These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
132 related articles for article (PubMed ID: 32403303)
1. Wavelet-Like Transform to Optimize the Order of an Autoregressive Neural Network Model to Predict the Dissolved Gas Concentration in Power Transformer Oil from Sensor Data. Elânio Bezerra F; Zemuner Garcia FA; Ikuyo Nabeta S; Martha de Souza GF; Chabu IE; Santos JC; Junior SN; Pereira FH Sensors (Basel); 2020 May; 20(9):. PubMed ID: 32403303 [TBL] [Abstract][Full Text] [Related]
2. A Ni-Doped Carbon Nanotube Sensor for Detecting Oil-Dissolved Gases in Transformers. Lu J; Zhang X; Wu X; Dai Z; Zhang J Sensors (Basel); 2015 Jun; 15(6):13522-32. PubMed ID: 26066989 [TBL] [Abstract][Full Text] [Related]
3. Highly Sensitive and Selective Acetylene CuO/ZnO Heterostructure Sensors through Electrospinning at Lean O Jung MH; Kwak M; Ahn J; Song JY; Kang H; Jung HT ACS Sens; 2024 Jan; 9(1):217-227. PubMed ID: 38165082 [TBL] [Abstract][Full Text] [Related]
4. Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review. Bustamante S; Manana M; Arroyo A; Castro P; Laso A; Martinez R Sensors (Basel); 2019 Sep; 19(19):. PubMed ID: 31546981 [TBL] [Abstract][Full Text] [Related]
5. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer]. Zhang YX; Cheng ZF; Xu ZP; Bai J Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Jan; 35(1):10-3. PubMed ID: 25993810 [TBL] [Abstract][Full Text] [Related]
6. Low-Power Chemiresistive Gas Sensors for Transformer Fault Diagnosis. Mei H; Peng J; Xu D; Wang T Molecules; 2024 Sep; 29(19):. PubMed ID: 39407555 [TBL] [Abstract][Full Text] [Related]
7. Bio-Inspired PHM Model for Diagnostics of Faults in Power Transformers Using Dissolved Gas-in-Oil Data. Dong H; Yang X; Li A; Xie Z; Zuo Y Sensors (Basel); 2019 Feb; 19(4):. PubMed ID: 30781700 [TBL] [Abstract][Full Text] [Related]
8. Novel Probabilistic Neural Network Models Combined with Dissolved Gas Analysis for Fault Diagnosis of Oil-Immersed Power Transformers. Zhou Y; Tao L; Yang X; Yang L ACS Omega; 2021 Jul; 6(28):18084-18098. PubMed ID: 34308042 [TBL] [Abstract][Full Text] [Related]
9. A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks. Shang H; Liu Z; Wei Y; Zhang S Entropy (Basel); 2024 Feb; 26(3):. PubMed ID: 38539698 [TBL] [Abstract][Full Text] [Related]
10. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques. Illias HA; Chai XR; Abu Bakar AH; Mokhlis H PLoS One; 2015; 10(6):e0129363. PubMed ID: 26103634 [TBL] [Abstract][Full Text] [Related]
11. A Novel Transformers Fault Diagnosis Method Based on Probabilistic Neural Network and Bio-Inspired Optimizer. Tao L; Yang X; Zhou Y; Yang L Sensors (Basel); 2021 May; 21(11):. PubMed ID: 34070963 [TBL] [Abstract][Full Text] [Related]
12. Dissolved Gases Forecasting Based on Wavelet Least Squares Support Vector Regression and Imperialist Competition Algorithm for Assessing Incipient Faults of Transformer Polymer Insulation. Liu J; Zheng H; Zhang Y; Li X; Fang J; Liu Y; Liao C; Li Y; Zhao J Polymers (Basel); 2019 Jan; 11(1):. PubMed ID: 30960069 [TBL] [Abstract][Full Text] [Related]
13. Pt Cluster Modified h-BN for Gas Sensing and Adsorption of Dissolved Gases in Transformer Oil: A Density Functional Theory Study. Gui Y; Li T; He X; Ding Z; Yang P Nanomaterials (Basel); 2019 Dec; 9(12):. PubMed ID: 31817995 [TBL] [Abstract][Full Text] [Related]
14. Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM. Li A; Yang X; Dong H; Xie Z; Yang C Sensors (Basel); 2018 Dec; 18(12):. PubMed ID: 30558208 [TBL] [Abstract][Full Text] [Related]
15. A novel approach for oil-based transformer fault identification in electrical secondary distribution networks. Mbembati H; Bakiri H Heliyon; 2024 Mar; 10(5):e26336. PubMed ID: 38434289 [TBL] [Abstract][Full Text] [Related]
16. Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors. Ward SA; El-Faraskoury A; Badawi M; Ibrahim SA; Mahmoud K; Lehtonen M; Darwish MMF Sensors (Basel); 2021 Mar; 21(6):. PubMed ID: 33810187 [TBL] [Abstract][Full Text] [Related]
17. Fuzzy reinforcement learning based intelligent classifier for power transformer faults. Malik H; Sharma R; Mishra S ISA Trans; 2020 Jun; 101():390-398. PubMed ID: 31959374 [TBL] [Abstract][Full Text] [Related]
18. [Quantitative analysis of transformer oil dissolved gases using FTIR]. Zhao AX; Tang XJ; Wang EZ; Zhang ZH; Liu JH Guang Pu Xue Yu Guang Pu Fen Xi; 2013 Sep; 33(9):2407-10. PubMed ID: 24369641 [TBL] [Abstract][Full Text] [Related]
19. Multigas Analysis by Cavity-Enhanced Raman Spectroscopy for Power Transformer Diagnosis. Wang P; Chen W; Wang J; Tang J; Shi Y; Wan F Anal Chem; 2020 Apr; 92(8):5969-5977. PubMed ID: 32216282 [TBL] [Abstract][Full Text] [Related]
20. Winding-to-ground fault location in power transformer windings using combination of discrete wavelet transform and back-propagation neural network. Chiradeja P; Ngaopitakkul A Sci Rep; 2022 Nov; 12(1):20157. PubMed ID: 36418527 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]