Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems

Abstract

In the evolving landscape of power systems, the integration of various renewable energy resources (RERs) introduces complex challenges, particularly in maintaining power quality, which are paramount for system stability. To address this issue, an adaptive power quality disturbance (PQD) detection framework is implemented in this paper. First, the optimal mode decomposition (OMD) is developed to decompose the compound PQDs into sub-ingredients to make them more visible based on the optimal energy ratio. Subsequently, we propose an improved attention convolutional neural network (IACNN), an advanced neural network architecture that leverages an enhanced attention mechanism to expedite the identification of PQDs. Importantly, the sub-ingredients can be strengthened based on the established PQD detection framework. Finally, a series of experiments are conducted under different noise levels and various types of PQDs. The results demonstrate that the proposed framework has profound detection effectivity with about 99.2% accuracy under the simulation condition of 20 dB noise level. In addition, the experimental verification analysis proves a satisfactory real-time performance. This underscores the potential of the proposed framework as a significant advancement in the realm of power quality management, offering a robust solution to the challenges posed by the integration of RERs into modern power systems.