tailieunhanh - Smart grid islanding, fault detection and classification with distributed generation based on wavelet alienation current signals approach

The Distributed generation (DG) is gaining significant attention due to increase in the demand for electricity. Distributed generations are mostly used with the association of power distribution systems to energize local loads and network. | Smart grid islanding fault detection and classification with distributed generation based on wavelet alienation current signals approach International Journal of Mechanical Engineering and Technology IJMET Volume 10 Issue 03 March 2019 pp. 1792 1805 Article ID IJMET_10_03_181 Available online at http ijmet JType IJMET amp VType 10 amp IType 3 ISSN Print 0976-6340 and ISSN Online 0976-6359 IAEME Publication Scopus Indexed SMART GRID ISLANDING FAULT DETECTION AND CLASSIFICATION WITH DISTRIBUTED GENERATION BASED ON WAVELET ALIENATION CURRENT SIGNALS APPROACH Kamala Devi Kolavennu Department of Electrical and Electronics Engineering Bapatla Engineering College Bapatla Andhra Pradesh India Abdul Gafoor Shaik Department of Electrical Engineering IIT- Jodhpur Rajasthan India ABSTRACT The Distributed generation DG is gaining significant attention due to increase in the demand for electricity. Distributed generations are mostly used with the association of power distribution systems to energize local loads and network. Islanding is technically an undesirable condition that demands necessary steps to reduce negative effects in maintaining stability of the system. In the present work alienation technique has been applied to detect and differentiate islanding faults and sudden load change and faults have been classified. A radial system with four Distributed Generations DFIG wind generator connected to the source through common coupling point PCC has been used for comprehensive study of this technique. Current signals were decomposed with Daubechies db1 wavelet in order to get approximate coefficients at each bus. Coefficients of Alienation are computed by using the wavelet based approximations over a length of half cycle moving window . The alienation coefficients were used to compute Islanding index and fault index. The same indices were compared with threshold to differentiate Islanding faults and sudden load change. The proposed algorithm has .