Research on Anomaly Detection Method of Power Generation Data Based on Isolation Forest

In the realm of power systems, anomaly detection techniques have proven instrumental, particularly in scrutinizing power generation datasets. Building upon this foundation, an innovative anomaly detection algorithm known as BPSO-iForest has been formulated. This novel approach enhances the conventional Isolated Forest algorithm by integrating Binary Particle Swarm Optimization (BPSO) to both refine its precision and augment execution speed. The BPSO-iForest algorithm addresses dual challenges inherent in traditional isolation forests: the algorithmic efficiency downturn and the potential detrimental impact of less discerning trees on the overall detection outcome.attest to the superior clustering capabilities and enhanced anomaly detectionproficiency of the proposed BPSO-iForest algorithm. Its efficacy transcends mere theoretical promise, demonstrating tangible benefits when deployed for the surveillance of abnormal power generation patterns within the energy sector. This cuttingedge methodology thus ensures a robust line of defense against anomalies in power generation data, fortifying the reliability and resilience of power grid operations.