Design of power theft detection method for distribution lines based on enhanced fuzzy clustering analysis model

A reinforcement sliding mode clustering analysis algorithm based on deep learning algorithm is designed to address the problems of insufficient voltage stability of distribution line nodes and decreased robustness of power systems caused by human electricity theft and other phenomena in distribution lines. Based on the equivalent topology model of the distribution line, establish node power equations based on active and reactive power, and introduce fuzzy clustering analysis algorithm to perform fuzzy analysis on the active and reactive power of the distribution line separately, so as to timely grasp the node power situation of the distribution line. Furthermore, by introducing deep learning algorithms to adaptively tune the fuzzy rules in sliding mode clustering analysis, a strengthened fuzzy clustering analysis algorithm is formed, which enables the system to adjust the fuzzy rules in a timely manner in the event of human electricity theft and other phenomena, accurately locate the electricity theft nodes, and lay a control foundation for the stability of node voltage. Finally, to verify the correctness and effectiveness of the proposed enhanced fuzzy clustering analysis algorithm, a 33 node power system was built on the MATLAB/Simulink digital experimental platform, and simulation comparisons were conducted for the proposed algorithm and other algorithms under different operating conditions. The results show that the algorithm proposed in this article has an accuracy of 93.6% in locating electricity theft phenomena, and the fluctuation of node voltage is only below 3%. This not only lays the foundation for the stability of node voltage, but also provides important guarantees for the stability of the power system.