Hybrid Physical-Data Driven Model for Denoising of Generator State Measurements

The measurement data of power systems are often mixed with a lot of noise due to the interference of the external environment. In order to eliminate the effect of noise, it is significant to denoise the noisy data to obtain the real state measurements. In order to deal with the problem of insufficient interpretability in existing data-driven denoising methods, a hybrid physical-data driven denoising model (PDDM) based on the stacked denoising autoencoder (SDAE) is proposed. First, the previous knowledge is extracted from the physical model of the generator. Physical constraints are designed based on the inherent relationships between rotor angle, angular frequency, and power. Second, based on SDAE deep-learning (DL) model, physical constraints are embedded into the loss function to guide the training of a neural network. The derivatives of denoised data are leveraged in anticipation of satisfying the differential-algebraic equations. The physical process is directly approximated by the neural network in this method, making the outputs satisfy the physical laws. The reliability and interpretability of the denoising results are improved. Meanwhile, the dependence on datasets is reduced by virtue of the hybrid physical-data driven mode. The robustness is still maintained. Finally, it is verified in the 39-bus New England system and a realistic regional power system. The real noisy data are also taken into account in testing to verify its extensibility. The test results show that the method proposed can achieve a satisfactory effect in both denoising accuracy and generalization capability.