Abstract
In response to the demand for identification of distribution network topology with a high percentage of renewable energy penetration, a distribution network topology analysis method based on decision trees and deep learning methods is proposed. First, the decision tree model is constructed to analyze the importance of each node’s characteristics to the observability of the distribution network topology. Next, we arrange the node feature importance from large to small and select the node measurement data with high importance as the training sample set. Then, the principal component analysis (PCA)-deep belief network (DBN) model is used to analyze the changes in the observability of the distribution network topology, and the nodes are selected as the optimal location for the measurement device when the distribution network is completely observable. Finally, the IEEE-33 bus system with a high proportion of renewable energy is used to verify that the method proposed has a good effect in the identification of the distribution network topology.
Highlights
With the large-scale grid connection of renewable energy and the wide application of power electronic devices, the operation of the power system becomes more complex and changeable
Zhao et al.[21] considered the distribution network topology identification of renewable energy, the performance of the proposed method is significantly reduced when the renewable energy penetration rate exceeds 40%
Based on the modified IEEE-33 bus system, as shown in Figure 4, several case studies are tested to prove the effectiveness of the proposed method. ere are 12 normally open switch and 5 normally closed switch used for topology reconfiguration. e total load is 3.715 MW and the types of loads are set as ZIP models. e proportions for the constant power load, constant impedance load, and constant current load are 50%, 30%, and 20%, respectively. ree photovoltaic power plants with rated capacity of 0.5 MW are placed at nodes 17, 21, and 32
Summary
With the large-scale grid connection of renewable energy and the wide application of power electronic devices, the operation of the power system becomes more complex and changeable. Erefore, the configuration principle of the measurement device of the distribution network is to achieve the power grid topology identification with the smallest number of measurement devices. Existing research has achieved certain results for the identification of distribution network topology and the optimization of distribution network topology measurement points, but it does not consider the impact of the high proportion of renewable energy penetration on the configuration plan. A topology analysis method is proposed for distribution network with high proportion of renewable energy based on decision tree and deep learning. (1) is paper studies the distribution network topology analysis method with a high proportion of renewable energy access and analyzes the performance of the proposed method under different renewable energy penetration rates.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
