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Integration Of Solar Pv With Low-voltage Weak Grid System: Using Normalized Laplacian Kernel Adaptive Kalman Filter And Learning Based Inc Algorithm

INTEGRATION OF SOLAR PV WITH LOW-VOLTAGE WEAK GRID SYSTEM: USING NORMALIZED LAPLACIAN KERNEL ADAPTIVE KALMAN FILTER AND LEARNING BASED INC ALGORITHM

  • Project Code :
  • TEREPS19_19
  • .

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INTEGRATION OF SOLAR PV WITH LOW-VOLTAGE WEAK GRID SYSTEM: USING NORMALIZED LAPLACIAN KERNEL ADAPTIVE KALMAN FILTER AND LEARNING BASED INC ALGORITHM

In this project evaluating the performance of a novel Normalized Laplacian Kernel Adaptive Kalman Filter (NLKAKF) based control technique and Learning based Incremental Conductance (LIC) MPPT (Maximum Power Point Tracking) algorithm for low voltage weak grid integrated solar photovoltaic system. Here the system configured as a two-stage topology of three-phase grid integrated solar photovoltaic system is implemented, where loads are connected to the point of common coupling (PCC). In the conventional method evaluated the performance of the system by using the incremental conductance method. In the proposed method by using NLKAKF and LIC algorithm gives better results as power factor correction, harmonics filtering and mitigation of other power quality issues. The proposed algorithm mainly concentrates on reducing steady-state errors and improving the dynamic response. If the generator side produces the power very less the voltage source converter acts as a DSTATCOM device which enhances the utilization factor of the system. the proposed technique and results can be evaluated by using Mat lab/Simulink.

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