Application of minimal radial basis function neural network to distance protection.

Application of minimal radial basis function neural network to distance protection.

The paper presents a new approach for the protection of power transmission lines using a minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to tri...

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Dettagli Bibliografici
Pubblicato in:IEEE Transactions on power delivery 16, 1 (2001).
Autore principale: Dash, P.K
Natura: Articolo
Lingua:English
Soggetti:
DC offset.
Kalman filtering approach.
Computer simulated tests.
Distance protection.
Extended Kalman filter.
Fault classification.
Fault location.
Input data noise.
Input variables.
Locally iterated Kalman filter.
Minimal radial basis function neural network.
Point current signals relaying.
Point voltage signals relaying.
Power transmission lines.
Sequential learning procedure.
System operating frequency.
Training patterns.
Training time.
Transmission lines.
Zero-sequence component.