Voltage Stability Indices with Adaptive Interval Type-2 Fuzzy Logic Controller for Voltage Collapse Prediction in an ILL-Conditioned Power System

Abstract Voltage instability remains a persistent challenge in modern power systems, especially in ill-conditioned and heavily loaded networks such as Nigeria’s 72-bus 330 kV transmission grid. This study presents a hybrid predictive framework combining conventional Voltage Stability Indexes (VSIs) with an Adaptive Interval Type-2 Fuzzy Logic Controller (AIT2FLC) to enhance voltage resilience and prevent system collapse. Traditional indexes—MVSI, Lmn, and VCPI—were applied to identify voltage vulnerabilities. Initial findings revealed weak buses with voltages as low as 0.87 p.u. and stability indexes below 0.80, indicating high collapse risk. After applying AIT2FLC, all critical metrics improved markedly. Weak buses showed an average voltage increase of 8.2%, with some buses like Bus 29 improving from 0.87 p.u. to 0.98 p.u. Critical buses recorded a 6.5% average increase, with Bus 15 rising from 0.91 p.u. to 1.02 p.u. Overall, the average voltage across weak buses improved from 0.93 p.u. to 1.01 p.u. and from 0.94 p.u. to 1.02 p.u. for critical buses. Furthermore, the MVSI average dropped from 0.52 to 0.41, and the Lmn index improved from 0.45 to 0.38, indicating reduced system stress. Voltage deviation across the system fell by 63%, and the system stability score improved by 23%, from 0.72 to 0.85. The percentage of buses within the ideal voltage range (0.95–1.05 p.u.) increased from 74% to 96%. These results underscore the effectiveness of AIT2FLC in real-time control and its potential as a policy tool for preventive grid management. Adoption of such intelligent control frameworks by grid operators can drastically reduce the risk of voltage collapse and ensure a more stable, adaptive, and resilient power system.