Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Distribution network power flow calculation based on the BPNN optimized by GA‐ADAM

Abstract

Power system operation and control are based on power flow calculations. In order to solve the uncertainty of the increasing penetration of renewable energy, the voltage fluctuation at the load point increases in the distribution network, and the inaccuracy of the power flow calculation due to the insufficient power flow data collection capability of the traditional power system. In this paper, a data‐driven power flow analysis model is proposed, a back propagation neural network combined with genetic algorithm (GA) and adaptive moment estimation (ADAM) optimization algorithm model is constructed to analyze the power flow calculation method of distribution networks under stochasticity. Firstly, the power flow initial value information, topology characteristics, and power factor index are introduced to construct a training set, and the mapping relationship between bus voltage and power is fully explored by training the regression model. Second, the GA‐ADAM algorithm is used to optimize the initial values and weight parameters of the model. Finally, it is verified based on IEEE‐33 bus distribution model, and the model is used for power flow calculation, and compared with other methods through each relevant error evaluation indicators. The results show that the model constructed in this paper has small error indicators and high accuracy, which improves the efficiency and accuracy of power flow calculation.

In order to improve the speed and accuracy of power system power flow calculation, this paper proposes a data driven power flow analysis model, and constructs a power flow calculation method based on back propagation neural network combined with genetic algorithm and Adam optimization algorithm model to analyze distribution networks under randomness.image

http://iet.metastore.ingenta.com/content/journals/10.1049/tje2.12330
Loading

Related content

content/journals/10.1049/tje2.12330
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address