Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generation

• URL: http://arxiv.org/abs/2301.03766v1
• Date: Tue, 10 Jan 2023 03:06:08 GMT
• Title: Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generation
• Authors: Zuntao Hu and Hongcai Zhang
• Abstract summary: We propose an OPF solver based on a physical-model-integrated neural network (NN) with worth-learning data generation. We show that the proposed method leads to an over 50% reduction of constraint violations and optimality loss compared to conventional NN solvers.
• Score: 1.370633147306388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fast and reliable solvers for optimal power flow (OPF) problems are attracting surging research interest. As surrogates of physical-model-based OPF solvers, neural network (NN) solvers can accelerate the solving process. However, they may be unreliable for ``unseen" inputs when the training dataset is unrepresentative. Enhancing the representativeness of the training dataset for NN solvers is indispensable but is not well studied in the literature. To tackle this challenge, we propose an OPF solver based on a physical-model-integrated NN with worth-learning data generation. The designed NN is a combination of a conventional multi-layer perceptron (MLP) and an OPF-model module, which outputs not only the optimal decision variables of the OPF problem but also the constraints violation degree. Based on this NN, the worth-learning data generation method can identify feasible samples that are not well generalized by the NN. By iteratively applying this method and including the newly identified worth-learning samples in the training set, the representativeness of the training set can be significantly enhanced. Therefore, the solution reliability of the NN solver can be remarkably improved. Experimental results show that the proposed method leads to an over 50% reduction of constraint violations and optimality loss compared to conventional NN solvers.

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