Wu, Xiaoxin

doi:10.1016/j.epsr.2021.107363

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…Therefore, it is necessary to effectively identify fault characteristics [1][2][3] . The research shows that the power supply reliability of power system directly affects social production and people's daily life, and in the state of rapid increase in power supply demand [4][5][6] , people have higher and higher requirements for power reliability. When power equipment fails, its signal waveform will change accordingly.…”

Section: Introductionmentioning

confidence: 99%

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

Tang,

Li,

Lei

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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The conventional automatic identification algorithm of double-feature fault signal waveform of power equipment mainly uses ART (Adaptive Resonnance Theory) network for classification and discrimination, which is easily influenced by the identification mapping relationship, resulting in low correct identification rate of fault signal waveform. Therefore, it is necessary to design a brand-new automatic identification algorithm of double-feature fault signal waveform of power equipment. That is to say, the waveform characteristics of dual-feature fault signal of power equipment are extracted, and the optimization algorithm for automatic identification of dual-feature fault signal waveform of power equipment is generated, so that the automatic identification of fault signal waveform is realized. The experimental results show that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has a high correct fault identification rate, which proves that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has good identification effect, reliability and certain application value, and has made certain contributions to improving the operation safety of power equipment.

show abstract

Section: Introductionmentioning

confidence: 99%

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

Tang,

Li,

Lei

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

show abstract

“…Nowadays, deep learning algorithms like CNN and LSTM, which are the most popular data-driven model algorithms, have a high demand for the number of training samples. When most deep learning models deal with fault classification problems, they will make the training samples of each fault type reach equilibrium to avoid the problem that a certain fault type is ignored by the neural network due to a lack of samples [11]. However, the oil-immersed transformer has a low fault incidence and rare fault type samples, which limits the operation efficiency and research directions for transformer fault diagnosis at present.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

Kari¹,

Li²,

Rouzi³

et al. 2023

Computers, Materials &Amp; Continua

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The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long shortterm memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN's data balancing and fault diagnosis. The discriminator adopts multilayer perceptron networks (MLP), which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large. The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models, enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%. Furthermore, the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods. Therefore, the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets. In addition, the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

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Influence of coordinated operation of distributed and centralized thermoelectric dehumidifiers on the environment of prefabricated chambers

Tao

Han

Zhang

et al. 2023

Case Studies in Thermal Engineering

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A space hybridization theory for dealing with data insufficiency in intelligent power equipment diagnosis

Cited by 10 publications

References 28 publications

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

Influence of coordinated operation of distributed and centralized thermoelectric dehumidifiers on the environment of prefabricated chambers

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