Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Alexandra Khalyasmaa; Artem Aminev; Dmitry Bliznyuk

doi:10.4028/www.scientific.net/AMM.792.243

Paper Titles

Subsynchronous Resonance in Independent Energo with Distributed Semiconductoring Load
p.215

Analysing the Impact of Electrical Displacement and Leakage Currents in Transformer Insulation on Voltage Measurement Accuracy
p.220

Population-Based Algorithms for Optimization of the Reactive Power Distribution and Selection of the Cable Cross-Section in the Power-Supply Systems
p.230

On Methods of Investigation of Power Generator Differential Protection Behavior in Automatic Mode
p.237

Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)
p.243

The Impact of Distributed Generation on Power Quality of the Electric Network
p.248

Improving of Energy Measurements Reliability Using Weighted and Normalized Residual Analysis
p.255

Problem of Reliability, Verification of Electrical Power Systems Simulation and Ways of Solving it
p.261

Fixing Magnetically Operated Sealed Switches of Current Protections in Closed Electrical Pathways of Electrical Installations at 6-20 kV
p.267

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 792Equipment State Assessment System Based on...

Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract:

The paper is dedicated to analyze the modern expert systems to assess the technical condition of power stations and substations high-voltage equipment. The main problems of modern expert systems and their possible solutions are determined. As the structure and their basic construction principles are considered. Also this paper proposes an algorithm for the expert system model using fuzzy inference on the basis of technical diagnostics and tests. As a case study of assessment of power transformers state based on dissolved gas analysis in the oil is presented.

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Periodical:

Applied Mechanics and Materials (Volume 792)

Pages:

243-247

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.792.243

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Online since:

September 2015

Authors:

Alexandra Khalyasmaa*, Artem Aminev, Dmitry Bliznyuk

Keywords:

ANFIS, Chromatographic Gas Analysis, Expert Systems, Hybrid Neural Network, Technical State Assessment

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* - Corresponding Author

References

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