TY  - JOUR
Y1  - 2022///
VL  - 822
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125278497&doi=10.1007%2f978-981-16-7664-2_35&partnerID=40&md5=267b4e1f741f11b9794919b8f11c9798
JF  - Lecture Notes in Electrical Engineering
A1  - Karthik, R.
A1  - Harsh, H.
A1  - Pavan Kumar, Y.V.
A1  - John Pradeep, D.
A1  - Pradeep Reddy, C.
A1  - Kannan, R.
KW  - Controllers; MATLAB; Power control; Wind; Wind power; Wind turbines
KW  -  Energy generations; Energy systems; Maximum power; Maximum Power Point Tracking; Network-based; Network-based controllers; Neural-networks; Power; Renewable energy source; Tracking controller
KW  -  Maximum power point trackers
ID  - scholars17747
N2  - Wind energy is one of the best renewable energy sources, used for energy generation in modern-day power generation system. Nowadays, wind energy is widely used to power up devices that consume huge power. As wind speed changes rapidly over time, its power generating capacity also varies, this gives rise to a need for a controller which controls the power harnessed from the wind energy system. The procedure to achieve maximum power from a renewable energy system is known as maximum power point tracking (MPPT). There are many methods to achieve maximum power from the wind turbine, and in this paper, a neural network-based controller for MPPT is proposed. Firstly, the mathematical model of a wind power turbine system is presented, followed by designing a neural network-based controller to achieve maximum power profile. The influence of the proposed controller on power point tracking is investigated, and the time domain parameters are presented. In this paper, MATLAB/Simulink software is used for the simulating the system and to verify the controller efficacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
SN  - 18761100
PB  - Springer Science and Business Media Deutschland GmbH
EP  - 439
AV  - none
TI  - Modelling of Neural Network-based MPPT Controller for Wind Turbine Energy System
SP  - 429
N1  - cited By 2; Conference of International Conference on Smart Grid Energy Systems and Control, SGESC 2021 ; Conference Date: 19 March 2021 Through 21 March 2021; Conference Code:271959
ER  -