Optimal fractional‐order adaptive neuro fuzzy inference system‐based droop controller for DFIG‐wind turbine to control load frequency of hybrid microgrid

This paper aims to ameliorate the contribution capability of doubly fed induction generator to damp the frequency and output power deviation of stand‐alone hybrid microgrid system. Skyrocketing progress in converter‐interfaced renewable energy sources leads to weakness of islanded microgrids' inertia which imposes some problems in the frequency control and hybrid system stability. This problem can be effectively solved by utilizing renewable resources with high harvestable energy such as doubly fed induction generator‐wind turbine. Furthermore, the alternative energy resources have been taken into account to participate in strength of weak inertia of the microgrid. Since the conventional controller like proportional integral derivative droop controller cannot present efficient damping term because of their slow controlling action in order to diminish the frequency and output power deviation. In this regard, a fractional‐order adaptive neuro fuzzy inference system droop is here introduced to stabilize the frequency within a satisfactory secure range with low fluctuation over the conventional droop controllers. To attain an accurate control action, multi‐objective sine cosine algorithm is used to optimize the parameters of based on multi‐criterion objective functions. To verify the performance of proposed droop controller, it has been tested under different scenarios. At last, the simulation results reveal that sine cosine algorithm‐based proposed droop controller can significantly enhance the microgrids' inertia and diminish the frequency and output power deviation.