Transformer-Oriented Intelligent Fault Diagnosis Method
TL;DR
An enhanced diagnostic approach integrating the Sparrow Search Algorithm with PNN is developed, which confirms that SSA can substantially strengthen the anti-disturbance capability of PNN, thereby providing reliable technical support for power transformer fault diagnosis and offering certain reference value for enhancing the operational reliability of power grid equipment.
Abstract
To address the insufficient diagnostic precision and weak anti-interference capability of the Probabilistic Neural Network (PNN) in identifying transformer faults, an enhanced diagnostic approach integrating the Sparrow Search Algorithm (SSA) with PNN is developed. The SSA is applied to search for the optimal smoothing factor of PNN, and the tuned parameter is then fed into the PNN framework for model training, thereby constructing a high-performance fault identification model. Several conventional approaches are also implemented for benchmarking purposes. Experimental outcomes reveal that the developed SSA-PNN model outperforms Particle Swarm Optimization (PSO)-PNN, Grey Wolf Optimizer (GWO)-PNN, and the standard PNN by margins of 7.1%, 10.7%, and 28.6% in overall diagnostic accuracy, respectively. Under data perturbation conditions, the accuracy degradation of the developed model remains minimal among all compared methods, which confirms that SSA can substantially strengthen the anti-disturbance capability of PNN, thereby providing reliable technical support for power transformer fault diagnosis and offering certain reference value for enhancing the operational reliability of power grid equipment.