Optimal SVC allocation for enhancing modern power network stability using a max–min aggregation based multi-criteria hybrid CF-PSO FAHP algorithm considering N-1 contingencies

• A hybrid Constriction Factor-PSO Fuzzy AHP algorithm is proposed for optimal allocation of SVC in power systems. • The optimization problem is conducted via four objective functions. • The four objective functions are combined into one overall objective function using max–min weighted sum approach. • The proposed scheme is tested on small and medium-scale power systems. • Optimal SVC allocation is determined under normal and N-1 contingency scenarios. As energy demand and resource depletion increase, managing power generation becomes more complex. It now requires a fast and robust coordinated control technique. Modern power systems can withstand strained situations with less difficulty because of the rapid development of intelligent algorithms. In this paper a novel approach for optimal SVC location is proposed using a multi-criteria hybrid CF-PSO Fuzzy AHP method considering the degree of centrality of buses. The best compromise solution is obtained through a max–min weighted sum approach for enhancing the voltage magnitude profile, reducing power losses, maximizing the load-ability as well as reducing the line stability index under normal and N-1 contingency situations. As a contribution in this study, two novel objective functions are integrated, the normalized degree of centrality of buses and the line stability index termed modern stability assessment index (MSAI). The effectiveness and robustness of the proposed algorithm are assessed and validated on IEEE-9 and −30 bus test systems. The practical implementation of the method is performed on the Cameroon Southern Interconnected Grid (SIG). The results obtained through different operating scenarios implemented in Matlab with its toolboxes Matpower and PSAT accurately designed bus 9 and 7 as potential SVC location in the IEEE-14 bus test system. For the IEEE-30 bus system, bus 10 and 3 are presented as most suitable optimal solution. In the case of the SIG, the buses 11 and 44 are the most optimal solutions. The optimal allocations in the different systems are justified based on a comparative analysis with respect to other SVC locations in the respective systems. Furthermore, the results expounded on the effectiveness of the proposed approach are compared with other optimization methods applied on IEEE-9 and −30 bus test systems.