Abstract
The safety and stability of the power supply system are affected by some faults that often occur in power system. To solve this problem, a criterion algorithm based on the chaotic neural network (CNN) and a fault detection algorithm based on discrete wavelet transform (DWT) are proposed in this paper. MATLAB/Simulink is used to establish the system model to output fault signals and travelling wave signals. Db4 wavelet decomposes the travelling wave signals into detail signals and approximate signals, and these signals are combined with the two-terminal travelling wave location method to achieve fault location. And the wavelet detail coefficients are extracted to input to the proposed chaotic neural network. The results show that the criterion algorithm can effectively determine whether there are faults in the power system, the fault detection algorithm has the capabilities of locating the system faults accurately, and both algorithms are not affected by fault type, fault location, fault initial angle, and transition resistance.
Highlights
Wavelet TransformWavelet transform is the process of convolving the signal with the wavelet basis function, and the signal is decomposed into different frequency bands and time periods [8]
E neural network simulates the neuron network of the human brain, and the values of the neurons in the input layer are mapped to the output layer, so that a certain implicit function relationship is established between the input and the output
The neural network has a strong learning ability, and its fault tolerance is better in fault diagnosis, but there are still some defects in the neural network: (1) Neural network needs a large number of samples for training, and it is difficult to obtain sufficient samples in the field of power system fault diagnosis. (2) e neural network was easy to be trapped in local minimum
Summary
Wavelet transform is the process of convolving the signal with the wavelet basis function, and the signal is decomposed into different frequency bands and time periods [8]. E signal f(t) is expanded under the telescopic-translational signal ψa,b(t), and the process of decomposing f(t) is called continuous wavelet transform (CWT), and the expression is. In the above formula, ψa,b(t) is called the mother wavelet, a is the scaling parameter, and b is the translation parameter. E essence of wavelet transform is to filter the signal with different filters. Since discrete signals are often used in the actual project, ψa,b(t) is discretized into the following formula: ψj,k(t) a−0(j/2)ψ⎛⎝t −. En, get the discrete wavelet transform (DWT): Cj,k 〈f, ψj,k〉 a−0(j/2) f(t)ψa−0jt − kb0dt. The signal f(t) can completely be characterized by discrete wavelet coefficient Cj,k
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