Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm

The performance of voltage stability indices in the multiobjective optimal power flow modern systems is presented this work. Six indices: Voltage Collapse Proximity Index (VCPI), Line Stability (LVSI), (Lmn), Fast (FVSI), Factor (LQP), and Novel (NLSI) were considered as case studies on a modified IEEE 30-bus consisting thermal, wind, solar hybrid wind-hydro generators. A evaluation using mayfly algorithm (MOMA) was performed two operational scenarios: normal contingency conditions, MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique used to determine best compromise solutions for each Pareto front. To evaluate computational efficiency studies, preference selection index used. results indicate that VCPI NLSI yielded best-optimized system minimizing generation costs, transmission loss reduction, simulation time conditions. best-case also promoted most scheduled reactive from renewable energy sources (RES). On average, contributed highest penetration level RES (13.40%), while Lmn had lowest. Overall, provided worst average both operating scenarios, respectively. Also, MOMA demonstrated superior against harris hawks (MHHO), Jaya (MOJAYA), particle swarm (MOPSO), nondominated sorting genetic III (NSGA-III) algorithms. In all, proposed approach yields lowest cost compared other methods.