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Pnklmf Based Neural Network Control And Learning Based Hc Mppt Technique For Multi-objective Grid Integrated Solar Pv Based Distributed Generating System

PNKLMF BASED NEURAL NETWORK CONTROL AND LEARNING BASED HC MPPT TECHNIQUE FOR MULTI-OBJECTIVE GRID INTEGRATED SOLAR PV BASED DISTRIBUTED GENERATING SYSTEM

  • Project Code :
  • TEMAPS18
  • .
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PNKLMF BASED NEURAL NETWORK CONTROL AND LEARNING BASED HC MPPT TECHNIQUE FOR MULTI-OBJECTIVE GRID INTEGRATED SOLAR PV BASED DISTRIBUTED GENERATING SYSTEM

In this project analyzing the relation between power normalized kernel least mean fourth algorithm-based neural network (NN) control (PNKLMF-NN) technique and learning-based hill-climbing algorithm (L-HC) grid integrated solar PV system. In this project design as the three-phase single-stage topology of grid integrated PV system, for feeding nonlinear/linear load at the point of common coupling (PCC). In the conventional method facing inherent problems of as steady-state oscillations, slow dynamic responses, and fixed step size issues are successfully minimized. PNKLMF-NN controller meets the load when generated powerless than the Power demand. If the solar irradiation value becomes zero, the voltage source converter acts as DSTATCOM, which enhances the utilization factor of the system. the proposed technique results can be evaluated by using Mat lab/Simulink.

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