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Data-driven Reactive Power Optimization for Distribution Networks Using Capsule Networks
The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel data-driven approach is proposed for reactive power optimization of distribution networks using capsule networks (CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines (e.g., convolutional neural network, multi-layer perceptron, and case-based reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.
Data-driven Reactive Power Optimization for Distribution Networks Using Capsule Networks
The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel data-driven approach is proposed for reactive power optimization of distribution networks using capsule networks (CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines (e.g., convolutional neural network, multi-layer perceptron, and case-based reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.
Data-driven Reactive Power Optimization for Distribution Networks Using Capsule Networks
Wenlong Liao (Autor:in) / Jiejing Chen (Autor:in) / Qi Liu (Autor:in) / Ruijin Zhu (Autor:in) / Like Song (Autor:in) / Zhe Yang (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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