Evaluation of Transformer Protection using Artificial Neural Network
Keywords:
Artificial neural network, Differential relay, Evaluation, Matlab/simulink, Mean square error, Protection, TransformerAbstract
The study explores the use of differential relay systems and Artificial Neural Networks (ANNs) to improve transformer protection measures. The differential relay system detects faults by comparing incoming and outgoing currents, identifying discrepancies indicative of faults. The system achieved a Mean Square Error (MSE) response of 0.03, demonstrating high accuracy in fault signature detection. An ANN system enhances fault detection capabilities, utilizing a two-layer feedforward network and the Levenberg-Marquardt backpropagation algorithm. The ANN classifier classifies incoming waveforms, deciding on the presence of faults, which triggers alarms or disconnects the transformer if necessary. By integrating ANNs with differential relays, the proposed system enhances the detection of overvoltage-related faults and supports predictive maintenance strategies. This study marks a significant advancement in transformer protection methodologies, aiming to improve electrical grid reliability and stability through enhanced fault detection and predictive maintenance strategies. The findings underscore the effectiveness of this integrated approach in bolstering the resilience and operational efficiency of power transformers and the wider electrical grid infrastructure.
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