Harmonic Detection Technology for Power Grids Based on Adaptive Ensemble Empirical Mode Decomposition

Harmonic detection and control for power grids have always been major concerns researchers. With the application of diverse semiconductor materials in systems, numerous asymmetrical loads arise, resulting increasingly poor performance traditional harmonic methods. Ensemble empirical mode decomposition (EEMD) provides a new approach systems. Because waves systems are indeterminate, optimal results cannot be achieved by means artificially configured parameters. For such cases, development deep neural networks has provided solution detection. In this study, particle swarm optimization is combined with network to establish an adaptive separation algorithm. By training model manner, EEMD can realized. Moreover, parameters established based on content signals effectively separate orders.