Enhanced cohesion hierarchical clustering-based distribution network user load feature classifying method

Abstract

The invention relates to an enhanced cohesion hierarchical clustering-based distribution network user load feature classifying method, which is characterized by comprising steps: calculating a daily load curve characteristic quantity according to an active power curve and a reactive power curve of users; obtaining a daily load characteristic quantity set (see the specification) of N users, an enhanced damping coefficient gamma and a similar coefficient matrix P(X) among all points; forming all groups of merging routes into a merging route set Sg(s), and calculating a hierarchical clustering cohesion process by using a value iteration algorithm; obtaining a group of routes with a minimal similar coefficient value weight sum in the merging route set Sg(s). By using the clustering enhanced cohesion hierarchical clustering algorithm for the characteristic quantities, return values of all results of each layer of clustering are calculated, the cohesion merging route with the maximal return value is selected, the accuracy of the clustering algorithm is improved, defects such as a sensitive initial value, occurrence of a continuous error, and integral deviation of the result of the hierarchical clustering are avoided, and certain measures are adopted to prevent the influence of a singular value on the results.

Description

technical field [0001] The invention relates to a method for classifying load characteristics of distribution network users, in particular to a method for classifying load characteristics of distribution network users based on enhanced agglomerative hierarchical clustering. Background technique [0002] With the improvement of the power market and the improvement of power service awareness, the scientific classification of distribution network user load characteristics and the summary of user typical load curves are conducive to grasping the distribution network load operation laws in the entire region, and rationally arranging distribution network dispatching. Improve the scientific effectiveness of load forecasting, demand side management, time-of-use electricity pricing, peak shaving and valley filling. Create conditions for the safe and reliable operation of the distribution network and improve economic and social benefits. [0003] Distribution network user load habits...

AI Technical Summary

Problems solved by technology

The current classification of power users is based on the type of electricity provided by the user to the power supply department when the user submits the installation. The classification according to the annual average load is not precise enough and does not reflect its time-series variation characteristics, which cannot provide a scientific analysis basis for the distribution network.

The distribution network structure is complex and changeable, and the distribution points of users are wide and wide. The noise of real-time load changes is very random, and it is difficult to summarize the typical statistics of massive measurement data by manual statistics.

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