RESEARCH ON ADAPTIVE DETECTION METHOD FOR DISTRIBUTED POWER ACCESS UNITS INTEGRATING CLUSTERING ALGORITHM AND POWER FLOW SIMULATION

The growing penetration of distributed generation units, which are often connected to power distribution networks, poses severe challenges for the proper operation and control of the system, and demands automatic detection methods that can dynamically adjust in varying work condition.A new adaptive detection approach that integrates the synergistic bond between the clustering tools and the power flow simulation is presented, which can efficiently detect and locate the distributed power access devices in real-time.The approach utilizes feedback between cluster information and power flow which in turn improves convergence, as well as a validation of physicsbased pattern recognition results.A reflexive controller keeps track of system performance in real time, and updates algorithm parameters adaptively via reinforcement learning, and the evolution works for all network conditions.A thorough experimental validation on IEEE benchmark systems and real-world data from a California utility network and also human-in-the-loop validation illustrates its high performance (96.8% detection accuracy) yet sub-200ms processing times that are conducive to real-time deployment.The method demonstrates superior robustness against measurement noise, communication loss, cyber-attacks and can still maintain the accuracy of 90% even at Signal to Noise Ratio (SNR) to 25 dB.The adaptive framework not only reduces false positive rates by 63% and increases detection accuracy by 14.4% under high distributed generation penetration but also outperforms traditional threshold-based detection and static machine learning algorithms.This work represents an advancement of smart power grids that can effectively incorporate renewable energy resources, and co-serve the grid reliability and resilience.