A scalable PQ event identification system

A scalable event identification system for power quality events is proposed . Unlike ANN-based approaches where the system is not scalable and not "deb ug-able" without retraining, the proposed approach is particularly advantag eous compared to those of ANN's since it is scalable, debug-able and easily modified. This approach is adopted from artificial intelligence's rule-bas ed approach and attempts to mimic power engineers thought process in identi fying PQ events. This paper describes prerequisites in constructing such a scalable system. Examples of rules to identify power quality event are also presented. The prototype of the system is built and tested using 770 field -measured voltage waveforms which covers ten types of PQ events. The accura cy rate is nearly 95% with less than 6% of rejection rate. Potential applic ations of the proposed system in PQ community are also described.