Abstract

Switching harmonics generated by power electronic converters have become a critical power quality issue in more-electric aircraft (MEA) dual-generator power systems, and accurate harmonic prediction is essential for effective harmonic cancellation. However, conventional analytical models often suffer from limited prediction accuracy, while data-driven methods are constrained by the scarcity of labeled data under certain operating conditions. This paper presents a method for harmonic prediction and cancellation in the dual-generator power systems of more-electric aircraft. To address the accumulation of switching harmonics on the DC bus, a comparative study is conducted among simplified analytical models, feedforward neural networks, and transfer learning-based models. A harmonic modeling framework and phase shift optimization strategy are established for harmonic cancellation. To overcome data limitations on the high-pressure shaft, transfer learning is employed to transfer knowledge learned from the low-pressure side to the high-pressure side. Results show that data-driven methods outperform traditional models, while transfer learning-based models further improve prediction accuracy and generalization under limited data conditions.

IPC Classification

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