Online Probabilistic Power Flow with Tie-line Power Transfer: A Stacked Denoising Auto-Encoders Method

Abstract

Based on stacked denoising auto-encoders (SDAE) and combined with the Monte-Carlo simulation (MCS) method, this paper proposes a hybrid algorithm (SDAE-Based-PPF, SPPF) for online calculation of probabilistic power flow (PPF) with tie-line power transfer. By incorporating the deep structure and reconstructive strategy of SDAE, this paper extracts high-level features of nonlinear power flow equations, thereby constructing SDAE-based power flow models. Then, the parameters of SDAE-based power flow models, with improved training speed and accuracy, are optimized by mini-batch gradient descent algorithm, momentum learning rate and introduced cross-entropy loss function. The SDAE-based power flow models are developed to approximate power flow equations with unidirectional extracted high-level features, so as to enable non-iterative solvability judgment and calculation of power flow, therefore fast and accurate. Benefiting from the parallelizability, speed and accuracy of SDAE, the proposed SDAE-based power flow models, with unsolved random samples generated by the MCS method, are able to calculate all samples simultaneously, and this enables PPF to be calculated online. Finally, numerical results are implemented on three standard IEEE test cases to verify the effectiveness of proposed SDAE-based power flow models and SPPF method.