Intelligent inspection of hybrid AC/DC distribution networks considering flexible load integration
Year: 2025
Type: article
Abstract: In complex operational environments of power systems, the inspection of distribution networks becomes considerably challenging, and maintaining operational stability is increasingly difficult. To achieve efficient and precise inspection of hybrid AC-DC distribution networks, this study proposes a digital intelligent inspection technique that incorporates the integration of flexible loads. A mathematical model for the hosting capacity of a hybrid AC/DC distribution network is established to quantify the maximum hosting capacity under standard operating conditions. To achieve the system’s maximum hosting capacity, flexible load integration is introduced for coordinated control of the hybrid AC/DC distribution network. The inspection status is evaluated based on the reference power derived from flexible coordinated control, and a digital data acquisition framework for the operational status of hybrid AC/DC distribution networks is designed based on a hierarchical control architecture. On a simulation platform of a 30-node hybrid AC-DC distribution network built in MATLAB/Python, along with data validation from actual industrial park distribution stations, experimental results demonstrate that the proposed inspection method requires no more than 4.1 s per node, with corresponding grid energy losses ranging from 0.72 % to 1.25 %. The method enables timely detection and response to abnormal nodes in the grid, facilitates real-time monitoring of the actual operating status of nodes, and accurately locates abnormal nodes. Reliable identification of abnormal states is achieved when voltage fluctuations exceed the ±3 % threshold, thereby reducing unnecessary losses and optimizing grid operation. The deviation between test results and actual values remains within ±2.6 kW. Inspection results reflect the actual operating conditions of each node in the grid, demonstrating favorable stability and accuracy. Hypothesis testing based on a rigorous statistical design confirms that the proposed method achieves zero missed detections at a significance level of α = 0.05 (χ 2 = 135.6, p < 0.001).
Authors: Shen Liu, Liping Zhao, Zhenyu Liu, Zhengwei Cai
Institution: State Grid Corporation of China (China)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
OpenAlex ID: W4417048174