This project is contemplated to realize a new technique to perform diagnostics and assessment on the multiple power quality events. The quality of electricity supplies has become a major concern of electric utilities and end-users. The newly developed and widely used electric devices, while themselves are often the sources responsible for producing variant disturbance, are becoming more and more sensitive to power quality variations. Considering cost and performance, different power quality disturbance in the power system requires solving method with differential approaches. However, for diagnosing power quality problems, the causes of the disturbances should be understood before appropriate action can be taken. Attempts to solve the power quality problem from different perspectives have been considerable. It is noted that, most of these method treated the power quality problem as a single event problem. However, the presence of multiple power quality events (such as simultaneously existing harmonics, voltage flicker, and voltage swell) is natural in many power systems and makes the diagnostics of power quality problem interesting to solve. Thereby, those methods are inconvenient to classify the multiple power quality events. Therefore, this project is aimed to develop a power quality disturbances classification system, which is capable of classifying multiple power quality disturbances in a measured waveform or a PQ event, and results in an online real-time measurement and analysis IC. This project is divided into 3 years to proceed. In the first year, the test system will be setup to measure the characteristic on power quality of electric systems under typical operating and disturbance modes. These data will be analysis by statistic method for building database for the next-two-years project. Also, this object will develop the virtual power system for the power quality analysis. The virtual power system is a time domain simulation tool, which can simulate the multiple events due to disturbances and determine the effects of a specific disturbance on specific loads. The second year project will develop a novel dynamic structural neural network algorithm for improving the disadvantage of the traditional neural networks to diagnose the power quality problems. In the meantime, this project will develop a wavelet-based algorithm for extracting the critical data from the huge measurement power quality waveforms. Finally, the two algorithms will be combined for implementing as a solution algorithm with GUI PC-based program. The third year project is to develop a power quality index meter algorithm by utilizing AI techniques to calculate the power quality related index and derive the severe grade of power quality. This project is also contemplated with the system-on-chip design to realize general-purpose portable power quality device with diagnostic and alarm function. Since system-on-chip makes the microprocessor, memory, control logic and algorithm integrally-designed in one chip, thus it is provided with the characteristics of low cost, small size, fast operation speed, etc. This report is for the first year project.