Photovoltaic(PV)systems are being increasingly implemented in the grid

and their intermittent output fluctuations threaten the stability of the grid

thereby requiring effective power ramp control(PRRC)strategies.In this study

we proposed a power fluctuation identification method to optimize the PRRC strategy.The K-means++ cluster based on DTW used in this method

which clusters the historical PV power generation data into power curves corresponding to a specific weather type(sunny

cloudy

and rainy)in a time zone.Subsequently

wavelet decomposition is applied to discretize the power curves with extreme RR overrun to accurately identify the extreme fluctuation time zones.We conducted an analysis using minute-level data from a 100 kW PV plant in Arizona

which demonstrates that the proposed method can effectively identify high-risk periods.Weather patterns within the time zones were quantitatively identified using a weather probability model.A hardware-in-the-loop experimental platform was employed to validate two days of actual power data in Arizona

demonstrating the weather zoning accuracy of the method and the reasonableness of the control.The proposed methodology contributes significantly to PRRC strategy selection and parameter optimization(e.g.

ESS capacity storage allocation and APC power reserve ΔP) in different time zones and weather conditions.