A new opposition crow search optimizer-based two-step approach for controlled intentional islanding in microgrids

Crow search optimizer is considered as the latest meta-heuristic algorithm that is influenced by crow’s behavior. The proposed oppositional crow search optimizer (OCSO) is intended here, for solving the intentional islanding problem. This paper has proposed a novel two-step method by considering the most significant factors such as the constraints of line capacity, bus voltage, load priority, load controllability, problem occurred due to spacing of solutions as well as the capability of integrating the islands to produce higher intentional islands. Initially, the tree knapsack problem is considered as an intentional islanding issue and therefore the OCSO algorithm is employed in solving such shortcomings. In OCSO approach, the opposition-based generation jumping and population initialization concept are used in crow search optimizer for improving the convergence profile and computational speed. In next process, island feasibility is verified by means of conducting power flow computation and providing significant modifications. Six distributed generations containing IEEE 69-bus test system are employed in validating experimentally the efficiency of the proposed approach, and comparison was done for the obtained results with the other existing approaches. The comparative analysis is evaluated to enhance the level of reliability, particularly critical load.