Genetic Algorithm-Based Contingency Ranking for the 500 kV JAMALI Interconnection System

Abstract

The performance of an electric power system is strongly tied to how well it can handle disturbances. In daily operation, one of the most frequent and serious disturbances is the loss of a transmission line. When a line trips, its load must be shared by the rest of the network. Sometimes this redistribution is harmless, but in stressed conditions it can create overloads and trigger further outages. To reduce this risk, system operators rely on contingency analysis. The (N-1) criterion, which considers the effect of losing a single component, is the most common standard. However, when applied to a large network, the number of cases becomes very high, and the analysis can be time-consuming. In this work, contingency ranking using a Genetic Algorithm (GA) is studied for two systems: the IEEE 30-bus test grid and the 500 kV Java–Madura–Bali (JAMALI) interconnection in Indonesia. The GA follows the usual cycle of initialization, selection, crossover, mutation, and fitness evaluation, with the Voltage Performance Index (VPI) used to measure severity. Different parameter settings were tested. The results show that line 36 (bus 28–27) is most critical in the IEEE 30-bus system with a VPI of 56.5915, while line 35 (Bangil–Paiton) is most critical in the JAMALI system with a VPI of 95.3947. These outcomes highlight the usefulness of GA in identifying vulnerable transmission lines.