A Multidimensional Feature-Driven Ensemble Model for Accurate Classification of Complex Power Quality Disturbance

As the proportion of power electronics-related facilities in modern power systems increases, the types of power quality disturbances (PQDs) tend to become more complex. Traditional methods struggle to accurately perform the classification task of complex PQDs under artificial empirical guidance. This paper proposes a multidimensional feature-driven ensemble model for the accurate classification of complex PQDs, which can complete the self-learning function from data. Unlike existing deep learning-based methods, this model considers both spatial features in the time-frequency domain and temporal relational features. Based on fully convolutional networks (FCN) and bidirectional gated recurrent unit (BiGRU), sub-modules suitable for multidimensional features mining are constructed separately. Meanwhile, the squeeze-and-excitation network (SENet) is introduced to complete the computation of the channel attention mechanism for each convolutional layer, which effectively improves the training efficiency and classification accuracy of the model. The proposed model has been thoroughly tested to validate its effectiveness on a synthetic dataset consisting of 71 different types of PQDs under varying signal-to-noise ratios (SNRs). Additionally, the model has been proven to be robust in the face of external factors such as DC offset, frequency variations, and phase jumps. To further demonstrate its reliability, the proposed model has been tested on real PQDs generated from an AC power source. Results from both simulation and experimentation have conclusively shown that the proposed method is superior to existing deep learning-based methods for the classification of complex PQDs.