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Paul, S. and Thomas, J. (2014) Comparison of MPPT Using GA Optimized ANN Employing PI Controller for Solar PV System with MPPT Using Incremental Conductance. 2014 International Conference on IEEE Power Signals Control and Computations (EPSCICON), Thrissur, 6-11 January 2014, 1-5.
http://dx.doi.org/10.1109/epscicon.2014.6887518

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.