Application of the Time-varying Outage Model of electric transmission and transformation equipment in Condition-based Maintenance

Ning, Liaoyi; Liu, Jian; Chenghao, Quan; Shen, Fang; Song, Yuan; Liu, Yan

doi:10.1109/isgt-asia.2012.6303104

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The Logistic equation [10] shown in equation ( 17), which is widely used at present, is used to construct a chaotic sequence to enhance the algorithm's capability for global optimization.…”

Section: Improvement Strategymentioning

confidence: 99%

IGSA-PNN-based Methods for Power Transformer Fault Diagnosis

Wang,

Qiu,

Liu

et al. 2024

J. Phys.: Conf. Ser.

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To enhance the precision of power transformer fault diagnosis, it is necessary to make improvements. Aiming at the shortcomings of Probabilistic Neural Network (PNN) network experience selection smoothing factor and avoiding the shortcomings of traditional Gravitational Search Algorithm (GSA) easy 0to fall into local optimum and convergence speed slow, a Probabilistic Neural Network (PNN) model using chaos sequence to improved GSA for power transformer fault diagnosis is proposed. Firstly, chaos sequence is used to increase the diversity of gravitational particles to avoid falling into local optimum during the training process. Then, the improved GSA algorithm is used to optimize the parameters of the PNN model itself to improve the prediction accuracy of the model. Finally, the prediction results are compared with the prediction results of other traditional diagnostic models. The results show that IGSA-PNN fault diagnosis model performs better in generalization ability and classification accuracy.

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“…The Logistic equation [10] shown in equation ( 17), which is widely used at present, is used to construct a chaotic sequence to enhance the algorithm's capability for global optimization.…”

Section: Improvement Strategymentioning

confidence: 99%

IGSA-PNN-based Methods for Power Transformer Fault Diagnosis

Wang,

Qiu,

Liu

et al. 2024

J. Phys.: Conf. Ser.

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“…erefore, their normal and continuous service is vital [4]. In the long-term use of transformer equipment, there are inevitable ageing and failure hidden dangers [5]. Moreover, the cost of a power transformer is high, so it is necessary to diagnose the state of the transformer and take effective measures to fix faults that will occur to ensure the reliable operation of the transformer and reduce the occurrence of faults [6].…”

Section: Introductionmentioning

confidence: 99%

Transformer Fault Identification with an IF-1DCNN Based on Informative Integration of Heterogeneous Sources

Wang

2021

Mathematical Problems in Engineering

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Only using single feature information as input feature cannot fully reflect the transformer fault classification and improve the accuracy of transformer fault diagnosis. To address the above problem, the convolution neural networks’ model is applied for transformer fault assessment designed to implement an end-to-end “different space feature extraction + transformer state diagnosis classification” to enable information from possibly heterogeneous sources to be integrated. This method integrates various feature information of the power transformer operation state to form the isomeric feature, and the model can be used to automatically extract different feature spaces’ information from isomeric feature quantity using its unique one-dimensional convolution and pooling operations. The performance of the proposed approach is compared with that of other models, such as a support vector machine (SVM), backpropagation neural network (BPNN), deep belief network (DBNs), and others. The experimental results show that the proposed one-dimensional convolution neural networks based on an isomeric feature (IF-1DCNN) can accurately classify the fault state of transformer and reduce the adverse interaction between different feature space information in the mixed feature, which has a good engineering application prospect.

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