Power system contingency classification using machine learning technique

One of the most effective ways for estimating impact and severity line failures on static security power system is contingency analysis. The categorization approach uses overall performance index to measure system's (OPI). newton raphson (NR) load flow technique used extract network variables in a situation each transmission failure. Static categorised into five categories this paper: secure (S), critically (CS), insecure (IS), highly (HIS), (MIS). K closest neighbor machine learning strategy presented categorize these patterns. proposed classifiers are trained IEEE 30 bus before being evaluated 14, 57, 118 systems. suggested k-nearest (KNN) classifier increases accuracy assessments categorization. A fuzzy logic was also investigated implemented 14 test forecast aforementioned classifications.