Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid

Energies

Yeong-Chin Chen¹  Cheng-I Chen²  Chien-Kai Lan³  Yung-Ruei Chang⁴  Chung-Hsien Chen⁵ 

[1] Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan;Department of Electrical Engineering, National Central University, Taoyuan 32001, Taiwan;Department of Mechatronics Engineering, National Changhua University of Education, Changhua 50074, Taiwan;Institute of Nuclear Energy Research, Taoyuan 32546, Taiwan;Metal Industries Research and Development Centre, Taichung 40768, Taiwan;

关键词: fault protection;    power quality;    wavelet energy fuzzy neural network-based technique;    microgrid;    voltage fluctuation;   

DOI  :  10.3390/en13041007

来源: DOAJ

【 摘 要 】

To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid.

【 授权许可】

Unknown