Bacterial Foraging Optimization Algorithm Trained ANN Based Differential Protection Scheme for Power Transformers
To avoid the malfunction of the differential relay, alternate improved protection techniques are to be formulated with improved accuracy and high operating speed. In this paper an entirely new approach for detection and discrimination of different operating and fault conditions of power transformers is proposed. In the proposed scheme Artificial Neural Network (ANN) techniques have been applied to power transformer protection to distinguish internal faults from normal operation, magnetizing inrush currents and external faults. Conventionally Levenberg-Marquardt learning rule based back propagation (BP) algorithm is used for optimizing the weights and bias values of the neural network. In this paper bacterial foraging algorithm (BFA), based on the self adaptability of individuals in the group searching activities is used for adjusting the weights and bias values in BP algorithm instead of Levenberg-Marquardt learning rule.
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