Study of nature inspired computing (NIC) technique for optimal reactive power dispatch problems - Sulaiman, Mohd Herwan (2017)
In this research, new nature-inspired meta-heuristic optimization algorithms namely moth-flame optimizer (MFO) and Ant Lion Optimizer (ALO) were implemented to address the optimal reactive power dispatch (ORPD) problems. MFO is developed based upon natural navigation technique of moths. This algorithm mimics the navigation characteristics of moths in order to travel according to the fittest position. The moths travel based upon the visible light sources as guidance during night time. ALO on the other hand is inspired by the foraging mechanism of antlions in catching preys. ALO is developed based upon five main stages: random walk of ants, entrapment of ants, building traps, catching preys and rebuilding traps. This research presents the realization of MFO and ALO in solving ORPD problems which is to investigate the optimal setting of control variables including generators voltage, transformers tap ratio and reactive compensators sizing in order to minimize transmission power loss and voltage deviation. ORPD problem is a nonlinear optimization problem that involving both equality constraints and inequality constraints. The proposed algorithms are tested on five different case studies which are IEEE 30-bus system with 13 control variables, IEEE 30-bus system with 19 control variables, IEEE 30-bus system with 25 control variables, IEEE 57-bus system with 25 control variables and IEEE 118-bus system with 77 control variables. The results from each case study were compared with the best results of other optimization algorithms that reported in the recent literatures in order to test the effectiveness of proposed MFO. The statistical simulation results of this project proved that MFO is able to produce compromising solutions by yielding the lowest power loss and voltage deviation among other reviewed algorithms. It can reduce 19.01 % (IEEE 30-bus system with 13 control variables), 20.47 % (IEEE 30-bus system with 19 control variables), 50.76 % (IEEE 30-bus system with 25 control variables), 12.96 % (IEEE 57-bus system) and 12.37 % (IEEE 118-bus system) of power losses from base case losses of each test case.