Data-driven Reactive Power Optimization for Distribution Networks Using Capsule Networks
نویسندگان
چکیده
The construction of advanced metering infrastructure and the rapid evolution artificial intelligence bring opportunities to quickly searching for optimal dispatching strategy reactive power optimization. This can be realized by mining existing prior knowledge massive data without explicitly constructing physical models. Therefore, a novel data-driven approach is proposed optimization distribution networks using capsule (CapsNet). convolutional layers with strong feature extraction ability are used project loads space realize automatic key features. Furthermore, complex relationship between input features strategies captured accurately layers. back propagation algorithm utilized complete training process CapsNet. Case studies show that accuracy robustness CapsNet better than those popular baselines (e.g., neural network, multi-layer perceptron, case-based reasoning). Besides, computing time much lower traditional heuristic methods such as genetic algorithm, which meet real-time demand in networks.
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ژورنال
عنوان ژورنال: Journal of modern power systems and clean energy
سال: 2022
ISSN: ['2196-5420', '2196-5625']
DOI: https://doi.org/10.35833/mpce.2021.000033