Improving the performance of grid-connected doubly fed induction generator by fault identification and diagnosis: A kernel PCA-ESMO technique

摘要

In this manuscript, a novel Kernel PCA-ESMO technique is proposed for protecting the system and diagnosing the exact fault occuring in the DFIG. The proposed method is joined implementation of Kernel principal component analysis (Kernel PCA) and enhanced Spider Monkey Optimization SMO (ESMO) technique, and, hence, it is named Kernel PCA-ESMO approach. Here, two phases are considered for fault analysis; they are fault identification and diagnosis. Primarily, the first phase identifies the system fault conditions of DFIG in grid-connected system using Kernel PCA approach. After that, the ESMO classifies the type of fault that has occurred in the DFIG. The major scope of the proposed Kernel PCA-ESMO method is to guarantee the system with less complexity for fault identification and diagnosis for enhancing the power quality of whole system. The implementation of proposed model is made at MATLAB/Simulink, and implementation is evaluated by existing techniques. The statistic analysis of proposed and existing systems of mean, median, and SD is analyzed. The efficiency of proposed and existing technique is also evaluated. The obtained values for percentage recall and precision that are enhanced using proposed technique are 97.8% and 98%. Consequently, the simulation outcome indicates that the efficiency of proposed technique and implementation of proposed strategy is compared to existing systems.