The Application of RBF Neural Network Optimized by K-means and Genetic-backpropagation in Fault Diagnosis of Power Transformer

Mi, Xinxin; Subramani, Gopinath; Chan, Mieow Kee

doi:10.1051/e3sconf/202124203002

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Neural networks can adequately approximate arbitrarily complex nonlinear relationships, and after learning from the initialized inputs and their relationships, it can also infer unknown relationships from unknown data, thus allowing the model to generalize and predict unknown data. Many researchers have combined neural networks with DGAs, such as RBF neural networks (Mi et al, 2021), probabilistic neural networks (PNN) (Yu et al, 2016;Yang et al, 2019Yang et al, , 2020, Elman neural networks (Duan and Liu, 2011), etc. Many researchers have also applied PNN to early fault diagnosis in transformers.…”

Section: Neural Networkmentioning

confidence: 99%

“…Intelligent techniques help to resolve the uncertainty of traditional DGA methods due to boundary problems and unresolved codes or multi-fault scenarios (Wani et al, 2021). Researchers have applied many artificial intelligence techniques to DGA fault diagnosis, such as neural networks (Duan and Liu, 2011;Wang et al, 2016;Qi et al, 2019;Yan et al, 2019;Yang et al, 2019Yang et al, , 2020Luo et al, 2020;Velásquez and Lara, 2020;Mi et al, 2021;Taha et al, 2021;Zhou et al, 2021), support vector machine (SVM) (Wang and Zhang, 2017;Fang et al, 2018;Huang et al, 2018;Illias and Liang, 2018;Kari et al, 2018;Kim et al, 2019;Zeng et al, 2019;Zhang et al, 2019;Zhang Y. et al, 2020;Benmahamed et al, 2021), and clustering (Islam et al, 2017;Misbahulmunir et al, 2020). These techniques involve statistical machine learning, deep learning, etc.…”

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confidence: 99%

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Fault diagnosis of transformer using artificial intelligence: A review

et al. 2022

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Transformer is one of the important components of the power system, capable of transmitting and distributing the electricity generated by renewable energy sources. Dissolved Gas Analysis (DGA) is one of the effective techniques to diagnose early faults in oil-immersed transformers. It correlates the concentration and ratio of dissolved gases with transformer faults. Researchers have proposed many methods for fault diagnosis, such as double ratio method, Rogers method, Duval triangle method, etc., but all of them have some problems. Based on the strong data mining capability and good robustness of AI techniques, many researchers introduced AI techniques to mine the features of DGA data. According to the characteristics and scale of DGA data, researchers select appropriate AI techniques or make appropriate improvements to AI techniques to improve diagnostic performance. This paper presents a systematic review of the literature on the application of artificial intelligence techniques for DGA-based diagnosis and for solving intractable problems in early transformer fault diagnosis, which include neural networks, clustering, support vector machines, etc. In addition to reviewing the applications of these intelligent techniques, the diagnostic thinking proposed in this literature, such as the introduction of temporal parameters for comprehensive analysis of DGA data and the extraction of optimal features for DGA data, is also reviewed. Finally, this paper summarizes and prospects the artificial intelligence techniques applied by researchers in transformer fault diagnosis.

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Section: Neural Networkmentioning

confidence: 99%

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confidence: 99%

Fault diagnosis of transformer using artificial intelligence: A review

et al. 2022

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“…from various measuring instruments into the trained neural network during the chemical reaction process, The network can predict abnormal situations in time to stop the occurrence of abnormal situations in time [9]. The good adaptability and self-learning performance of artificial neural network in processing high-dimensional, non-linear process industry data can provide petrochemical enterprises with production operating system optimization modeling [10], device optimization [11], dynamic and static equipment fault diagnosis [12], energy saving and safety and environmental protection and pre-efficient support [13].…”

Section: Introductionmentioning

confidence: 99%

Study on the Simulation Control of Neural Network Algorithm in Thermally Coupled Distillation

Zheng

2021

AJRCoS

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Thermally coupled distillation is a new energy-saving method, but the traditional thermally coupled distillation simulation calculation process is complicated, and the optimization method based on the traditional simulation process is difficult to obtain a good feasible solution. The neural network algorithm has the advantages of fast learning and can approach nonlinear functions arbitrarily. For the problems in complex process control systems, neural network control does not require cumbersome control structures or precise mathematical models. When training the network, only the input and output samples it needs are given, so that the dynamics of the system can be controlled. In this way, the dynamic performance of the system can be approximated. This method can effectively solve the mathematical model of the thermally coupled distillation process, and quickly obtain the solution of the optimized variables and the objective function. This article summarizes the research progress of artificial neural network and the optimization control of thermally coupled distillation and the application of neural network in thermally coupled distillation.

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Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

Ngwenyama,

Gitau

2024

Sci Rep

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Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.

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The Application of RBF Neural Network Optimized by K-means and Genetic-backpropagation in Fault Diagnosis of Power Transformer

Cited by 6 publications

References 6 publications

Fault diagnosis of transformer using artificial intelligence: A review

Fault diagnosis of transformer using artificial intelligence: A review

Study on the Simulation Control of Neural Network Algorithm in Thermally Coupled Distillation

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

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