A Line Loss Management Method Based on Improved Random Forest Algorithm in Distributed Generation System
DOI:
https://doi.org/10.13052/dgaej2156-3306.3711Keywords:
Distributed power generation, 35 kV, line loss, random forest algorithm.Abstract
The distributed power generation in Gansu Province is dominated by wind
power and photovoltaic power. Most of these distributed power plants are
located in underdeveloped areas. Due to the weak local consumption capacity,
the distributed electricity is mainly sent and consumed outside. A key indi-
cator that affects ultra-long-distance power transmission is line loss. This is
an important indicator of the economic operation of the power system, and it
also comprehensively reflects the planning, design, production and operation
level of power companies. However, most of the current research on line loss
is focused on ultra-high voltage (=110 KV), and there is less involved in
distributed power generation lines below 110 KV. In this study, 35 kV and 110
kV lines are taken as examples, combined with existing weather, equipment,
operation, power outages and other data, we summarize and integrate an
analysis table of line loss impact factors. Secondly, from the perspective of
feature relevance and feature importance, we analyze the factors that affect
line loss, and obtain data with higher feature relevance and feature importance
ranking. In the experiment, these two factors are determined as the final
line loss influence factor. Then, based on the conclusion of the line loss
influencing factor, the optimized random forest regression algorithm is used
to construct the line loss prediction model. The prediction verification results show that the training set error is 0.021 and the test set error is 0.026. The
prediction error of the training set and test set is only 0.005. The experimental
results show that the optimized random forest algorithm can indeed analyze
the line loss of 35 kV and 110 kV lines well, and can also explain the
performance of 110-EaR1120 reasonably.
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