Adaptive Neuro-Fuzzy Damping Controller of Grid-Connected Microgrid Hybrid System Integrating Wind Farms and Batteries

Abstract

Renewable energies equipped with LIRs as wind turbines and photovoltaic arrays provide negative impacts on power system dynamic securities. For this issue, developing adaptive controlling strategies play important role on controlling the system dynamic oscillations. In this paper, based on HVDC link, an ANFDC damping controller is proposed for controlling the system dynamic behavior through different LIRs. To do this, a fuzzy linguistic role proposed for tuning ANFDC parameters which the input dynamic signals are transferred into linguistic variables through offline working mode. Then, considering microgrid consists of different LIRs as offshore and onshore WTs, PVs and SSSGs integrated with together, ANFDC is trained which then in online working mode, the system damping performances through different operational and technological structures are evaluated. The proposed scheme is an online and non-model-based controller which uses the advantages of both neural and fuzzy logics together for providing a fast and secure structure of damping controller through online evaluations. The merit and effectiveness of the proposed approach investigated on a grid-connected microgrid consisting of PV, WT and SSSG connected in an incorporated HVDC link which considering different short circuit faults, damping performances are evaluated. Results indicate effectiveness of the proposed scheme for damping dynamic oscillations of LIRs with high damping ratios subject against severe fault events.

Keywords

• Low inertia resources (LIRs)
• high voltage direct current (HVDC)
• microgrid
• wind turbines (WT)
• photovoltaic arrays (PV)
• small-scale synchronous generators (SSSGs)