Learning adaptive fuzzy droop of PV contribution to frequency excursion of hybrid micro-grid during parameters uncertainties

Abstract

This paper improves the performance of contribution of photovoltaic (PV) panels to the stabilization of a stand-alone hybrid microgrid during frequency excursions. Recent growth in converter-interfaced topologies and using renewable energy resources has led to decrease in inertia of isolated micro-grids (MGs) and presented some challenges to the frequency control and strength of hybrid ones. One of the effective measures to resolve mentioned problems is to deploy some potential energy resources like PV by considering a small proportion of solar panels’ generation as headroom. In addition, other distributed energy resources (DERs), which can be regarded as ancillary resources, can play an active role in the resiliency of MG’s performance in the low-inertia power systems. To this effective contribution considering some uncertainties of the microgrid parameters like the time constant of the micro-turbine, the time constant of the governor, the speed droop regulation constant as well as the load damping coefficient, an adaptive fuzzy mechanism can be proposed to determine the level of active power injection. Recurrent Adaptive Neuro Fuzzy Inference System (ANFIS) technique trains this non-linear adaptable droop to deal with uncertainties in a reasonable way. In order to ascertain parameters of membership functions in an intelligent way, this droop takes advantage of Artificial Bee Colony (ABC) algorithm based on a multi-objective. The Simulation results verify the robustness and reliability of this flexible fuzzy droop during different operating conditions as well as intermittent nature of renewable energy resources such as wind turbine generators in hybrid micro-grid.