The distribution network has multi-physical field coupling characteristics

and its spatiotemporal evolution is influenced by various factors such as power grid topology

load nonlinearity

and new energy access. When disturbances such as voltage sag

harmonics

and three-phase imbalance occur simultaneously

complex interaction phenomena are difficult to decouple

making it difficult to uniformly characterize heterogeneous disturbance modes

thereby reducing the accuracy of power quality disturbance identification. To solve the above problems

a new method for adaptive identification of power quality disturbances in distribution network considering the volatility of renewable energy is proposed. The three most critical factors of renewable energy distortion coefficient

renewable energy waveform standard deviation

and statistical value characteristic waveform kurtosis are selected to describe the volatility of renewable energy. The density peak clustering (DPC) clustering algorithm based on shared neighborhood is applied to cluster the operation data of the distribution network

so as to obtain the clustering results of the three influencing factors mentioned above

thereby decoupling complex interaction phenomena and extracting heterogeneous disturbance modal features of key influencing factors. The clustering results are input into a one-dimensional adaptive convolutional neural network to identify the category of power quality disturbances in distribution network. The research results show that the DPC clustering algorithm based on shared neighborhood can accurately cluster the indicator data reflecting the volatility of renewable energy in the operation data of the distribution network. The proposed method can quickly and adaptively identify power quality disturbance in distribution network under both single disturbance and multiple disturbance conditions

with a very short response time. This research achievement provides an effective way to improve the accuracy and timeliness of identifying power quality disturbance in distribution network

which helps to ensure the stable and reliable operation of distribution network.