A novel study for hydro-generators loss of excitation faults detection using ANFIS. (2nd January 2017)

• Record Type: Journal Article
• Authors: Abdel Aziz, M. S.; Elsamahy, M.; Moustafa Hassan, M. A.; Bendary, F. M. A.
• Is Part Of: International journal of modelling & simulation. Volume 37:Number 1(2017)
• Page Start: 36
• Page End: 45
• Publication Date: 2017-01-02
• Subjects: Adaptive neuro fuzzy inference system -- loss of excitation -- hydro-generator -- dynamic performance -- simulation; Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2016.1232956
• Languages: English
• ISSNs: 0228-6203

Abstract

Abstract: This paper presents a new approach for Loss of Excitation (LOE) faults detection in hydro-generators using adaptive neuro fuzzy inference system. The proposed scheme was trained by data from simulation of a 345 kV system under various faults conditions and tested for different loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the paper. Three techniques are used in this article based on the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements ( R and X ), the generator RMS Line to Line voltage and Phase current ( V trms and I a ) and the positive sequence components of the generator voltage magnitude, phase current magnitude, and angle (│ V +ve │, │ I +ve │, and ∟ I +ve ). The obtained results from these schemes are compared with each other and are compared with other techniques. The results show that the proposed technique is effective in detecting the LOE faults and the obtained results are very encouraging.