Open AccessArtificial Neural Network Controller Strategy for Improving DC Link Voltage of Grid Connected Hybrid PV-Wind Generation Systems
Author(s) -
B. Maheswara Rao,
G. V. Nagesh Kumar,
Vempalle Rafi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2070/1/012133
Subject(s) - maximum power point tracking , photovoltaic system , controller (irrigation) , control theory (sociology) , computer science , wind power , maximum power principle , matlab , grid , power factor , power optimizer , voltage , power (physics) , engineering , electrical engineering , control (management) , mathematics , inverter , agronomy , physics , geometry , artificial intelligence , quantum mechanics , biology , operating system
This paper presents a power system, consisting of photovoltaic (PV) station and wind farm integrated by ac bus, connected to the grid. The load gets power from both the sources and maximum power is tracked by maximum power point techniques (MPPT) during any changes in the environment. The paper explores how MPPT techniques help power system in tracking power from PV and wind in the conditions of different solar irradiances and different wind speeds. This paper’s objective is to show the improvement in step response of dc link voltage by artificial neural network (ANN) controller. The control method significantly maintains constant grid voltage ensuring unity power factor even during climatic conditions variation. The whole system is simulated using matlab/simulink software and the results compare the proposed system with existing controller i.e., Proportional Integral (PI). The results show the efficient performance of ANN controller than PI controller.