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RISS 인기검색어
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- 저자 : N. Narasimhulu (검색결과 1 건)
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N. Narasimhulu,D. V. Ashok Kumar,M. Vijay Kumar 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.4
In this paper, the proposed wavelet-based methodology is developed to identify and classify the High Impedance Fault (HIF) in the Power Distribution System (PDS). The planned technique is based on the combination of Ant Lion Optimizer (ALO) and Artifcial Neural Network (ANN), which is performed to accurately isolate the HIF. The change in phase current waveforms caused by faults and normal switching events has been used in this methodology. In order to develop the method to detect high impedance arcing faults under the linear conditions. The faults are identifed through the computation of the basic electric descriptions of current and voltage signals. From the voltage and current signals, the harmonic components also computed. From the voltage, current signals, the fault are identifed and classifed in the system which can be able to solve the problem in the system. The harmonics level also analyzed which also detected and able to correct it for enabling the stable operation in the system. ANN is an Artifcial Intelligence (AI) method that applied for optimizing precise generation limits as blocking happened. The neural network contains two stages: training stage and testing stage. Here, the ALO algorithm is utilized to improve the performance of the ANN training process. ALO is a new nature-inspired algorithm mimicking the hunting behavior of ant lions. The design of Lifting Wavelet Transform (LWT) is suitable for the classifcation process. The main objective of ANN with the aid of the ALO algorithm is the detection and classifcation of the HIF in PDS and analyzed the delay time of diferent locations. From the evaluation of the proposed technique, the inputs and their corresponding outputs are noted. The performance of the work is implemented in MATLAB/Simulink platform and the presentation of this model is investigated on the basis of the two cases of analysis. The results show that the projected algorithm detects the HIFs accurately and compared with the existing methods ALO, GSA and ANN, and GA and Fuzzy, respectively.
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