Artificial Neural Network Based Droop-Control Technique for Accurate Power Sharing in an Islanded Microgrid. Issue 5 (2nd September 2016)

• Record Type: Journal Article
• Authors: Vigneysh, T.; Kumarappan, N.
• Is Part Of: International journal of computational intelligence systems. Volume 9:Issue 5(2016)
• Page Start: 827
• Page End: 838
• Publication Date: 2016-09-02
• Subjects: Artificial neural network (ANN) -- Microgrid -- Photovoltaic (PV) -- Battery energy storage system (BESS) -- Solid oxide fuel cell (SOFC) -- Droop control; Computational intelligence -- Periodicals; 006.305
• DOI: 10.1080/18756891.2016.1237183
• Languages: English
• ISSNs: 1875-6891

Abstract

Abstract: In an islanded microgrid, while considering the complex nature of line impedance, the generalized droop control fails to share the actual real/reactive power between the distributed generation (DG) units. To overcome this power sharing issue, in this paper a new approach based on feed forward neural network (FFNN) is proposed. Also, the proposed FFNN based droop control method simultaneously controls the microgrid voltage and frequency within the limits. The proposed microgrid consists of combination of photovoltaic (PV) system and battery energy storage system (BESS) as the first DG unit and solid oxide fuel cell (SOFC) as the second DG unit. The simulation of the proposed microgrid is carried out in Matlab/Simulink environment and necessary results are compared to show the effectiveness of the proposed method.