Research on Abnormal Line Loss Diagnosis and Loss Reduction Optimization in Distribution Transformer Areas under the Background of New Power Systems
Accurate line-loss diagnosis and loss-reduction control are essential for low-voltage distribution transformer areas under high penetration of distributed energy resources (DERs). With electric vehicles (EVs), rooftop photovoltaic (PV) generation, and energy storage systems (ESS) connected at the customer side, traditional line-loss patterns are reshaped by bidirectional power flow, evening-concentrated charging behavior, voltage rise, and local flexibility. To address both abnormal-loss identification and operational mitigation, this paper proposes an integrated framework for transformer-area line-loss governance. First, the physical and operational factors affecting technical and non-technical line losses are analyzed, and simulation-supported sensitivity intervals are linked to DER penetration, power factor, load rate, and feeder impedance. Second, a two-stage anomaly diagnosis scheme is constructed, in which Isolation Forest screens suspicious samples from a 20-dimensional feature matrix and XGBoost classifies the detected abnormal samples into electricity theft, equipment failure, metering error, and topology error. Third, a collaborative loss-reduction strategy coordinates ESS dispatch, ordered EV charging, and PV hosting-capacity screening through time-series MATPOWER AC power-flow calculation. Case studies on a 500-household transformer area and the IEEE 69-bus radial feeder implemented in MATPOWER show that the classification stage achieves 99.02% accuracy and a macro-F1 score of 0.9917. The coordinated strategy selected by enumerative search reduces daily line loss by 25.3%-28.6%, with an average reduction of 27.1%, while reducing the maximum voltage deviation from 6.8% to 2.9%. The results demonstrate that combining data-driven diagnosis with DER-aware operational optimization can provide practical support for high-loss station governance in new power systems.