Sage Journals: Discover world-class research

Abstract

This paper presents two methods for condition monitoring of high-voltage equipment based on the thermography approach. The overheating temperature of the hot spot has been obtained using the performed thermography procedure. MATLAB® technical computing software has been used to design the fuzzy controller and artificial neural network. The age of the element, the voltage level, the overheating temperature and the temperature of the previous overheating have been used as the reference inputs for the designed controller and artificial neural network. The developed software tool has been applied for the evaluation of the urgency of intervention in the function of the input data, designed rule base and the methods of defuzzification. Real measurements were used as input data in both methods so that the results were confirmed. The results might serve as a good orientation in the high-voltage equipment condition monitoring. The educational aspects of the application of this software tool are very important for both undergraduate and master's students studying Monitoring and Diagnostics of High Voltage Substations. During the past two academic years, the software application has received favorable comments from students.

Keywords

MATLAB®fuzzy logic artificial neural networks monitoring thermography

Get full access to this article

View all access options for this article.

References

Chen

ATP

Loi

. Three-dimensional thermal imaging for power equipment monitoring. IEEE Proc: Gener Transm Distrib 2000; 147: 355–360.

Carlos

Almeida

Braga

. Intelligent thermographic diagnostic applied to surge arresters: A new approach. IEEE Trans Power Delivery 2009; 24: 751–757.

Song

Johns

. Applications of fuzzy logic in power systems, part II: comparison and integration with expert systems. Neural Networks Genet Algorithms Power Eng J 1998; 12: 185–190.

Jung

Kim

Lee

. Bernd Klockl, Wavelet and neuro-fuzzy based fault location for combined transmission systems. Electr Power Energy Syst 2007; 29: 445–454.

Isermann

. Fault-diagnosis applications, Berlin: Springer, 2011.

Nageswararao

Jeyasurya

. Fuzzy-expert system for voltage stability monitoring and control. Electric Power Syst Res 1998; 47: 215–222.

da Silva

ACM

Garcez Castro

Miranda

. Transformer failure diagnosis by means of fuzzy rules extracted from Kohonen self-organizing map. Electr Power Energy Syst 2012; 43: 1034–1042.

Tanaka

. An introduction on Fuzzy Logic for practical applications, Berlin Heidelberg: Springer, 1996, pp. 255.

Baba

Cin

Bakanay

. A fuzzy system for evaluating students' project in engineering education. Comput Appl Eng Educ 2009; 17: 287–294.

10.

MATLAB®. The language of technical computing, Version 7.11.0 (R2011a), The Math Works Inc., MA, USA, 2011.

11.

Wahab

MAA

Hamada

Mohamed

. An undergraduate fuzzy logic control lab using. Comput Appl Eng Educ 2012; 19: 639–646.

12.

International Electrotehnical Commission, IEC/TR 60943-98, March 2009.

13.

Oguz

Guney

Erdal

. Modeling of hybrid wind-gas power generation system and adaptive neuro-fuzzy controller to improve the system performance. Comput Appl Eng Educ 2010; 18: 669–683.

14.

Toktas

Basak

. Chain gear design using artificial neural networks. Comput Appl Eng Educ 2012; 12: 38–44.

15.

Stojković

. Computer-aided design in power engineering – application of software tools, Berlin Heidelberg: Springer, 2012, pp. 436.

Fuzzy logic and artificial neural network-based thermography approach for monitoring of high-voltage equipment

Abstract

Keywords

Get full access to this article

References