Fault detection method for flexible DC grid based on CEEMDAN multiscale entropy and GA-SVM

Compared with the traditional AC grid, the flexible DC grid has the advantages of low wire loss and large transmission capacity, but it is difficult to extract fault signals and diagnose various faults. Therefore, a fault detection method based on complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) multiscale entropy (MSE) and genetic algorithm optimization support vector machine (GA-SVM) is proposed. Firstly, CEEMDAN is used to decompose the extracted fault line mode voltage signal into several intrinsic mode function (IMF). The IMF containing more fault information is selected to reconstruct the denoising signal. The MSE of the reconstructed signal is calculated and input into the GA-SVM classifier as the fault feature, and the fault line mode voltage signals of different fault types under different operating conditions are classified and recognized. A large number of simulation results prove that the proposed method has strong anti-interference ability and high reliability, and has high classification accuracy in the case of small sample data. Compared with Linear-SVM, PSO-SVM, KNN and Fine Tree intelligent algorithms, the proposed method shows a significantly improved accuracy, 93.8888% on average.