Random forest algorithm and multi-source data fusion based on the converter valve electrical characteristics of time-varying law extraction method and condition monitoring technology research

As the core equipment of high-voltage direct current transmission system, the operation status of the converter valve directly affects the safety of the power grid.In this paper, we first construct a multisource data fusion system to realize the error-free fusion of fault information parameters.Then, combined with the random forest algorithm, the time-varying law of the electrical characteristics of the converter valve based on harmonic theory is extracted.Finally, the collected time-varying laws of electrical characteristics are input into the constructed Random Forest particle swarm optimization model, and the trained model is used to monitor the status of the converter valve.In the simulation experiment, the 800kV UHV DC transmission system is built by PSCAD/EMTDC software, from which the current waveforms are collected when the converter valve fails, the time domain features of the current are extracted, and the obtained converter feature indicators are selected using the Random Forest algorithm, and 10 important features will be finally identified to construct the converter valve feature indicator set, and input into the Random Forest Particle Swarm Optimization model and the other comparative models for training and testing.The accuracy of this model is 97.5%, which is better than other comparative models.The study provides a high-precision solution for converter valve condition monitoring and effectively extends the application of multi-source data fusion in power equipment.