A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound
Source abstract
Abstract
Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Infrastructure Maintenance and Monitoring · High voltage insulation and dielectric phenomena
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electrical Engineering Graduate Program, Regional University of Blumenau (FURB), R. Sao Paulo 3250 (Itoupava Seca), Blumenau 89030-000, Brazil
Electrical Engineering Graduate Program, Santa Catarina State University (UDESC), R. Paulo Malschitzki 200 (North Industrial Zone), Joinville 89219-710, Brazil
Faculty of Engineering and Applied Science, University of Regina, Wascana Parkway 3737, Regina, SK S4S 0A2, Canada
Graduate Program in Applied Computer Science, University of Vale do Itajaí (UNIVALI), R. Uruguai 458 (Centro), Itajaí 88302-202, Brazil
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain
VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
Funding
Junta De Castilla y León—Consejería De Economía Y Empleo · Fundação para a Ciência e a Tecnologia - Portugal
References (61)
Show source-provided references
1. Evaluating Electricity Distributors Efficiency Using Self-Organizing Map and Data Envelopment Analysis10.1109/TLA.2014.7014515 ↗
2. Analysis of Transient Short Circuits in Transmission Lines Using UDW Software
3. Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks10.1109/TSMC.2018.2871750 ↗
4. AC Flashover Voltage Model for Polluted Suspension Insulators and an Experimental Investigation in Salt Fog10.1109/ACCESS.2020.3030326 ↗
5. Wavelet group method of data handling for fault prediction in electrical power insulators10.1016/j.ijepes.2020.106269 ↗
6. Automatic Fault Diagnosis of Infrared Insulator Images Based on Image Instance Segmentation and Temperature Analysis10.1109/TIM.2020.2965635 ↗
7. Analysis of the Electric Field in Porcelain Pin-Type Insulators via Finite Elements Software10.1109/TLA.2018.8795129 ↗
8. An Evaluation of Alternative Techniques for Monitoring Insulator Pollution10.1109/TPWRD.2009.2016628 ↗
9. Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring10.1016/j.energy.2015.09.044 ↗
10. Inclined Plane Test for Erosion of Polymeric Insulators under AC and DC Voltages10.1109/TLA.2020.9111682 ↗
11. Analysis of the dielectric behavior of distribution insulators under non-standard lightning impulse voltages10.1109/TLA.2011.6030983 ↗
12. Short Time Modified Hilbert Transform-Aided Sparse Representation for Sensing of Overhead Line Insulator Contamination10.1109/JSEN.2018.2865009 ↗
13. Application of Machine Learning to Evaluate Insulator Surface Erosion10.1109/TIM.2019.2956300 ↗
14. Fault diagnosis of insulators from ultrasound detection using neural networks10.3233/JIFS-190013 ↗
15. Stefenon, S.F., Freire, R.Z., Coelho, L.S., Meyer, L.H., Grebogi, R.B., Buratto, W.G., and Nied, A. (2020). Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System. Energies, 13.10.3390/en13020484 ↗
16. A submicrometre silicon-on-insulator resonator for ultrasound detection10.1038/s41586-020-2685-y ↗
17. Analysis of training techniques of ANN for classification of insulators in electrical power systems10.1049/iet-gtd.2019.1579 ↗
18. Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization
19. Deep Learning for Photovoltaic Generation Forecast in Active Solar Trackers10.1109/TLA.2019.9011546 ↗
20. Stefenon, S.F., Seman, L.O., Schutel Furtado Neto, C., Nied, A., Seganfredo, D.M., da Garcia, L.F., Sabino, P.H., Torreblanca González, J., and Quietinho Leithardt, V.R. (2020). Electric Field Evaluation Using the Finite Element Method and Proxy Models for the Design of Stator Slots in a Permanent Magnet Synchronous Motor. Electronics, 9.10.3390/electronics9111975 ↗
21. Joint Visualization Diagnosis of Outdoor Insulation Status with Optical and Acoustical Detections10.1109/TPWRD.2018.2870080 ↗
22. Hybrid deep learning for power generation forecasting in active solar trackers10.1049/iet-gtd.2020.0814 ↗
23. Evaluation of Methods for Electric Field Calculation in Transmission Lines10.1109/TLA.2018.8804264 ↗
24. Ribeiro, M.H.D.M., Stefenon, S.F., de Lima, J.D., Nied, A., Marini, V.C., and Coelho, L.d.S. (2020). Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning. Energies, 13.10.3390/en13195190 ↗
25. Robles, G., Fresno, J.M., Martínez-Tarifa, J.M., Ardila-Rey, J., and Parrado-Hernandez, E. (2018). Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization. Sensors, 18.10.3390/s18030746 ↗
26. Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings10.1109/ACCESS.2019.2931144 ↗
27. An Extreme-Learning-Machine-Based Hyperspectral Detection Method of Insulator Pollution Degree10.1109/ACCESS.2019.2937885 ↗
28. Classification of common discharges in outdoor insulation using acoustic signals and artificial neural network10.1049/hve.2019.0113 ↗
29. Uncertainty Sources in the Estimation of the Partial Discharge Inception Voltage in Turn-to-Turn Insulation Systems10.1109/ACCESS.2020.3018870 ↗
30. Bruns, R. (2019). Evaluation of Electrical Insulators Using Ultrasound and Machine Learning. Computer Furthermore, Computer Seminar, SEMINCO.
31. Optimized Ensemble Extreme Learning Machine for Classification of Electrical Insulators Conditions10.1109/TIE.2019.2926044 ↗
32. Diagnostic of Insulators of Conventional Grid Through LabVIEW Analysis of FFT Signal Generated from Ultrasound Detector10.1109/TLA.2017.7910202 ↗
33. Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions10.1109/TLA.2018.8444153 ↗
34. Performance Evaluation of 230 kV Polymer Insulators in the Coastal Area of Saudi Arabia10.1109/ACCESS.2020.3022521 ↗
35. Risk Assessment of Polluted Glass Insulator Using Leakage Current Index Under Different Operating Conditions10.1109/ACCESS.2020.3026136 ↗
36. Understanding surface degradation on polymeric insulators using rotating wheel and dip test under DC stress10.1109/TDEI.2017.007119 ↗
37. The Leakage Current Components as a Diagnostic Tool to Estimate Contamination Level on High Voltage Insulators
38. Studies on metallurgical contamination accumulation characteristics on ceramic insulator of 500 kV AC transmission line10.1049/iet-smt.2018.5099 ↗
39. Measurement of Saturated Water Absorption of the Contamination Layer Deposited on Insulator Surface10.1109/JSEN.2019.2932192 ↗
40. Tracking and erosion resistance of LSR and HTV silicon rubber samples under acid rain conditions10.1109/TDEI.2018.006672 ↗
41. Effects of structure and material of polluted insulators on the wetting characteristics10.1049/iet-smt.2018.5069 ↗
42. Impact of Hydrophobicity on Wetting Characteristics of Composite Insulators10.1109/ACCESS.2020.3019585 ↗
43. Influence of series resistance on dry-band discharge characteristics on wet polluted insulators10.1109/TDEI.2018.007005 ↗
44. (2017). NBR 10621, ABNT High-voltage insulators to be used on A.C. systems—Artificial pollution tests. Braz. Assoc. Tech. Stand., 3, 1–34.
45. (1991). IEC 507, CEI Artificial pollution tests on high-voltage insulators to be used on A.C. systems. Int. Stand., 2, 1–7.
46. Denoising of acoustic partial discharge signals corrupted with random noise10.1109/TDEI.2015.005532 ↗
47. Boya, C., and Robles, G. (2017). Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation. Sensors, 17.10.3390/s17112625 ↗
48. Robles, G., Fresno, J.M., and Martínez-Tarifa, J.M. (2018). Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers. Sensors, 18.10.3390/s18051410 ↗
49. Detection and classification of defects in ceramic insulators using RF antenna10.1109/TDEI.2016.005867 ↗
50. Soltani, A., and El-Hag, A. (2019). Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network. Energies, 12.10.3390/en12183485 ↗
51. Application of Active Methodologies in Engineering Education Through the Integrative Evaluation at the Universidade do Planalto Catarinense, Brazil
52. Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators10.1049/iet-smt:20080009 ↗
53. Influence of artificial neural network structure on quality of short-term electric energy consumption forecast10.1049/ip-gtd:20040070 ↗
54. Tuning of the structure and parameters of a neural network using an improved genetic algorithm10.1109/TNN.2002.804317 ↗
55. Flashover forecasting on high-voltage insulators with a backpropagation neural net10.1109/CJECE.1996.7102114 ↗
56. Haghighi, A., Shadloo, M.S., Maleki, A., and Abdollahzadeh Jamalabadi, M.Y. (2020). Using Committee Neural Network for Prediction of Pressure Drop in Two-Phase Microchannels. Appl. Sci., 10.10.3390/app10155384 ↗
57. Chen, P.C., and Chien, K.Y. (2020). Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper. Appl. Sci., 10.10.3390/app10155342 ↗
58. A neural network based method for leakage current prediction of polymeric insulators10.1109/TPWRD.2005.858805 ↗
59. The discrimination method as applied to a deteriorated porcelain insulator used in transmission lines on the basis of a convolution neural network10.1109/TDEI.2017.006840 ↗
60. Partial discharge signal propagation in medium voltage branched cable feeder10.1109/MEI.2018.8507714 ↗
61. Double-check base editing for efficient A to G conversions10.1021/acssynbio.9b00284 ↗