Optimal power flow using clustered adaptive teaching learning-based optimisation

In this paper, optimal power flow (OPF) with non-convex and non-smooth generator cost characteristics is presented using clustered adaptive teaching learning-based optimisation (CATLBO) algorithm. The proposed OPF formulation includes active and reactive power constraints; prohibited zones, and valve point loading (VPL) effects of generators. In the problem formulation, transformer tap settings and reactive power compensating devices settings are also considered as control variables. OPF is a complicated optimisation problem, hence there is a need to solve this problem with an accurate algorithm. In the proposed CATLBO algorithm, the class is divided into different sections, and allot different teacher to every section depending on the performance of that particular section. This sectioning of the class makes the proposed technique more robust and less prone to trapping in local optima. The OPF solution is obtained by considering generator fuel cost, transmission loss and voltage stability index as objective functions. The effectiveness of proposed CATLBO algorithm is validated on IEEE 30 bus test system, and the simulation results obtained with CATLBO algorithm are compared with other optimisation techniques presented in the literature.