Application of minimal radial basis function neural network to distance protection. - Dash, P.K.
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 trial and error. The input data to this network comprises fundamental peak values of relaying point voltage and current signals, the zero-sequence component of current and system operating frequency. These input variables are obtained by a Kalman filtering approach. Further, the parameters of the network are adjusted using a variant of extended Kalman filter known as locally iterated Kalman filter to produce better accuracy in the output for harmonics, DC offset and noise in the input data. The number of training patterns and the training time are drastically reduced and significant accuracy is achieved in different types of fault classification and location in transmission lines using computer simulated tests
Keywords: 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