AN ADAPTIVE HYBRID OPTIMIZATION ALGORITHM FOR MULTI-OBJECTIVE SECURITY CONSTRAINED OPF WITH FACTS DEVICE release_rev_818ef2a8-100b-4d1a-9b03-53a12ebf69a9

Abstract

This paper presents a Hybrid Particle Swarm Optimization with Differential Perturbed Velocity with adaptive acceleration coefficient (APSO-DV) to examine the security constrained Multi-objective Optimal Power Flow (OPF) control with a powerful Flexible Alternating Current Transmission Systems (FACTS) device such as Unified Power Flow Controller (UPFC) under normal and network contingencies. Firstly, contingency analysis and ranking is done by taking voltage magnitudes, voltage stability index (L-Index) and Fast Voltage Stability Index(FVSI) along with Line Loadings as input parameters to the fuzzy system where L-Index and FVSI are real numbers which gives fair and consistent results for stability analysis among different methods of voltage stability analysis. Secondly, the strategic location of UPFC and the optimal control settings of UPFC are found using APSO-DV under severe contingencies along with OPF constraints. The fuzzy based System Overall Severity Index (SOSI) and the combination of fuzzy based SOSI along with fuel cost were used as an objective to be minimized to improve the security of the power system. The feasibility of the proposed method has been tested on IEEE-30 bus system with two different objective functions. The test results show the effectiveness of robustness of the proposed approach and provides superior results compared with the existing results in the literature.
In text/plain format