In the field of photovoltaic

how to cluster high-dimensional time series data other than light intensity and whether time series clustering has clear physical significance have not been studied.Time series clustering is a new technique to extract valuable information from complex and massive time series data sets.The photovoltaic power station operates under different working conditions

and time series data such as current

voltage

temperature and irradiance are statistics.Then

the time series data are clustered through the timing DBSCAN algorithm.The experimental results show that there is a physical correlation between the unknown conditions and the clustering results.The discovery of the physical meaning between the unknown working conditions and time series clusters will promote the development of intelligent technologies for monitoring

forecasting and fault diagnosis of photovoltaic power generation systems.