Dynamic Reactive Power Optimization of Low Perception Distribution Networks Based on Data-driven Approach
Due to poor communication infrastructure in the distribution network and incomplete node monitoring coverage,there are nodes that cannot collect data in real-time,resulting in the inability to perform traditional reactive power optimization.To this end,a data-driven low perception dynamic reactive power optimization method for distribution networks was proposed.Cluster node historical loads using K-means algorithm,and classify non real-time observation nodes based on features;the optimal hyper parameters were selected to complete the measurement data based on the time convolution network;finally,the improved social network search algorithm was used to achieve dynamic reactive power optimization,and the effectiveness of the method was verified through simulation.
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