Abstract
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.
Highlights
Due to advantages of reducing the investment of new energy costs in power systems, providing the compensation of reactive power, regulating the frequency of power system, increasing the system backup capacity, and improving the stability of power system, the distributed generation has become popular in recent years [1,2]
Through the extracted features of fuzzy analysis, the identification of power quality disturbances based on the fuzzy neural network is established for the fault protection of microgrid, as depicted in
A robust system based on the wavelet energy fuzzy neural network-based technique for fault identification and protection in the microgrid is developed
Summary
Due to advantages of reducing the investment of new energy costs in power systems, providing the compensation of reactive power, regulating the frequency of power system, increasing the system backup capacity, and improving the stability of power system, the distributed generation has become popular in recent years [1,2]. The complexity of classifier has been dramatically increased and the hardware requirements for the implementation have been critical To resolve this problem, the feature condensation based on the wavelet energy and variation of singular power signal is proposed in this paper to integrate the feature information. The proposed wavelet energy fuzzy neural network-based technique (WEFNNBT) can be divided into three stages, including feature extraction (FE), feature condensation (FC), and disturbance identification (DI). 2. Proposed Wavelet Energy Fuzzy Neural Network-Based Microgrid Fault Protection Strategy. Are selected as the important features for the fault identification of proposed fuzzy neural energy of wavelet coefficients are calculated. Through the extracted features of fuzzy analysis, the identification of power quality disturbances based on the fuzzy neural network is established for the fault protection of microgrid, as depicted in Input signals, as shown in Equation (10),j where wj is the weight associated with jth rule
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