Development of artificial-intelligent power quality diagnosis equipment for single-phase power system

A DSP process-based equipment to diagnose the power quality of a single-phase power system is developed. The artificial-intelligent equipment diagnoses the transient, the voltage sag, the voltage swell and the THD among the power quality index of a power system. The 256 data sampled in a period of the single-phase voltage of the power system are used for the real-time calculation of RMS value, Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). The type of mother function for DWT is DB4. The data are measured at 154kV or 22.9kV substations for a year. The feature vectors extracted from the data are used to train the neural network for the artificial-intelligent diagnosis of power quality. The type of the activation function in the neural network is sigmoidal. After learning with the feature vectors, the back-propagation algorithm simulated in PSIM program and C⁺⁺ code generates the weights and biases of the neural network, which are used in the DSP320C6713-based Artificial-Intelligent Power Quality Diagnosis Equipment (AIPQDE). The developed equipment detects satisfactorily the PQ problems in a real situation simulated in the laboratory.