On-line monitoring and diagnosis of powersystem operating conditions using artificial neural networks

An adaptive pattern recognition methodology for online monitoring and diagnosis of power system operating conditions has been developed. It is implemented on highly parallel distributed architectures of the functional-link-net (FLN) type. The flat structure of the FLN allows the tasks of unsupervised learning, supervised learning, and associative recall to be carried out without intervention in network and data structures. The proposed methodology is capable of processing large bodies of information gathered by the data acquisition system in real time. It enhances the performance of the energy management system and effectively reduces the operator's response time. The real-time monitoring and diagnosis facility can quickly detect and identify abnormal operating conditions. The main features of the system are described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>