Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller

Authors

  • K. Naresh Jawaharlal Nehru Technological University Anantapur
  • P. Umapathi Reddy Sree Vidyanikethan Engineering College, Tirupati
  • P. Sujatha Jawaharlal Nehru Technological University Anantapur

DOI:

https://doi.org/10.4108/eai.29-6-2021.170251

Keywords:

Multimode control strategy, PI controller, Fuzzy controller, Artificial neural network, Model predictive controller

Abstract

The everyday benefits of environmentally friendly power sources urges to build their use to the bigger degree of whichwind energy is the most accessible asset. This paper presents the plan of multimode hang control methodology based variable speed wind power age framework. The multimode hang control procedure improves the framework to work regarding the network framework and furthermore in the independent method of activity. The multimode control methodology utilizes the DC connect voltage regulator to control the DC interface capacitor voltage for working the framework side converter and current regulator to control current and force of the rotor side converter. The control methodology is investigated with the customary regulator like PI regulator, astute regulators like Fuzzy regulator, fake neural organization (ANN) and model prescient regulator (MPC) which predicts the future factors. A correlation has been performed with the previously mentioned various sorts of regulators based breeze power age framework regarding various boundaries. This paper likewise includes examination of various experiments with the previously mentioned regulators. The examination of various experiments with various regulators has been performed utilizing MATLAB 2013a and every one of the outcomes are checked.

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Published

29-06-2021

How to Cite

1.
Naresh K, Umapathi Reddy P, Sujatha P. Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller. EAI Endorsed Trans Energy Web [Internet]. 2021 Jun. 29 [cited 2024 May 8];9(37):e6. Available from: https://publications.eai.eu/index.php/ew/article/view/64