Latham, A.M., Pilawa-Podgurski, R., Odame, K.M. and Sullivan, C.R. (2013) Analysis and Optimization of Maximum Power Point Tracking Algorithms in the Presence of Noise. IEEE Transactions on Power Electronics, 28, 3479-3494.

has been cited by the following article:

• TITLE: Solar PV System for Energy Conservation Incorporating an MPPT Based on Computational Intelligent Techniques Supplying Brushless DC Motor Drive

  AUTHORS: R. Anand, Dr. S. Saravanan

  KEYWORDS: Photovoltaic, Genetic Algorithms, Neural Network, Brushless DC Motors and Maximum Power Point Tracking

  JOURNAL NAME: Circuits and Systems, Vol.7 No.8, June 16, 2016

  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.