Abstract :
Power quality disturbances can interrupt production lines, cause damage to products and equipment, result in lost orders or transactions, corrupt data communication and storage, and cause an overall decrease in productivity. At present, there are no techniques that can effectively correlate the occurrences of the power quality disturbances to the failure of the sensitive equipments. Most of the time, the causes of the equipment failures were termed as unknown or nuisance tripping. Unlike a comprehensive electrical system survey, a power quality based condition monitoring focuses on a small set of parameters that can indicate the existence of power quality disturbances and predict possible critical load failures. The condition of the power at specific dates can be used to predict possible downtime of sensitive machinery. It is important to note that voltage fluctuation, harmonic distortion, and unbalance are good indicators to indicate the existence of these power quality disturbances. These data can also indicate the condition of the load and power system, and can be recorded quickly with little incremental labor using a power quality recorder. In this paper, a new technique for performing power quality based condition monitoring is presented. The new technique involves the use of advanced signal processing and artificial intelligence techniques. A sample case study is presented to demonstrate the effectiveness of this new technique.
