Fault-Type Identification in New-Type Power Systems Using Online Dictionary Learning and an STP–Attention Spiking Neural Network

With the large-scale integration of renewable energy resources into power grids, fault behaviors in new power systems have become more frequent and more complex. To address the difficulty of fault feature extraction under limited measurement configurations, this paper proposes a fault type identification method that combines online dictionary learning (ODL), compressed sensing, and an improved spiking neural network (SNN). First, the mechanisms of typical short-circuit and branch open-circuit faults are analyzed to provide a physical basis for feature construction. Then, an adaptive overcomplete dictionary is built through ODL to obtain sparse representations of fault signals. Finally, short-term plasticity (STP) and an attention mechanism are introduced into the SNN to enhance temporal adaptation and feature selectivity. Simulations on the IEEE 33-bus system evaluate bus- and line-fault classification under different fault locations and simulated noise levels. In the baseline simulations, the overall accuracies were 88.93% for bus faults and 82.48% for line faults, with corresponding macro-F1 scores of 88.83% and 82.38%.