A Trustable Data-Driven Optimal Power Flow Computational Method With Robust Generalization Ability

Data-driven optimal power flow (OPF) approach has been a research focus in recent years. However, the current data-driven OPF approaches face the following difficulties: 1) the data-driven solutions may have large deviations and are not trustable, facing out-of-distribution (OOD) samples and 2) it is hard to judge whether the solutions of the data-driven approach can be trusted. To handle these problems, this article first improves the generalization ability of the data-driven OPF method by embedding the inherent pattern of the OPF solution into the data-driven learning process. As an optimization problem, the OPF solution has certain fixed patterns that are not influenced by the distribution of samples. For example, the load balance constraints should always be satisfied. This leads to an inherent requirement of output vectors, which can be utilized to guide the learning process of the data-driven OPF method. Second, an adaptability judging method based on the decoder neural network is proposed to determine whether the data-driven OPF approach can produce trustable solutions. By measuring the decoding error from latent features to input features, the adaptability of neural networks for input samples could be accurately judged. According to extensive results on various systems, the proposed method can improve the calculation accuracy of OOD data by an average of 30.19% compared with state-of-the-art methods. With the adaptability judgment method, the accuracy of the data-driven approach can achieve higher than 98% for OOD data, whereas the accuracy of other methods ranges from 34.08% to 94.50% on the same set of OOD test data.