With the scale of new energy access expanding, the proportion of nonlinear loads in the power grid has increased, leading to frequent impact disturbance events. The types of power quality disturbances (PQDs) are becoming increasingly complex, placing greater demands on the accurate identification of disturbance signals. Therefore, this paper proposes a PQD recognition method based on adaptive KS transform and a Multimodal Feature Fusion Network (MFNet). Firstly, using an improved red-billed blue magpie optimization algorithm, the traditional KS transform window function parameters are adaptively optimized to achieve accurate time–frequency localization of PQD. Secondly, considering the differential characteristics of PQDs in different modes, combined with the proposed adaptive KS transform, a parallel MFNet with three branches in the time domain, frequency domain, and time–frequency domain is constructed; to further enhance feature extraction capability and reduce information loss, residual structures are introduced in the network. Multiple comparative experimental results show that the proposed method achieves an average classification accuracy of 99.52% at 20 dB of noise and demonstrates good noise resistance.
Liu et al. (Thu,) studied this question.
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