At present, DC transmission is developing toward high voltage and large capacity technology. Its advantages in longdistance transmission, cross-region networking and flexible dispatch are becoming more and more obvious, but at the same time, abnormal outage of DC system caused by critical equipment failure of converter station has a greater impact on power system. Therefore, it is of great significance to enhance the perception of critical DC equipment, to predict and handle the faults of critical DC equipment in advance, to reduce abnormal outage of DC system and to improve power supply reliability. Taking the multi-protocol wireless data of Ningxia State Grid converter station as an example, an improved particle swarm algorithm based on the gray wolf algorithm is proposed. The experimental results show that the particle swarm-wolf algorithm can more accurately predict the monitoring data of the converter station, reduce the prediction error, and provide a basis for the operation and maintenance of the converter station in the future.
MAO Chunxiang
,
CHAI Bin
,
LIU Ruopeng
. Research on multi-protocol wireless data analysis and prediction of converter station based on improved particle swarm algorithm[J]. Science & Technology Review, 2024
, 42(2)
: 120
-128
.
DOI: 10.3981/j.issn.1000-7857.2024.02.012
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