Power quality has attracted considerable attentions from utilities and customers due to the popular uses of the sensitive electronic equipment. Harmonics, voltage swell, voltage sag, and power interruption could downgrade the service quality. Harmonic currents injected by non-linear loads throughout the network could degrade the quality of services to sensitive high-tech customers such as the science park of Xin-Zhu and Tai-Nan in Taiwan. In recent years, massive rapid transit system (MRT) and high speed railway (HSR) have been rapidly developed, with the applications of wide-spread semi-conductor technologies in the auto-traction system. Swell and sag could occur from thundering, capacitor switching, motor starting, nearby circuit faults, or artificial calamity, and could also attribute to the power interruption. To ensure the power quality, harmonic and voltage disturbances detection becomes important. Fourier transformation is used to analyze distorted waves in the frequency domain, with low-pass filter used to eliminate the fundamental component, and then characteristic harmonics can be detected. The complicated process is difficult to operate in real-time. The method-based processing model with physical harmonic data is needed to simplify the processing architecture. The thesis proposes to use wavelet transformation (WT) and probabilistic neural network (PNN) for power quality and characteristic harmonics detection. Wavelet-probabilistic network (WPN) is first used to extract distorted waves. PNN based processing model will then analyze the harmonic components. Computer simulation shows a simplified model to shorten the processing time in this study.

The paper proposes a model of detection for voltages and harmonics using wavelet-probabilistic network (WPN). WPN is a two-layer structure, containing the wavelet layer and probabilistic network. It uses the wavelet transformation (WT) and probabilistic neural network (PNN) to analyze distorted waves and classify tasks. In this thesis, the field programmable gate array (FPGA) is employed for the hardware realization of WPN. In the implementation process, by the use of the hardware description language, the WPN algorithm has been embedded into the FPGA chip. Firstly, we divide the mathematical formula of basic WPN algorithm into several parts in order to set up each module individually, then we integrate all modules to complete the design of basic WPN algorithm with digital circuits by the bottom-up process.

The technology of power electronics is used increasingly during recent years, and the electronic power facilities are used more and more in the power system. The non-linear electronic loads produce heavy harmonic currents and could significantly degrade the power quality. Nonlinear loads, including the un-interruptible power supply, motor control and converter, etc, are important equipment in a modern factory, however, these nonlinear loads could lead to power facility malfunction and capacitor damage. The harmonics would eventually cause severe unexpected capital loss. Identification of harmonic sources location becomes an important study for power quality. An effective tool is thus helpful for the harmonic source locating. This paper proposes a method to deal with the harmonic sources and location detection in the power system by using the artificial neural network (ANN). The non-linear loading characteristics are studied by the power flow analysis, and then the proposed methodology uses the Probabilistic Neural Networks¡]PNN¡^and wavelet-probabilistic network (WPN) for harmonic source locating. An IEEE 14-bus power system is used for study to show the effectiveness of the proposed approach.