Impact of load flow and network reconfiguration for unbalanced distribution systems

被引:0
|
作者
Babu, M. Naveen [1 ]
Dhal, P.K. [1 ]
机构
[1] Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India
来源
Measurement: Sensors | 2024年 / 32卷
关键词
Electric network analysis - Electric power distribution - Iterative methods;
D O I
10.1016/j.measen.2024.101078
中图分类号
学科分类号
摘要
This study focuses on mitigating power losses within unbalanced radial distribution networks by employing transformer modeling and network reconfiguration. The process commences with load flow analysis, utilizing a simplified three-phase load flow method tailored for unbalanced radial distribution networks (URDNs) featuring voltage-dependent loads. Using vector data and fundamental electric circuit analysis, the algorithm efficiently resolves voltage magnitude equations, conserving memory resources, and accurately identifies buses and branches downstream from a designated bus. This method circumvents the repetitive identification issues inherent in conventional forward-backward sweep approaches. The proposed methodology demonstrates robust convergence when applied to URDNs with realistic resistance/reactance ratios and has been rigorously tested on 19-bus and 25-bus unbalanced radial distribution networks. Evaluation criteria encompass CPU execution time and iteration benchmarks. Leveraging empirical formulas, this study achieves optimal designs characterized by improved voltage profiles and reduced power losses. An asymmetric power flow program is employed to compare bus voltages and system power losses, facilitating informed switch operation decisions and allowing for the elimination of feeder sectionalizing switch actions. This approach streamlines CPU processing time by eliminating switching procedures and has been successfully validated using 19-bus and 25-bus URDN samples. This work distinguishes itself through its efficiency, necessitating fewer switching operations when compared to existing methodologies. © 2024 The Authors
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