Reactive power planning by opposition-based grey wolf optimization method

This article proposes a new meta-heuristic optimization algorithm named grey wolf optimization algorithm based on leadership and hunting behavior of grey wolf for the reactive power planning of a connected power network. The performance of the proposed opposition-based grey wolf optimization (OGWO) and grey wolf optimization (GWO) is examined and tested successfully in standard IEEE 14, IEEE 30, and IEEE 57 bus test systems for the minimization of active power loss and the total operating cost while maintaining voltage profile of the buses within permissible limit. Active power loss and total operating cost are minimized by optimal planning of the reactive generation of generators, transformer tap setting arrangements, and reactive output of shunt capacitors as installed at weak nodes under different loading conditions. The weak nodes are detected by power flow analysis. The results obtained by GWO algorithm is compared to other popular techniques recently reported in recent state-of-the-art literature. It is observed that proposed OGWO and GWO algorithm yields much better result in terms of reducing operating cost and minimizing active power loss. Merit lies with GWO is that its simple structure for implementation and its ability not to be trapped in local minima, thus exploring wider search area.