| Issue |
E3S Web Conf.
Volume 242, 2021
The 7th International Conference on Renewable Energy Technologies (ICRET 2021)
|
|
|---|---|---|
| Article Number | 03004 | |
| Number of page(s) | 4 | |
| Section | Electronics and Electrical Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202124203004 | |
| Published online | 10 March 2021 | |
Research on DBN-based Evaluation of Distribution Network Reliability
1
State Grid Shanghai Electric Power Research Institute, Shanghai, China
2
Shanghai University of Engineering Science, Shanghai, China
* Corresponding author: rlijia@126.com
In order to accurately and efficiently analyze the reliability of distribution network, this paper proposes a method of analyzing the reliability of distribution network based on a deep belief network. The Deep Belief Network (DBN) is composed of limiting Boltzmann machine layer-by-layer stacking. It has a strong advantage of automatic feature extraction, which overcomes the shortcomings of traditional neural networks in extracting data features. The entire training process of DBN can be roughly divided into two stages: pre-training and fine-tuning.First of all, the pre-training of the DBN model is realized by training the Restricted Boltzmann Machine (RBM) layer by layer, then the BP algorithm is used for reverse fine-tuning to complete the training process of the entire network. finally, the reliability analysis of distribution network is performed by the trained DBN. Compared with the BP neural network method and the traditional Monte Carlo simulation method, it is verified that the proposed model of distribution network reliability analysis has high accuracy.
© The Authors, published by EDP Sciences, 2021
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