Publication Type:

Journal Article

Source:

Circuits and Systems, Volume 7, Issue 8, p.1635-1652 (2016)

URL:

https://www.scirp.org/journal/PaperInformation.aspx?paperID=67424

Keywords:

Brushless DC Motors and Maximum Power Point Tracking, Genetic algorithms, neural network, Photovoltaic

Abstract:

This paper proposes an effective Maximum Power Point Tracking (MPPT) controller being incorporated into a solar Photovoltaic system supplying a Brushless DC (BLDC) motor drive as the load. The MPPT controller makes use of a Genetic Assisted Radial Basis Function Neural Network based technique that includes a high step up Interleaved DC-DC converter. The BLDC motor combines a controller with a Proportional Integral (PI) speed control loop. MATLAB/Simulink has been used to construct the dynamic model and simulate the system. The solar Photovoltaic system uses Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) where the output signal governs the DC-DC boost converters to accomplish the MPPT. This proposed GA-RBF-NN based MPPT controller produces an average power increase of 26.37% and faster response time.

Cite this Research Publication

D. S. Saravanan and Anand Rajendran, “Solar PV System for Energy Conservation Incorporating an MPPT Based on Computational Intelligent Techniques Supplying Brushless DC Motor Drive”, Circuits and Systems, vol. 7, no. 8, pp. 1635-1652, 2016.