Power quality monitoring using neural networks

With the proliferation of sensitive control systems and personal computers in the commercial and industrial sector, comes a need for electrical utilities to deliver `clean´ power. Voltage variations in the form of sags, surges and impulses, i.e., disturbances, can chronically plague and permanently damage electrical equipment. Southern California Edison (SCE) in joint effort with Basic Measuring Instruments (BMI) were teamed up to automate the process of collecting disturbance data, viewing their contents and applying artificial intelligence paradigms (neural networks) to help identify their causes and present possible solutions